Overlap Algorithms in Flexible Job-shop Scheduling

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Celia Gutierrez

2014-06-01

Full Text Available The flexible Job-shop Scheduling Problem (fJSP considers the execution of jobs by a set of candidate resources while satisfying time and technological constraints. This work, that follows the hierarchical architecture, is based on an algorithm where each objective (resource allocation, start-time assignment is solved by a genetic algorithm (GA that optimizes a particular fitness function, and enhances the results by the execution of a set of heuristics that evaluate and repair each scheduling constraint on each operation. The aim of this work is to analyze the impact of some algorithmic features of the overlap constraint heuristics, in order to achieve the objectives at a highest degree. To demonstrate the efficiency of this approach, experimentation has been performed and compared with similar cases, tuning the GA parameters correctly.

An extended Intelligent Water Drops algorithm for workflow scheduling in cloud computing environment

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Shaymaa Elsherbiny

2018-03-01

Full Text Available Cloud computing is emerging as a high performance computing environment with a large scale, heterogeneous collection of autonomous systems and flexible computational architecture. Many resource management methods may enhance the efficiency of the whole cloud computing system. The key part of cloud computing resource management is resource scheduling. Optimized scheduling of tasks on the cloud virtual machines is an NP-hard problem and many algorithms have been presented to solve it. The variations among these schedulers are due to the fact that the scheduling strategies of the schedulers are adapted to the changing environment and the types of tasks. The focus of this paper is on workflows scheduling in cloud computing, which is gaining a lot of attention recently because workflows have emerged as a paradigm to represent complex computing problems. We proposed a novel algorithm extending the natural-based Intelligent Water Drops (IWD algorithm that optimizes the scheduling of workflows on the cloud. The proposed algorithm is implemented and embedded within the workflows simulation toolkit and tested in different simulated cloud environments with different cost models. Our algorithm showed noticeable enhancements over the classical workflow scheduling algorithms. We made a comparison between the proposed IWD-based algorithm with other well-known scheduling algorithms, including MIN-MIN, MAX-MIN, Round Robin, FCFS, and MCT, PSO and C-PSO, where the proposed algorithm presented noticeable enhancements in the performance and cost in most situations.

Advance Resource Provisioning in Bulk Data Scheduling

Energy Technology Data Exchange (ETDEWEB)

Balman, Mehmet

2012-10-01

Today?s scientific and business applications generate mas- sive data sets that need to be transferred to remote sites for sharing, processing, and long term storage. Because of increasing data volumes and enhancement in current net- work technology that provide on-demand high-speed data access between collaborating institutions, data handling and scheduling problems have reached a new scale. In this paper, we present a new data scheduling model with ad- vance resource provisioning, in which data movement operations are defined with earliest start and latest comple- tion times. We analyze time-dependent resource assign- ment problem, and propose a new methodology to improve the current systems by allowing researchers and higher-level meta-schedulers to use data-placement as-a-service, so they can plan ahead and submit transfer requests in advance. In general, scheduling with time and resource conflicts is NP-hard. We introduce an efficient algorithm to organize multiple requests on the fly, while satisfying users? time and resource constraints. We successfully tested our algorithm in a simple benchmark simulator that we have developed, and demonstrated its performance with initial test results.

A meta-heuristic method for solving scheduling problem: crow search algorithm

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Adhi, Antono; Santosa, Budi; Siswanto, Nurhadi

2018-04-01

Scheduling is one of the most important processes in an industry both in manufacturingand services. The scheduling process is the process of selecting resources to perform an operation on tasks. Resources can be machines, peoples, tasks, jobs or operations.. The selection of optimum sequence of jobs from a permutation is an essential issue in every research in scheduling problem. Optimum sequence becomes optimum solution to resolve scheduling problem. Scheduling problem becomes NP-hard problem since the number of job in the sequence is more than normal number can be processed by exact algorithm. In order to obtain optimum results, it needs a method with capability to solve complex scheduling problems in an acceptable time. Meta-heuristic is a method usually used to solve scheduling problem. The recently published method called Crow Search Algorithm (CSA) is adopted in this research to solve scheduling problem. CSA is an evolutionary meta-heuristic method which is based on the behavior in flocks of crow. The calculation result of CSA for solving scheduling problem is compared with other algorithms. From the comparison, it is found that CSA has better performance in term of optimum solution and time calculation than other algorithms.

A decentralized scheduling algorithm for time synchronized channel hopping

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Andrew Tinka

2011-09-01

Full Text Available Time Synchronized Channel Hopping (TSCH is an existing Medium Access Control scheme which enables robust communication through channel hopping and high data rates through synchronization. It is based on a time-slotted architecture, and its correct functioning depends on a schedule which is typically computed by a central node. This paper presents, to our knowledge, the first scheduling algorithm for TSCH networks which both is distributed and which copes with mobile nodes. Two variations on scheduling algorithms are presented. Aloha-based scheduling allocates one channel for broadcasting advertisements for new neighbors. Reservation- based scheduling augments Aloha-based scheduling with a dedicated timeslot for targeted advertisements based on gossip information. A mobile ad hoc motorized sensor network with frequent connectivity changes is studied, and the performance of the two proposed algorithms is assessed. This performance analysis uses both simulation results and the results of a field deployment of floating wireless sensors in an estuarial canal environment. Reservation-based scheduling performs significantly better than Aloha-based scheduling, suggesting that the improved network reactivity is worth the increased algorithmic complexity and resource consumption.

Scheduling algorithm for data relay satellite optical communication based on artificial intelligent optimization

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Zhao, Wei-hu; Zhao, Jing; Zhao, Shang-hong; Li, Yong-jun; Wang, Xiang; Dong, Yi; Dong, Chen

2013-08-01

Optical satellite communication with the advantages of broadband, large capacity and low power consuming broke the bottleneck of the traditional microwave satellite communication. The formation of the Space-based Information System with the technology of high performance optical inter-satellite communication and the realization of global seamless coverage and mobile terminal accessing are the necessary trend of the development of optical satellite communication. Considering the resources, missions and restraints of Data Relay Satellite Optical Communication System, a model of optical communication resources scheduling is established and a scheduling algorithm based on artificial intelligent optimization is put forwarded. According to the multi-relay-satellite, multi-user-satellite, multi-optical-antenna and multi-mission with several priority weights, the resources are scheduled reasonable by the operation: "Ascertain Current Mission Scheduling Time" and "Refresh Latter Mission Time-Window". The priority weight is considered as the parameter of the fitness function and the scheduling project is optimized by the Genetic Algorithm. The simulation scenarios including 3 relay satellites with 6 optical antennas, 12 user satellites and 30 missions, the simulation result reveals that the algorithm obtain satisfactory results in both efficiency and performance and resources scheduling model and the optimization algorithm are suitable in multi-relay-satellite, multi-user-satellite, and multi-optical-antenna recourses scheduling problem.

Artificial Immune Algorithm for Subtask Industrial Robot Scheduling in Cloud Manufacturing

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Suma, T.; Murugesan, R.

2018-04-01

The current generation of manufacturing industry requires an intelligent scheduling model to achieve an effective utilization of distributed manufacturing resources, which motivated us to work on an Artificial Immune Algorithm for subtask robot scheduling in cloud manufacturing. This scheduling model enables a collaborative work between the industrial robots in different manufacturing centers. This paper discussed two optimizing objectives which includes minimizing the cost and load balance of industrial robots through scheduling. To solve these scheduling problems, we used the algorithm based on Artificial Immune system. The parameters are simulated with MATLAB and the results compared with the existing algorithms. The result shows better performance than existing.

Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments

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Kairong Duan

2018-05-01

Full Text Available Cloud computing is a new commercial model that enables customers to acquire large amounts of virtual resources on demand. Resources including hardware and software can be delivered as services and measured by specific usage of storage, processing, bandwidth, etc. In Cloud computing, task scheduling is a process of mapping cloud tasks to Virtual Machines (VMs. When binding the tasks to VMs, the scheduling strategy has an important influence on the efficiency of datacenter and related energy consumption. Although many traditional scheduling algorithms have been applied in various platforms, they may not work efficiently due to the large number of user requests, the variety of computation resources and complexity of Cloud environment. In this paper, we tackle the task scheduling problem which aims to minimize makespan by Genetic Algorithm (GA. We propose an incremental GA which has adaptive probabilities of crossover and mutation. The mutation and crossover rates change according to generations and also vary between individuals. Large numbers of tasks are randomly generated to simulate various scales of task scheduling problem in Cloud environment. Based on the instance types of Amazon EC2, we implemented virtual machines with different computing capacity on CloudSim. We compared the performance of the adaptive incremental GA with that of Standard GA, Min-Min, Max-Min , Simulated Annealing and Artificial Bee Colony Algorithm in finding the optimal scheme. Experimental results show that the proposed algorithm can achieve feasible solutions which have acceptable makespan with less computation time.

Multisensors Cooperative Detection Task Scheduling Algorithm Based on Hybrid Task Decomposition and MBPSO

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Changyun Liu

2017-01-01

Full Text Available A multisensor scheduling algorithm based on the hybrid task decomposition and modified binary particle swarm optimization (MBPSO is proposed. Firstly, aiming at the complex relationship between sensor resources and tasks, a hybrid task decomposition method is presented, and the resource scheduling problem is decomposed into subtasks; then the sensor resource scheduling problem is changed into the match problem of sensors and subtasks. Secondly, the resource match optimization model based on the sensor resources and tasks is established, which considers several factors, such as the target priority, detecting benefit, handover times, and resource load. Finally, MBPSO algorithm is proposed to solve the match optimization model effectively, which is based on the improved updating means of particle’s velocity and position through the doubt factor and modified Sigmoid function. The experimental results show that the proposed algorithm is better in terms of convergence velocity, searching capability, solution accuracy, and efficiency.

A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters

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Weiwei Lin

2016-01-01

Full Text Available Cloud computing provides on-demand computing and storage services with high performance and high scalability. However, the rising energy consumption of cloud data centers has become a prominent problem. In this paper, we first introduce an energy-aware framework for task scheduling in virtual clusters. The framework consists of a task resource requirements prediction module, an energy estimate module, and a scheduler with a task buffer. Secondly, based on this framework, we propose a virtual machine power efficiency-aware greedy scheduling algorithm (VPEGS. As a heuristic algorithm, VPEGS estimates task energy by considering factors including task resource demands, VM power efficiency, and server workload before scheduling tasks in a greedy manner. We simulated a heterogeneous VM cluster and conducted experiment to evaluate the effectiveness of VPEGS. Simulation results show that VPEGS effectively reduced total energy consumption by more than 20% without producing large scheduling overheads. With the similar heuristic ideology, it outperformed Min-Min and RASA with respect to energy saving by about 29% and 28%, respectively.

Hybrid and dependent task scheduling algorithm for on-board system software

Institute of Scientific and Technical Information of China (English)

魏振华; 洪炳熔; 乔永强; 蔡则苏; 彭俊杰

2003-01-01

In order to solve the hybrid and dependent task scheduling and critical source allocation problems, atask scheduling algorithm has been developed by first presenting the tasks, and then describing the hybrid anddependent scheduling algorithm and deriving the predictable schedulability condition. The performance of thisagorithm was evaluated through simulation, and it is concluded from the evaluation results that the hybrid taskscheduling subalgorithm based on the comparison factor can be used to solve the problem of aperiodic task beingblocked by periodic task in the traditional operating system for a very long time, which results in poor schedu-ling predictability; and the resource allocation subalgorithm based on schedulability analysis can be used tosolve the problems of critical section conflict, ceiling blocking and priority inversion; and the scheduling algo-rithm is nearest optimal when the abortable critical section is 0.6.

Evaluation of a Cross Layer Scheduling Algorithm for LTE Downlink

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A. Popovska Avramova

2013-06-01

Full Text Available The LTE standard is a leading standard in the wireless broadband market. The Radio Resource Management at the base station plays a major role in satisfying users demand for high data rates and quality of service. This paper evaluates a cross layer scheduling algorithm that aims at minimizing the resource utilization. The algorithm makes decisions based on channel conditions, the size of transmission buffers and different quality of service demands. Simulation results show that the new algorithm improves the resource utilization and provides better guarantees for service quality.

A HYBRID HEURISTIC ALGORITHM FOR SOLVING THE RESOURCE CONSTRAINED PROJECT SCHEDULING PROBLEM (RCPSP

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Juan Carlos Rivera

Full Text Available The Resource Constrained Project Scheduling Problem (RCPSP is a problem of great interest for the scientific community because it belongs to the class of NP-Hard problems and no methods are known that can solve it accurately in polynomial processing times. For this reason heuristic methods are used to solve it in an efficient way though there is no guarantee that an optimal solution can be obtained. This research presents a hybrid heuristic search algorithm to solve the RCPSP efficiently, combining elements of the heuristic Greedy Randomized Adaptive Search Procedure (GRASP, Scatter Search and Justification. The efficiency obtained is measured taking into account the presence of the new elements added to the GRASP algorithm taken as base: Justification and Scatter Search. The algorithms are evaluated using three data bases of instances of the problem: 480 instances of 30 activities, 480 of 60, and 600 of 120 activities respectively, taken from the library PSPLIB available online. The solutions obtained by the developed algorithm for the instances of 30, 60 and 120 are compared with results obtained by other researchers at international level, where a prominent place is obtained, according to Chen (2011.

ENHANCED HYBRID PSO – ACO ALGORITHM FOR GRID SCHEDULING

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P. Mathiyalagan

2010-07-01

Full Text Available Grid computing is a high performance computing environment to solve larger scale computational demands. Grid computing contains resource management, task scheduling, security problems, information management and so on. Task scheduling is a fundamental issue in achieving high performance in grid computing systems. A computational GRID is typically heterogeneous in the sense that it combines clusters of varying sizes, and different clusters typically contains processing elements with different level of performance. In this, heuristic approaches based on particle swarm optimization and ant colony optimization algorithms are adopted for solving task scheduling problems in grid environment. Particle Swarm Optimization (PSO is one of the latest evolutionary optimization techniques by nature. It has the better ability of global searching and has been successfully applied to many areas such as, neural network training etc. Due to the linear decreasing of inertia weight in PSO the convergence rate becomes faster, which leads to the minimal makespan time when used for scheduling. To make the convergence rate faster, the PSO algorithm is improved by modifying the inertia parameter, such that it produces better performance and gives an optimized result. The ACO algorithm is improved by modifying the pheromone updating rule. ACO algorithm is hybridized with PSO algorithm for efficient result and better convergence in PSO algorithm.

Geometric Distribution-Based Readers Scheduling Optimization Algorithm Using Artificial Immune System

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Litian Duan

2016-11-01

Full Text Available In the multiple-reader environment (MRE of radio frequency identification (RFID system, multiple readers are often scheduled to interrogate the randomized tags via operating at different time slots or frequency channels to decrease the signal interferences. Based on this, a Geometric Distribution-based Multiple-reader Scheduling Optimization Algorithm using Artificial Immune System (GD-MRSOA-AIS is proposed to fairly and optimally schedule the readers operating from the viewpoint of resource allocations. GD-MRSOA-AIS is composed of two parts, where a geometric distribution function combined with the fairness consideration is first introduced to generate the feasible scheduling schemes for reader operation. After that, artificial immune system (including immune clone, immune mutation and immune suppression quickly optimize these feasible ones as the optimal scheduling scheme to ensure that readers are fairly operating with larger effective interrogation range and lower interferences. Compared with the state-of-the-art algorithm, the simulation results indicate that GD-MRSOA-AIS could efficiently schedules the multiple readers operating with a fairer resource allocation scheme, performing in larger effective interrogation range.

Concurrent processes scheduling with scarce resources in small and medium enterprises

Institute of Scientific and Technical Information of China (English)

马嵩华

2016-01-01

Scarce resources , precedence and non-determined time-lag are three constraints commonly found in small and medium manufacturing enterprises (SMEs), which are deemed to block the ap-plication of workflow management system ( WfMS ) .To tackle this problem , a workflow scheduling approach is proposed based on timing workflow net (TWF-net) and genetic algorithm (GA).The workflow is modelled in a form of TWF-net in favour of process simulation and resource conflict checking .After simplifying and reconstructing the set of workflow instance , the conflict resolution problem is transformed into a resource-constrained project scheduling problem ( RCPSP ) , which could be efficiently solved by a heuristic method , such as GA.Finally, problems of various sizes are utilized to test the performance of the proposed algorithm and to compare it with first-come-first-served ( FCFS) strategy.The evaluation demonstrates that the proposed method is an overwhelming and effective approach for scheduling the concurrent processes with precedence and resource con -straints .

An improved ant colony optimization algorithm with fault tolerance for job scheduling in grid computing systems.

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Hajara Idris

Full Text Available The Grid scheduler, schedules user jobs on the best available resource in terms of resource characteristics by optimizing job execution time. Resource failure in Grid is no longer an exception but a regular occurring event as resources are increasingly being used by the scientific community to solve computationally intensive problems which typically run for days or even months. It is therefore absolutely essential that these long-running applications are able to tolerate failures and avoid re-computations from scratch after resource failure has occurred, to satisfy the user's Quality of Service (QoS requirement. Job Scheduling with Fault Tolerance in Grid Computing using Ant Colony Optimization is proposed to ensure that jobs are executed successfully even when resource failure has occurred. The technique employed in this paper, is the use of resource failure rate, as well as checkpoint-based roll back recovery strategy. Check-pointing aims at reducing the amount of work that is lost upon failure of the system by immediately saving the state of the system. A comparison of the proposed approach with an existing Ant Colony Optimization (ACO algorithm is discussed. The experimental results of the implemented Fault Tolerance scheduling algorithm show that there is an improvement in the user's QoS requirement over the existing ACO algorithm, which has no fault tolerance integrated in it. The performance evaluation of the two algorithms was measured in terms of the three main scheduling performance metrics: makespan, throughput and average turnaround time.

Multiple R&D projects scheduling optimization with improved particle swarm algorithm.

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Liu, Mengqi; Shan, Miyuan; Wu, Juan

2014-01-01

For most enterprises, in order to win the initiative in the fierce competition of market, a key step is to improve their R&D ability to meet the various demands of customers more timely and less costly. This paper discusses the features of multiple R&D environments in large make-to-order enterprises under constrained human resource and budget, and puts forward a multi-project scheduling model during a certain period. Furthermore, we make some improvements to existed particle swarm algorithm and apply the one developed here to the resource-constrained multi-project scheduling model for a simulation experiment. Simultaneously, the feasibility of model and the validity of algorithm are proved in the experiment.

Multiobjective Variable Neighborhood Search algorithm for scheduling independent jobs on computational grid

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S. Selvi

2015-07-01

Full Text Available Grid computing solves high performance and high-throughput computing problems through sharing resources ranging from personal computers to super computers distributed around the world. As the grid environments facilitate distributed computation, the scheduling of grid jobs has become an important issue. In this paper, an investigation on implementing Multiobjective Variable Neighborhood Search (MVNS algorithm for scheduling independent jobs on computational grid is carried out. The performance of the proposed algorithm has been evaluated with Min–Min algorithm, Simulated Annealing (SA and Greedy Randomized Adaptive Search Procedure (GRASP algorithm. Simulation results show that MVNS algorithm generally performs better than other metaheuristics methods.

A note on resource allocation scheduling with group technology and learning effects on a single machine

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Lu, Yuan-Yuan; Wang, Ji-Bo; Ji, Ping; He, Hongyu

2017-09-01

In this article, single-machine group scheduling with learning effects and convex resource allocation is studied. The goal is to find the optimal job schedule, the optimal group schedule, and resource allocations of jobs and groups. For the problem of minimizing the makespan subject to limited resource availability, it is proved that the problem can be solved in polynomial time under the condition that the setup times of groups are independent. For the general setup times of groups, a heuristic algorithm and a branch-and-bound algorithm are proposed, respectively. Computational experiments show that the performance of the heuristic algorithm is fairly accurate in obtaining near-optimal solutions.

ComprehensiveBench: a Benchmark for the Extensive Evaluation of Global Scheduling Algorithms

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Pilla, Laércio L.; Bozzetti, Tiago C.; Castro, Márcio; Navaux, Philippe O. A.; Méhaut, Jean-François

2015-10-01

Parallel applications that present tasks with imbalanced loads or complex communication behavior usually do not exploit the underlying resources of parallel platforms to their full potential. In order to mitigate this issue, global scheduling algorithms are employed. As finding the optimal task distribution is an NP-Hard problem, identifying the most suitable algorithm for a specific scenario and comparing algorithms are not trivial tasks. In this context, this paper presents ComprehensiveBench, a benchmark for global scheduling algorithms that enables the variation of a vast range of parameters that affect performance. ComprehensiveBench can be used to assist in the development and evaluation of new scheduling algorithms, to help choose a specific algorithm for an arbitrary application, to emulate other applications, and to enable statistical tests. We illustrate its use in this paper with an evaluation of Charm++ periodic load balancers that stresses their characteristics.

Ant colony optimisation for scheduling of flexible job shop with multi-resources requirements

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Kalinowski Krzysztof

2017-01-01

Full Text Available The paper presents application of ant colony optimisation algorithm for scheduling multi-resources operations in flexible job shop type of production systems. Operations that require the participation of two or more resources are common in industrial practice, when planning are subject not only machines, but also other additional resources (personnel, tools, etc.. Resource requirements of operation are indicated indirectly by resource groups. The most important parameters of the resource model and resource groups are also described. A basic assumptions for ant colony algorithm used for scheduling in the considered model with multiresources requirements of operations is discussed. The main result of the research is the schema of metaheuristic that enables searching best-score solutions in manufacturing systems satisfying presented constraints.

A Novel LTE Scheduling Algorithm for Green Technology in Smart Grid

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Hindia, Mohammad Nour; Reza, Ahmed Wasif; Noordin, Kamarul Ariffin; Chayon, Muhammad Hasibur Rashid

2015-01-01

Smart grid (SG) application is being used nowadays to meet the demand of increasing power consumption. SG application is considered as a perfect solution for combining renewable energy resources and electrical grid by means of creating a bidirectional communication channel between the two systems. In this paper, three SG applications applicable to renewable energy system, namely, distribution automation (DA), distributed energy system-storage (DER) and electrical vehicle (EV), are investigated in order to study their suitability in Long Term Evolution (LTE) network. To compensate the weakness in the existing scheduling algorithms, a novel bandwidth estimation and allocation technique and a new scheduling algorithm are proposed. The technique allocates available network resources based on application’s priority, whereas the algorithm makes scheduling decision based on dynamic weighting factors of multi-criteria to satisfy the demands (delay, past average throughput and instantaneous transmission rate) of quality of service. Finally, the simulation results demonstrate that the proposed mechanism achieves higher throughput, lower delay and lower packet loss rate for DA and DER as well as provide a degree of service for EV. In terms of fairness, the proposed algorithm shows 3%, 7 % and 9% better performance compared to exponential rule (EXP-Rule), modified-largest weighted delay first (M-LWDF) and exponential/PF (EXP/PF), respectively. PMID:25830703

A novel LTE scheduling algorithm for green technology in smart grid.

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Mohammad Nour Hindia

Full Text Available Smart grid (SG application is being used nowadays to meet the demand of increasing power consumption. SG application is considered as a perfect solution for combining renewable energy resources and electrical grid by means of creating a bidirectional communication channel between the two systems. In this paper, three SG applications applicable to renewable energy system, namely, distribution automation (DA, distributed energy system-storage (DER and electrical vehicle (EV, are investigated in order to study their suitability in Long Term Evolution (LTE network. To compensate the weakness in the existing scheduling algorithms, a novel bandwidth estimation and allocation technique and a new scheduling algorithm are proposed. The technique allocates available network resources based on application's priority, whereas the algorithm makes scheduling decision based on dynamic weighting factors of multi-criteria to satisfy the demands (delay, past average throughput and instantaneous transmission rate of quality of service. Finally, the simulation results demonstrate that the proposed mechanism achieves higher throughput, lower delay and lower packet loss rate for DA and DER as well as provide a degree of service for EV. In terms of fairness, the proposed algorithm shows 3%, 7 % and 9% better performance compared to exponential rule (EXP-Rule, modified-largest weighted delay first (M-LWDF and exponential/PF (EXP/PF, respectively.

A novel LTE scheduling algorithm for green technology in smart grid.

Science.gov (United States)

Hindia, Mohammad Nour; Reza, Ahmed Wasif; Noordin, Kamarul Ariffin; Chayon, Muhammad Hasibur Rashid

2015-01-01

Smart grid (SG) application is being used nowadays to meet the demand of increasing power consumption. SG application is considered as a perfect solution for combining renewable energy resources and electrical grid by means of creating a bidirectional communication channel between the two systems. In this paper, three SG applications applicable to renewable energy system, namely, distribution automation (DA), distributed energy system-storage (DER) and electrical vehicle (EV), are investigated in order to study their suitability in Long Term Evolution (LTE) network. To compensate the weakness in the existing scheduling algorithms, a novel bandwidth estimation and allocation technique and a new scheduling algorithm are proposed. The technique allocates available network resources based on application's priority, whereas the algorithm makes scheduling decision based on dynamic weighting factors of multi-criteria to satisfy the demands (delay, past average throughput and instantaneous transmission rate) of quality of service. Finally, the simulation results demonstrate that the proposed mechanism achieves higher throughput, lower delay and lower packet loss rate for DA and DER as well as provide a degree of service for EV. In terms of fairness, the proposed algorithm shows 3%, 7 % and 9% better performance compared to exponential rule (EXP-Rule), modified-largest weighted delay first (M-LWDF) and exponential/PF (EXP/PF), respectively.

Resource Constrained Project Scheduling Subject to Due Dates: Preemption Permitted with Penalty

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Behrouz Afshar-Nadjafi

2014-01-01

Full Text Available Extensive research works have been carried out in resource constrained project scheduling problem. However, scarce researches have studied the problems in which a setup cost must be incurred if activities are preempted. In this research, we investigate the resource constrained project scheduling problem to minimize the total project cost, considering earliness-tardiness and preemption penalties. A mixed integer programming formulation is proposed for the problem. The resulting problem is NP-hard. So, we try to obtain a satisfying solution using simulated annealing (SA algorithm. The efficiency of the proposed algorithm is tested based on 150 randomly produced examples. Statistical comparison in terms of the computational times and objective function indicates that the proposed algorithm is efficient and effective.

On utilization bounds for a periodic resource under rate monotonic scheduling

NARCIS (Netherlands)

Renssen, van A.M.; Geuns, S.J.; Hausmans, J.P.H.M.; Poncin, W.; Bril, R.J.

2009-01-01

This paper revisits utilization bounds for a periodic resource under the rate monotonic (RM) scheduling algorithm. We show that the existing utilization bound, as presented in [8, 9], is optimistic. We subsequently show that by viewing the unavailability of the periodic resource as a deferrable

Developing a new method for modifying over-allocated multi-mode resource constraint schedules in the presence of preemptive resources

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Aidin Delgoshaei

2016-09-01

Full Text Available The issue of resource over-allocating is a big concern for project engineers in the process of scheduling project activities. Resource over-allocating is frequently seen after initial scheduling of a project in practice and causes significant amount of efforts to modify the initial schedules. In this research, a new method is developed for modifying over-allocated schedules in a multi-mode resource constrained project scheduling problems (MRCPSPs with positive cash flows (MRCPSP-PCF. The aim is to maximize profit of the MRCPSPs or logically minimizing costs. The proposed method can be used as a macro in Microsoft Office Project® Software to modify resource over-allocated days after scheduling a project. This research considers progress payment method and preemptive resources. The proposed approach maximizes profit by scheduling activities through the resource calendar respecting to the available level of preemptive resources and activity numbers. To examine the performance of the proposed method a number of experiments derived from the literature are solved. The results are then compared with the circumstances where resource constraints are relaxed. The outcomes show that in all studied cases, the proposed algorithm can provide modified schedules with no over-allocated days. Afterward the method is applied to modify a manufacturing project in practice.

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

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Amjad Mahmood

2017-04-01

Full Text Available In the Infrastructure-as-a-Service cloud computing model, virtualized computing resources in the form of virtual machines are provided over the Internet. A user can rent an arbitrary number of computing resources to meet their requirements, making cloud computing an attractive choice for executing real-time tasks. Economical task allocation and scheduling on a set of leased virtual machines is an important problem in the cloud computing environment. This paper proposes a greedy and a genetic algorithm with an adaptive selection of suitable crossover and mutation operations (named as AGA to allocate and schedule real-time tasks with precedence constraint on heterogamous virtual machines. A comprehensive simulation study has been done to evaluate the performance of the proposed algorithms in terms of their solution quality and efficiency. The simulation results show that AGA outperforms the greedy algorithm and non-adaptive genetic algorithm in terms of solution quality.

Resource Allocation in a Repetitive Project Scheduling Using Genetic Algorithm

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Samuel, Biju; Mathew, Jeeno

2018-03-01

Resource Allocation is procedure of doling out or allocating the accessible assets in a monetary way and productive way. Resource allocation is the scheduling of the accessible assets and accessible exercises or activities required while thinking about both the asset accessibility and the total project completion time. Asset provisioning and allocation takes care of that issue by permitting the specialist co-ops to deal with the assets for every individual demand of asset. A probabilistic selection procedure has been developed in order to ensure various selections of chromosomes

A FAST AND ELITIST BI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR SCHEDULING INDEPENDENT TASKS ON HETEROGENEOUS SYSTEMS

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G.Subashini

2010-07-01

Full Text Available To meet the increasing computational demands, geographically distributed resources need to be logically coupled to make them work as a unified resource. In analyzing the performance of such distributed heterogeneous computing systems scheduling a set of tasks to the available set of resources for execution is highly important. Task scheduling being an NP-complete problem, use of metaheuristics is more appropriate in obtaining optimal solutions. Schedules thus obtained can be evaluated using several criteria that may conflict with one another which require multi objective problem formulation. This paper investigates the application of an elitist Nondominated Sorting Genetic Algorithm (NSGA-II, to efficiently schedule a set of independent tasks in a heterogeneous distributed computing system. The objectives considered in this paper include minimizing makespan and average flowtime simultaneously. The implementation of NSGA-II algorithm and Weighted-Sum Genetic Algorithm (WSGA has been tested on benchmark instances for distributed heterogeneous systems. As NSGA-II generates a set of Pareto optimal solutions, to verify the effectiveness of NSGA-II over WSGA a fuzzy based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set.

Resource-constrained project scheduling: computing lower bounds by solving minimum cut problems

NARCIS (Netherlands)

Möhring, R.H.; Nesetril, J.; Schulz, A.S.; Stork, F.; Uetz, Marc Jochen

1999-01-01

We present a novel approach to compute Lagrangian lower bounds on the objective function value of a wide class of resource-constrained project scheduling problems. The basis is a polynomial-time algorithm to solve the following scheduling problem: Given a set of activities with start-time dependent

A Study on the Enhanced Best Performance Algorithm for the Just-in-Time Scheduling Problem

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Sivashan Chetty

2015-01-01

Full Text Available The Just-In-Time (JIT scheduling problem is an important subject of study. It essentially constitutes the problem of scheduling critical business resources in an attempt to optimize given business objectives. This problem is NP-Hard in nature, hence requiring efficient solution techniques. To solve the JIT scheduling problem presented in this study, a new local search metaheuristic algorithm, namely, the enhanced Best Performance Algorithm (eBPA, is introduced. This is part of the initial study of the algorithm for scheduling problems. The current problem setting is the allocation of a large number of jobs required to be scheduled on multiple and identical machines which run in parallel. The due date of a job is characterized by a window frame of time, rather than a specific point in time. The performance of the eBPA is compared against Tabu Search (TS and Simulated Annealing (SA. SA and TS are well-known local search metaheuristic algorithms. The results show the potential of the eBPA as a metaheuristic algorithm.

Aeon: Synthesizing Scheduling Algorithms from High-Level Models

Science.gov (United States)

Monette, Jean-Noël; Deville, Yves; van Hentenryck, Pascal

This paper describes the aeon system whose aim is to synthesize scheduling algorithms from high-level models. A eon, which is entirely written in comet, receives as input a high-level model for a scheduling application which is then analyzed to generate a dedicated scheduling algorithm exploiting the structure of the model. A eon provides a variety of synthesizers for generating complete or heuristic algorithms. Moreover, synthesizers are compositional, making it possible to generate complex hybrid algorithms naturally. Preliminary experimental results indicate that this approach may be competitive with state-of-the-art search algorithms.

Efficient scheduling request algorithm for opportunistic wireless access

KAUST Repository

Nam, Haewoon

2011-08-01

An efficient scheduling request algorithm for opportunistic wireless access based on user grouping is proposed in this paper. Similar to the well-known opportunistic splitting algorithm, the proposed algorithm initially adjusts (or lowers) the threshold during a guard period if no user sends a scheduling request. However, if multiple users make requests simultaneously and therefore a collision occurs, the proposed algorithm no longer updates the threshold but narrows down the user search space by splitting the users into multiple groups iteratively, whereas the opportunistic splitting algorithm keeps adjusting the threshold until a single user is found. Since the threshold is only updated when no user sends a request, it is shown that the proposed algorithm significantly alleviates the burden of the signaling for the threshold distribution to the users by the scheduler. More importantly, the proposed algorithm requires a less number of mini-slots to make a user selection given a certain scheduling outage probability. © 2011 IEEE.

Joint User Scheduling and MU-MIMO Hybrid Beamforming Algorithm for mmWave FDMA Massive MIMO System

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Jing Jiang

2016-01-01

Full Text Available The large bandwidth and multipath in millimeter wave (mmWave cellular system assure the existence of frequency selective channels; it is necessary that mmWave system remains with frequency division multiple access (FDMA and user scheduling. But for the hybrid beamforming system, the analog beamforming is implemented by the same phase shifts in the entire frequency band, and the wideband phase shifts may not be harmonious with all users scheduled in frequency resources. This paper proposes a joint user scheduling and multiuser hybrid beamforming algorithm for downlink massive multiple input multiple output (MIMO orthogonal frequency division multiple access (OFDMA systems. In the first step of user scheduling, the users with identical optimal beams form an OFDMA user group and multiplex the entire frequency resource. Then base station (BS allocates the frequency resources for each member of OFDMA user group. An OFDMA user group can be regarded as a virtual user; thus it can support arbitrary MU-MIMO user selection and beamforming algorithms. Further, the analog beamforming vectors employ the best beam of each selected MU-MIMO user and the digital beamforming algorithm is solved by weight MMSE to acquire the best performance gain and mitigate the interuser inference. Simulation results show that hybrid beamforming together with user scheduling can greatly improve the performance of mmWave OFDMA massive MU-MIMO system.

Resource planning and scheduling of payload for satellite with particle swarm optimization

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Li, Jian; Wang, Cheng

2007-11-01

The resource planning and scheduling technology of payload is a key technology to realize an automated control for earth observing satellite with limited resources on satellite, which is implemented to arrange the works states of various payloads to carry out missions by optimizing the scheme of the resources. The scheduling task is a difficult constraint optimization problem with various and mutative requests and constraints. Based on the analysis of the satellite's functions and the payload's resource constraints, a proactive planning and scheduling strategy based on the availability of consumable and replenishable resources in time-order is introduced along with dividing the planning and scheduling period to several pieces. A particle swarm optimization algorithm is proposed to address the problem with an adaptive mutation operator selection, where the swarm is divided into groups with different probabilities to employ various mutation operators viz., differential evolution, Gaussian and random mutation operators. The probabilities are adjusted adaptively by comparing the effectiveness of the groups to select a proper operator. The simulation results have shown the feasibility and effectiveness of the method.

A hybrid algorithm for flexible job-shop scheduling problem with setup times

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Ameni Azzouz

2017-01-01

Full Text Available Job-shop scheduling problem is one of the most important fields in manufacturing optimization where a set of n jobs must be processed on a set of m specified machines. Each job consists of a specific set of operations, which have to be processed according to a given order. The Flexible Job Shop problem (FJSP is a generalization of the above-mentioned problem, where each operation can be processed by a set of resources and has a processing time depending on the resource used. The FJSP problems cover two difficulties, namely, machine assignment problem and operation sequencing problem. This paper addresses the flexible job-shop scheduling problem with sequence-dependent setup times to minimize two kinds of objectives function: makespan and bi-criteria objective function. For that, we propose a hybrid algorithm based on genetic algorithm (GA and variable neighbourhood search (VNS to solve this problem. To evaluate the performance of our algorithm, we compare our results with other methods existing in literature. All the results show the superiority of our algorithm against the available ones in terms of solution quality.

Packet-Scheduling Algorithm by the Ratio of Transmit Power to the Transmission Bits in 3GPP LTE Downlink

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Gil Gye-Tae

2010-01-01

Full Text Available Packet scheduler plays the central role in determining the overall performance of the 3GPP long-term evolution (LTE based on packet-switching operation. In this paper, a novel minimum transmit power-based (MP packet-scheduling algorithm is proposed that can achieve power-efficient transmission to the UEs while providing both system throughput gain and fairness improvement. The proposed algorithm is based on a new scheduling metric focusing on the ratio of the transmit power per bit and allocates the physical resource block (PRB to the UE that requires the least ratio of the transmit power per bit. Through computer simulation, the performance of the proposed MP packet-scheduling algorithm is compared with the conventional packet-scheduling algorithms by two primary criteria: fairness and throughput. The simulation results show that the proposed algorithm outperforms the conventional algorithms in terms of the fairness and throughput.

The serial message-passing schedule for LDPC decoding algorithms

Science.gov (United States)

Liu, Mingshan; Liu, Shanshan; Zhou, Yuan; Jiang, Xue

2015-12-01

The conventional message-passing schedule for LDPC decoding algorithms is the so-called flooding schedule. It has the disadvantage that the updated messages cannot be used until next iteration, thus reducing the convergence speed . In this case, the Layered Decoding algorithm (LBP) based on serial message-passing schedule is proposed. In this paper the decoding principle of LBP algorithm is briefly introduced, and then proposed its two improved algorithms, the grouped serial decoding algorithm (Grouped LBP) and the semi-serial decoding algorithm .They can improve LBP algorithm's decoding speed while maintaining a good decoding performance.

A priority-based heuristic algorithm (PBHA for optimizing integrated process planning and scheduling problem

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Muhammad Farhan Ausaf

2015-12-01

Full Text Available Process planning and scheduling are two important components of a manufacturing setup. It is important to integrate them to achieve better global optimality and improved system performance. To find optimal solutions for integrated process planning and scheduling (IPPS problem, numerous algorithm-based approaches exist. Most of these approaches try to use existing meta-heuristic algorithms for solving the IPPS problem. Although these approaches have been shown to be effective in optimizing the IPPS problem, there is still room for improvement in terms of quality of solution and algorithm efficiency, especially for more complicated problems. Dispatching rules have been successfully utilized for solving complicated scheduling problems, but haven’t been considered extensively for the IPPS problem. This approach incorporates dispatching rules with the concept of prioritizing jobs, in an algorithm called priority-based heuristic algorithm (PBHA. PBHA tries to establish job and machine priority for selecting operations. Priority assignment and a set of dispatching rules are simultaneously used to generate both the process plans and schedules for all jobs and machines. The algorithm was tested for a series of benchmark problems. The proposed algorithm was able to achieve superior results for most complex problems presented in recent literature while utilizing lesser computational resources.

Cloud Service Scheduling Algorithm Research and Optimization

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Hongyan Cui

2017-01-01

Full Text Available We propose a cloud service scheduling model that is referred to as the Task Scheduling System (TSS. In the user module, the process time of each task is in accordance with a general distribution. In the task scheduling module, we take a weighted sum of makespan and flowtime as the objective function and use an Ant Colony Optimization (ACO and a Genetic Algorithm (GA to solve the problem of cloud task scheduling. Simulation results show that the convergence speed and output performance of our Genetic Algorithm-Chaos Ant Colony Optimization (GA-CACO are optimal.

Efficient Resource Scheduling by Exploiting Relay Cache for Cellular Networks

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Chun He

2015-01-01

Full Text Available In relay-enhanced cellular systems, throughput of User Equipment (UE is constrained by the bottleneck of the two-hop link, backhaul link (or the first hop link, and access link (the second hop link. To maximize the throughput, resource allocation should be coordinated between these two hops. A common resource scheduling algorithm, Adaptive Distributed Proportional Fair, only ensures that the throughput of the first hop is greater than or equal to that of the second hop. But it cannot guarantee a good balance of the throughput and fairness between the two hops. In this paper, we propose a Two-Hop Balanced Distributed Scheduling (TBS algorithm by exploiting relay cache for non-real-time data traffic. The evolved Node Basestation (eNB adaptively adjusts the number of Resource Blocks (RBs allocated to the backhaul link and direct links based on the cache information of relays. Each relay allocates RBs for relay UEs based on the size of the relay UE’s Transport Block. We also design a relay UE’s ACK feedback mechanism to update the data at relay cache. Simulation results show that the proposed TBS can effectively improve resource utilization and achieve a good trade-off between system throughput and fairness by balancing the throughput of backhaul and access link.

Cloud Computing Task Scheduling Based on Cultural Genetic Algorithm

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Li Jian-Wen

2016-01-01

Full Text Available The task scheduling strategy based on cultural genetic algorithm(CGA is proposed in order to improve the efficiency of task scheduling in the cloud computing platform, which targets at minimizing the total time and cost of task scheduling. The improved genetic algorithm is used to construct the main population space and knowledge space under cultural framework which get independent parallel evolution, forming a mechanism of mutual promotion to dispatch the cloud task. Simultaneously, in order to prevent the defects of the genetic algorithm which is easy to fall into local optimum, the non-uniform mutation operator is introduced to improve the search performance of the algorithm. The experimental results show that CGA reduces the total time and lowers the cost of the scheduling, which is an effective algorithm for the cloud task scheduling.

Iterative group splitting algorithm for opportunistic scheduling systems

KAUST Repository

Nam, Haewoon

2014-05-01

An efficient feedback algorithm for opportunistic scheduling systems based on iterative group splitting is proposed in this paper. Similar to the opportunistic splitting algorithm, the proposed algorithm adjusts (or lowers) the feedback threshold during a guard period if no user sends a feedback. However, when a feedback collision occurs at any point of time, the proposed algorithm no longer updates the threshold but narrows down the user search space by dividing the users into multiple groups iteratively, whereas the opportunistic splitting algorithm keeps adjusting the threshold until a single user is found. Since the threshold is only updated when no user sends a feedback, it is shown that the proposed algorithm significantly alleviates the signaling overhead for the threshold distribution to the users by the scheduler. More importantly, the proposed algorithm requires a less number of mini-slots than the opportunistic splitting algorithm to make a user selection with a given level of scheduling outage probability or provides a higher ergodic capacity given a certain number of mini-slots. © 2013 IEEE.

Scheduling theory, algorithms, and systems

CERN Document Server

Pinedo, Michael L

2016-01-01

This new edition of the well-established text Scheduling: Theory, Algorithms, and Systems provides an up-to-date coverage of important theoretical models in the scheduling literature as well as important scheduling problems that appear in the real world. The accompanying website includes supplementary material in the form of slide-shows from industry as well as movies that show actual implementations of scheduling systems. The main structure of the book, as per previous editions, consists of three parts. The first part focuses on deterministic scheduling and the related combinatorial problems. The second part covers probabilistic scheduling models; in this part it is assumed that processing times and other problem data are random and not known in advance. The third part deals with scheduling in practice; it covers heuristics that are popular with practitioners and discusses system design and implementation issues. All three parts of this new edition have been revamped, streamlined, and extended. The reference...

Car painting process scheduling with harmony search algorithm

Science.gov (United States)

Syahputra, M. F.; Maiyasya, A.; Purnamawati, S.; Abdullah, D.; Albra, W.; Heikal, M.; Abdurrahman, A.; Khaddafi, M.

2018-02-01

Automotive painting program in the process of painting the car body by using robot power, making efficiency in the production system. Production system will be more efficient if pay attention to scheduling of car order which will be done by considering painting body shape of car. Flow shop scheduling is a scheduling model in which the job-job to be processed entirely flows in the same product direction / path. Scheduling problems often arise if there are n jobs to be processed on the machine, which must be specified which must be done first and how to allocate jobs on the machine to obtain a scheduled production process. Harmony Search Algorithm is a metaheuristic optimization algorithm based on music. The algorithm is inspired by observations that lead to music in search of perfect harmony. This musical harmony is in line to find optimal in the optimization process. Based on the tests that have been done, obtained the optimal car sequence with minimum makespan value.

Algorithm comparison for schedule optimization in MR fingerprinting.

Science.gov (United States)

Cohen, Ouri; Rosen, Matthew S

2017-09-01

In MR Fingerprinting, the flip angles and repetition times are chosen according to a pseudorandom schedule. In previous work, we have shown that maximizing the discrimination between different tissue types by optimizing the acquisition schedule allows reductions in the number of measurements required. The ideal optimization algorithm for this application remains unknown, however. In this work we examine several different optimization algorithms to determine the one best suited for optimizing MR Fingerprinting acquisition schedules. Copyright © 2017 Elsevier Inc. All rights reserved.

Genetic algorithm to solve the problems of lectures and practicums scheduling

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Syahputra, M. F.; Apriani, R.; Sawaluddin; Abdullah, D.; Albra, W.; Heikal, M.; Abdurrahman, A.; Khaddafi, M.

2018-02-01

Generally, the scheduling process is done manually. However, this method has a low accuracy level, along with possibilities that a scheduled process collides with another scheduled process. When doing theory class and practicum timetable scheduling process, there are numerous problems, such as lecturer teaching schedule collision, schedule collision with another schedule, practicum lesson schedules that collides with theory class, and the number of classrooms available. In this research, genetic algorithm is implemented to perform theory class and practicum timetable scheduling process. The algorithm will be used to process the data containing lists of lecturers, courses, and class rooms, obtained from information technology department at University of Sumatera Utara. The result of scheduling process using genetic algorithm is the most optimal timetable that conforms to available time slots, class rooms, courses, and lecturer schedules.

Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm.

Science.gov (United States)

Abdulhamid, Shafi'i Muhammad; Abd Latiff, Muhammad Shafie; Abdul-Salaam, Gaddafi; Hussain Madni, Syed Hamid

2016-01-01

Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques.

Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm

Science.gov (United States)

Abdulhamid, Shafi’i Muhammad; Abd Latiff, Muhammad Shafie; Abdul-Salaam, Gaddafi; Hussain Madni, Syed Hamid

2016-01-01

Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques. PMID:27384239

Using Optimization Models for Scheduling in Enterprise Resource Planning Systems

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Frank Herrmann

2016-03-01

Full Text Available Companies often use specially-designed production systems and change them from time to time. They produce small batches in order to satisfy specific demands with the least tardiness. This imposes high demands on high-performance scheduling algorithms which can be rapidly adapted to changes in the production system. As a solution, this paper proposes a generic approach: solutions were obtained using a widely-used commercially-available tool for solving linear optimization models, which is available in an Enterprise Resource Planning System (in the SAP system for example or can be connected to it. In a real-world application of a flow shop with special restrictions this approach is successfully used on a standard personal computer. Thus, the main implication is that optimal scheduling with a commercially-available tool, incorporated in an Enterprise Resource Planning System, may be the best approach.

A Bayesian matching pursuit based scheduling algorithm for feedback reduction in MIMO broadcast channels

KAUST Repository

Shibli, Hussain J.

2013-06-01

Opportunistic schedulers rely on the feedback of all users in order to schedule a set of users with favorable channel conditions. While the downlink channels can be easily estimated at all user terminals via a single broadcast, several key challenges are faced during uplink transmission. First of all, the statistics of the noisy and fading feedback channels are unknown at the base station (BS) and channel training is usually required from all users. Secondly, the amount of network resources (air-time) required for feedback transmission grows linearly with the number of users. In this paper, we tackle the above challenges and propose a Bayesian based scheduling algorithm that 1) reduces the air-time required to identify the strong users, and 2) is agnostic to the statistics of the feedback channels and utilizes the a priori statistics of the additive noise to identify the strong users. Numerical results show that the proposed algorithm reduces the feedback air-time while improving detection in the presence of fading and noisy channels when compared to recent compressed sensing based algorithms. Furthermore, the proposed algorithm achieves a sum-rate throughput close to that obtained by noiseless dedicated feedback systems. © 2013 IEEE.

An improved sheep flock heredity algorithm for job shop scheduling and flow shop scheduling problems

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Chandramouli Anandaraman

2011-10-01

Full Text Available Job Shop Scheduling Problem (JSSP and Flow Shop Scheduling Problem (FSSP are strong NP-complete combinatorial optimization problems among class of typical production scheduling problems. An improved Sheep Flock Heredity Algorithm (ISFHA is proposed in this paper to find a schedule of operations that can minimize makespan. In ISFHA, the pairwise mutation operation is replaced by a single point mutation process with a probabilistic property which guarantees the feasibility of the solutions in the local search domain. A Robust-Replace (R-R heuristic is introduced in place of chromosomal crossover to enhance the global search and to improve the convergence. The R-R heuristic is found to enhance the exploring potential of the algorithm and enrich the diversity of neighborhoods. Experimental results reveal the effectiveness of the proposed algorithm, whose optimization performance is markedly superior to that of genetic algorithms and is comparable to the best results reported in the literature.

Iterative group splitting algorithm for opportunistic scheduling systems

KAUST Repository

Nam, Haewoon; Alouini, Mohamed-Slim

2014-01-01

An efficient feedback algorithm for opportunistic scheduling systems based on iterative group splitting is proposed in this paper. Similar to the opportunistic splitting algorithm, the proposed algorithm adjusts (or lowers) the feedback threshold

Efficient scheduling request algorithm for opportunistic wireless access

KAUST Repository

Nam, Haewoon; Alouini, Mohamed-Slim

2011-01-01

An efficient scheduling request algorithm for opportunistic wireless access based on user grouping is proposed in this paper. Similar to the well-known opportunistic splitting algorithm, the proposed algorithm initially adjusts (or lowers

An Improved Recovery Algorithm for Decayed AES Key Schedule Images

Science.gov (United States)

Tsow, Alex

A practical algorithm that recovers AES key schedules from decayed memory images is presented. Halderman et al. [1] established this recovery capability, dubbed the cold-boot attack, as a serious vulnerability for several widespread software-based encryption packages. Our algorithm recovers AES-128 key schedules tens of millions of times faster than the original proof-of-concept release. In practice, it enables reliable recovery of key schedules at 70% decay, well over twice the decay capacity of previous methods. The algorithm is generalized to AES-256 and is empirically shown to recover 256-bit key schedules that have suffered 65% decay. When solutions are unique, the algorithm efficiently validates this property and outputs the solution for memory images decayed up to 60%.

Using a vision cognitive algorithm to schedule virtual machines

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Zhao Jiaqi

2014-09-01

Full Text Available Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption

Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

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Imam Ahmad Ashari

2016-11-01

Full Text Available Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization  algorithm in solving the case of course scheduling.

A distributed scheduling algorithm for heterogeneous real-time systems

Science.gov (United States)

Zeineldine, Osman; El-Toweissy, Mohamed; Mukkamala, Ravi

1991-01-01

Much of the previous work on load balancing and scheduling in distributed environments was concerned with homogeneous systems and homogeneous loads. Several of the results indicated that random policies are as effective as other more complex load allocation policies. The effects of heterogeneity on scheduling algorithms for hard real time systems is examined. A distributed scheduler specifically to handle heterogeneities in both nodes and node traffic is proposed. The performance of the algorithm is measured in terms of the percentage of jobs discarded. While a random task allocation is very sensitive to heterogeneities, the algorithm is shown to be robust to such non-uniformities in system components and load.

Scheduling preemptable jobs on identical processors under varying availability of an additional continuous resource

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Różycki Rafał

2016-09-01

Full Text Available In this work we consider a problem of scheduling preemptable, independent jobs, characterized by the fact that their processing speeds depend on the amounts of a continuous, renewable resource allocated to jobs at a time. Jobs are scheduled on parallel, identical machines, with the criterion of minimization of the schedule length. Since two categories of resources occur in the problem: discrete (set of machines and continuous, it is generally called a discrete-continuous scheduling problem. The model studied in this paper allows the total available amount of the continuous resource to vary over time, which is a practically important generalization that has not been considered yet for discrete-continuous scheduling problems. For this model we give some properties of optimal schedules on a basis of which we propose a general methodology for solving the considered class of problems. The methodology uses a two-phase approach in which, firstly, an assignment of machines to jobs is defined and, secondly, for this assignment an optimal continuous resource allocation is found by solving an appropriate mathematical programming problem. In the approach various cases are considered, following from assumptions made on the form of the processing speed functions of jobs. For each case an iterative algorithm is designed, leading to an optimal solution in a finite number of steps.

Research and Applications of Shop Scheduling Based on Genetic Algorithms

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Hang ZHAO

Full Text Available ABSTRACT Shop Scheduling is an important factor affecting the efficiency of production, efficient scheduling method and a research and application for optimization technology play an important role for manufacturing enterprises to improve production efficiency, reduce production costs and many other aspects. Existing studies have shown that improved genetic algorithm has solved the limitations that existed in the genetic algorithm, the objective function is able to meet customers' needs for shop scheduling, and the future research should focus on the combination of genetic algorithm with other optimized algorithms. In this paper, in order to overcome the shortcomings of early convergence of genetic algorithm and resolve local minimization problem in search process,aiming at mixed flow shop scheduling problem, an improved cyclic search genetic algorithm is put forward, and chromosome coding method and corresponding operation are given.The operation has the nature of inheriting the optimal individual ofthe previous generation and is able to avoid the emergence of local minimum, and cyclic and crossover operation and mutation operation can enhance the diversity of the population and then quickly get the optimal individual, and the effectiveness of the algorithm is validated. Experimental results show that the improved algorithm can well avoid the emergency of local minimum and is rapid in convergence.

A Novel Algorithm for Efficient Downlink Packet Scheduling for Multiple-Component-Carrier Cellular Systems

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Yao-Liang Chung

2016-11-01

Full Text Available The simultaneous aggregation of multiple component carriers (CCs for use by a base station constitutes one of the more promising strategies for providing substantially enhanced bandwidths for packet transmissions in 4th and 5th generation cellular systems. To the best of our knowledge, however, few previous studies have undertaken a thorough investigation of various performance aspects of the use of a simple yet effective packet scheduling algorithm in which multiple CCs are aggregated for transmission in such systems. Consequently, the present study presents an efficient packet scheduling algorithm designed on the basis of the proportional fair criterion for use in multiple-CC systems for downlink transmission. The proposed algorithm includes a focus on providing simultaneous transmission support for both real-time (RT and non-RT traffic. This algorithm can, when applied with sufficiently efficient designs, provide adequate utilization of spectrum resources for the purposes of transmissions, while also improving energy efficiency to some extent. According to simulation results, the performance of the proposed algorithm in terms of system throughput, mean delay, and fairness constitute substantial improvements over those of an algorithm in which the CCs are used independently instead of being aggregated.

Simplifying Multiproject Scheduling Problem Based on Design Structure Matrix and Its Solution by an Improved aiNet Algorithm

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Chunhua Ju

2012-01-01

Full Text Available Managing multiple project is a complex task involving the unrelenting pressures of time and cost. Many studies have proposed various tools and techniques for single-project scheduling; however, the literature further considering multimode or multiproject issues occurring in the real world is rather scarce. In this paper, design structure matrix (DSM and an improved artificial immune network algorithm (aiNet are developed to solve a multi-mode resource-constrained scheduling problem. Firstly, the DSM is used to simplify the mathematic model of multi-project scheduling problem. Subsequently, aiNet algorithm comprised of clonal selection, negative selection, and network suppression is adopted to realize the local searching and global searching, which will assure that it has a powerful searching ability and also avoids the possible combinatorial explosion. Finally, the approach is tested on a set of randomly cases generated from ProGen. The computational results validate the effectiveness of the proposed algorithm comparing with other famous metaheuristic algorithms such as genetic algorithm (GA, simulated annealing algorithm (SA, and ant colony optimization (ACO.

Energy-driven scheduling algorithm for nanosatellite energy harvesting maximization

Science.gov (United States)

Slongo, L. K.; Martínez, S. V.; Eiterer, B. V. B.; Pereira, T. G.; Bezerra, E. A.; Paiva, K. V.

2018-06-01

The number of tasks that a satellite may execute in orbit is strongly related to the amount of energy its Electrical Power System (EPS) is able to harvest and to store. The manner the stored energy is distributed within the satellite has also a great impact on the CubeSat's overall efficiency. Most CubeSat's EPS do not prioritize energy constraints in their formulation. Unlike that, this work proposes an innovative energy-driven scheduling algorithm based on energy harvesting maximization policy. The energy harvesting circuit is mathematically modeled and the solar panel I-V curves are presented for different temperature and irradiance levels. Considering the models and simulations, the scheduling algorithm is designed to keep solar panels working close to their maximum power point by triggering tasks in the appropriate form. Tasks execution affects battery voltage, which is coupled to the solar panels through a protection circuit. A software based Perturb and Observe strategy allows defining the tasks to be triggered. The scheduling algorithm is tested in FloripaSat, which is an 1U CubeSat. A test apparatus is proposed to emulate solar irradiance variation, considering the satellite movement around the Earth. Tests have been conducted to show that the scheduling algorithm improves the CubeSat energy harvesting capability by 4.48% in a three orbit experiment and up to 8.46% in a single orbit cycle in comparison with the CubeSat operating without the scheduling algorithm.

Simultaneous Scheduling of Jobs, AGVs and Tools Considering Tool Transfer Times in Multi Machine FMS By SOS Algorithm

Science.gov (United States)

Sivarami Reddy, N.; Ramamurthy, D. V., Dr.; Prahlada Rao, K., Dr.

2017-08-01

This article addresses simultaneous scheduling of machines, AGVs and tools where machines are allowed to share the tools considering transfer times of jobs and tools between machines, to generate best optimal sequences that minimize makespan in a multi-machine Flexible Manufacturing System (FMS). Performance of FMS is expected to improve by effective utilization of its resources, by proper integration and synchronization of their scheduling. Symbiotic Organisms Search (SOS) algorithm is a potent tool which is a better alternative for solving optimization problems like scheduling and proven itself. The proposed SOS algorithm is tested on 22 job sets with makespan as objective for scheduling of machines and tools where machines are allowed to share tools without considering transfer times of jobs and tools and the results are compared with the results of existing methods. The results show that the SOS has outperformed. The same SOS algorithm is used for simultaneous scheduling of machines, AGVs and tools where machines are allowed to share tools considering transfer times of jobs and tools to determine the best optimal sequences that minimize makespan.

A Heuristic Scheduling Algorithm for Minimizing Makespan and Idle Time in a Nagare Cell

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M. Muthukumaran

2012-01-01

Full Text Available Adopting a focused factory is a powerful approach for today manufacturing enterprise. This paper introduces the basic manufacturing concept for a struggling manufacturer with limited conventional resources, providing an alternative solution to cell scheduling by implementing the technique of Nagare cell. Nagare cell is a Japanese concept with more objectives than cellular manufacturing system. It is a combination of manual and semiautomatic machine layout as cells, which gives maximum output flexibility for all kind of low-to-medium- and medium-to-high- volume productions. The solution adopted is to create a dedicated group of conventional machines, all but one of which are already available on the shop floor. This paper focuses on the development of heuristic scheduling algorithm in step-by-step method. The algorithm states that the summation of processing time of all products on each machine is calculated first and then the sum of processing time is sorted by the shortest processing time rule to get the assignment schedule. Based on the assignment schedule Nagare cell layout is arranged for processing the product. In addition, this algorithm provides steps to determine the product ready time, machine idle time, and product idle time. And also the Gantt chart, the experimental analysis, and the comparative results are illustrated with five (1×8 to 5×8 scheduling problems. Finally, the objective of minimizing makespan and idle time with greater customer satisfaction is studied through.

Discrete Bat Algorithm for Optimal Problem of Permutation Flow Shop Scheduling

Science.gov (United States)

Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang

2014-01-01

A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem. PMID:25243220

Discrete bat algorithm for optimal problem of permutation flow shop scheduling.

Science.gov (United States)

Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang

2014-01-01

A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem.

An Enhanced Feedback-Base Downlink Packet Scheduling Algorithm for Mobile TV in WIMAX Networks

Directory of Open Access Journals (Sweden)

Joseph Oyewale

2013-06-01

Full Text Available With high speed access network technology like WIMAX, there is the need for efficient management of radio resources where the throughput and Qos requirements for Multicasting Broadcasting Services (MBS for example TV are to be met. An enhanced  feedback-base downlink Packet scheduling algorithm  that can be used in IEEE 802.16d/e networks for mobile TV “one way traffic”(MBS is needed to support many users utilizing multiuser diversity of the  broadband of WIMAX systems where a group of users(good/worst channels share allocated resources (bandwidth. This paper proposes a WIMAX framework feedback-base (like a channel-awareness downlink packet scheduling algorithm for Mobile TV traffics in IEEE806.16, in which network Physical Timing Slots (PSs resource blocks are allocated in a dynamic way to mobile TV subscribers based on the Channel State information (CSI feedback, and then considering users with worst channels with the aim of improving system throughput while system coverage is being guaranteed. The algorithm was examined by changing the PSs bandwidth allocation of the users and different number of users of a cell. Simulation results show our proposed algorithm performed better than other algorithms (blind algorithms in terms of improvement in system throughput performance. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso

Solving Multi-Resource Constrained Project Scheduling Problem using Ant Colony Optimization

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Hsiang-Hsi Huang

2015-01-01

Full Text Available This paper applied Ant Colony Optimization (ACO to develop a resource constraints scheduling model to achieve the resource allocation optimization and the shortest completion time of a project under resource constraints and the activities precedence requirement for projects. Resource leveling is also discussed and has to be achieved under the resource allocation optimization in this research. Testing cases and examples adopted from the international test bank were studied for verifying the effectiveness of the proposed model. The results showed that the solutions of different cases all have a better performance within a reasonable time. These can be obtained through ACO algorithm under the same constrained conditions. A program was written for the proposed model that is able to automatically produce the project resource requirement figure after the project duration is solved.

SYSTEMATIC LITERATURE REVIEW ON RESOURCE ALLOCATION AND RESOURCE SCHEDULING IN CLOUD COMPUTING

OpenAIRE

B. Muni Lavanya; C. Shoba Bindu

2016-01-01

The objective the work is intended to highlight the key features and afford finest future directions in the research community of Resource Allocation, Resource Scheduling and Resource management from 2009 to 2016. Exemplifying how research on Resource Allocation, Resource Scheduling and Resource management has progressively increased in the past decade by inspecting articles, papers from scientific and standard publications. Survey materialized in three-fold process. Firstly, investigate on t...

Proportional fair scheduling algorithm based on traffic in satellite communication system

Science.gov (United States)

Pan, Cheng-Sheng; Sui, Shi-Long; Liu, Chun-ling; Shi, Yu-Xin

2018-02-01

In the satellite communication network system, in order to solve the problem of low system capacity and user fairness in multi-user access to satellite communication network in the downlink, combined with the characteristics of user data service, an algorithm study on throughput capacity and user fairness scheduling is proposed - Proportional Fairness Algorithm Based on Traffic(B-PF). The algorithm is improved on the basis of the proportional fairness algorithm in the wireless communication system, taking into account the user channel condition and caching traffic information. The user outgoing traffic is considered as the adjustment factor of the scheduling priority and presents the concept of traffic satisfaction. Firstly,the algorithm calculates the priority of the user according to the scheduling algorithm and dispatches the users with the highest priority. Secondly, when a scheduled user is the business satisfied user, the system dispatches the next priority user. The simulation results show that compared with the PF algorithm, B-PF can improve the system throughput, the business satisfaction and fairness.

Heuristic Scheduling Algorithm Oriented Dynamic Tasks for Imaging Satellites

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Maocai Wang

2014-01-01

Full Text Available Imaging satellite scheduling is an NP-hard problem with many complex constraints. This paper researches the scheduling problem for dynamic tasks oriented to some emergency cases. After the dynamic properties of satellite scheduling were analyzed, the optimization model is proposed in this paper. Based on the model, two heuristic algorithms are proposed to solve the problem. The first heuristic algorithm arranges new tasks by inserting or deleting them, then inserting them repeatedly according to the priority from low to high, which is named IDI algorithm. The second one called ISDR adopts four steps: insert directly, insert by shifting, insert by deleting, and reinsert the tasks deleted. Moreover, two heuristic factors, congestion degree of a time window and the overlapping degree of a task, are employed to improve the algorithm’s performance. Finally, a case is given to test the algorithms. The results show that the IDI algorithm is better than ISDR from the running time point of view while ISDR algorithm with heuristic factors is more effective with regard to algorithm performance. Moreover, the results also show that our method has good performance for the larger size of the dynamic tasks in comparison with the other two methods.

Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment.

Science.gov (United States)

Abdullahi, Mohammed; Ngadi, Md Asri

2016-01-01

Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS) has been shown to perform competitively with Particle Swarm Optimization (PSO). The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA) based SOS (SASOS) in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.

Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment.

Directory of Open Access Journals (Sweden)

Mohammed Abdullahi

Full Text Available Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS has been shown to perform competitively with Particle Swarm Optimization (PSO. The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA based SOS (SASOS in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.

Scheduling Diet for Diabetes Mellitus Patients using Genetic Algorithm

Science.gov (United States)

Syahputra, M. F.; Felicia, V.; Rahmat, R. F.; Budiarto, R.

2017-01-01

Diabetes Melitus (DM) is one of metabolic diseases which affects on productivity and lowers the human resources quality. This disease can be controlled by maintaining and regulating balanced and healthy lifestyle especially for daily diet. However, nowadays, there is no system able to help DM patient to get any information of proper diet. Therefore, an approach is required to provide scheduling diet every day in a week with appropriate nutrition for DM patients to help them regulate their daily diet for healing this disease. In this research, we calculate the number of caloric needs using Harris-Benedict equation and propose genetic algorithm for scheduling diet for DM patient. The results show that the greater the number of individuals, the greater the more the possibility of changes in fitness score approaches the best fitness score. Moreover, the greater the created generation, the more the opportunites to obtain best individual with fitness score approaching 0 or equal to 0.

An Improved Genetic Algorithm for Single-Machine Inverse Scheduling Problem

Directory of Open Access Journals (Sweden)

Jianhui Mou

2014-01-01

Full Text Available The goal of the scheduling is to arrange operations on suitable machines with optimal sequence for corresponding objectives. In order to meet market requirements, scheduling systems must own enough flexibility against uncertain events. These events can change production status or processing parameters, even causing the original schedule to no longer be optimal or even to be infeasible. Traditional scheduling strategies, however, cannot cope with these cases. Therefore, a new idea of scheduling called inverse scheduling has been proposed. In this paper, the inverse scheduling with weighted completion time (SMISP is considered in a single-machine shop environment. In this paper, an improved genetic algorithm (IGA with a local searching strategy is proposed. To improve the performance of IGA, efficient encoding scheme, fitness evaluation mechanism, feasible initialization methods, and a local search procedure have been employed in the paper. Because of the local improving method, the proposed IGA can balance its exploration ability and exploitation ability. We adopt 27 instances to verify the effectiveness of the proposed algorithm. The experimental results illustrated that the proposed algorithm can generate satisfactory solutions. This approach also has been applied to solve the scheduling problem in the real Chinese shipyard and can bring some benefits.

Performance comparison of some evolutionary algorithms on job shop scheduling problems

Science.gov (United States)

Mishra, S. K.; Rao, C. S. P.

2016-09-01

Job Shop Scheduling as a state space search problem belonging to NP-hard category due to its complexity and combinational explosion of states. Several naturally inspire evolutionary methods have been developed to solve Job Shop Scheduling Problems. In this paper the evolutionary methods namely Particles Swarm Optimization, Artificial Intelligence, Invasive Weed Optimization, Bacterial Foraging Optimization, Music Based Harmony Search Algorithms are applied and find tuned to model and solve Job Shop Scheduling Problems. To compare about 250 Bench Mark instances have been used to evaluate the performance of these algorithms. The capabilities of each these algorithms in solving Job Shop Scheduling Problems are outlined.

Continuity-Aware Scheduling Algorithm for Scalable Video Streaming

Directory of Open Access Journals (Sweden)

Atinat Palawan

2016-05-01

Full Text Available The consumer demand for retrieving and delivering visual content through consumer electronic devices has increased rapidly in recent years. The quality of video in packet networks is susceptible to certain traffic characteristics: average bandwidth availability, loss, delay and delay variation (jitter. This paper presents a scheduling algorithm that modifies the stream of scalable video to combat jitter. The algorithm provides unequal look-ahead by safeguarding the base layer (without the need for overhead of the scalable video. The results of the experiments show that our scheduling algorithm reduces the number of frames with a violated deadline and significantly improves the continuity of the video stream without compromising the average Y Peek Signal-to-Noise Ratio (PSNR.

ALGORITHMIC CONSTRUCTION SCHEDULES IN CONDITIONS OF TIMING CONSTRAINTS

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Alexey S. Dobrynin

2014-01-01

Full Text Available Tasks of time-schedule construction (JSSP in various fields of human activities have an important theoretical and practical significance. The main feature of these tasks is a timing requirement, describing allowed planning time periods and periods of downtime. This article describes implementation variations of the work scheduling algorithm under timing requirements for the tasks of industrial time-schedules construction, and service activities.

Hybrid Genetic Algorithm with Multiparents Crossover for Job Shop Scheduling Problems

Directory of Open Access Journals (Sweden)

Noor Hasnah Moin

2015-01-01

Full Text Available The job shop scheduling problem (JSSP is one of the well-known hard combinatorial scheduling problems. This paper proposes a hybrid genetic algorithm with multiparents crossover for JSSP. The multiparents crossover operator known as extended precedence preservative crossover (EPPX is able to recombine more than two parents to generate a single new offspring distinguished from common crossover operators that recombine only two parents. This algorithm also embeds a schedule generation procedure to generate full-active schedule that satisfies precedence constraints in order to reduce the search space. Once a schedule is obtained, a neighborhood search is applied to exploit the search space for better solutions and to enhance the GA. This hybrid genetic algorithm is simulated on a set of benchmarks from the literatures and the results are compared with other approaches to ensure the sustainability of this algorithm in solving JSSP. The results suggest that the implementation of multiparents crossover produces competitive results.

A DIFFERENTIAL EVOLUTION ALGORITHM DEVELOPED FOR A NURSE SCHEDULING PROBLEM

Directory of Open Access Journals (Sweden)

Shahnazari-Shahrezaei, P.

2012-11-01

Full Text Available Nurse scheduling is a type of manpower allocation problem that tries to satisfy hospital managers objectives and nurses preferences as much as possible by generating fair shift schedules. This paper presents a nurse scheduling problem based on a real case study, and proposes two meta-heuristics a differential evolution algorithm (DE and a greedy randomised adaptive search procedure (GRASP to solve it. To investigate the efficiency of the proposed algorithms, two problems are solved. Furthermore, some comparison metrics are applied to examine the reliability of the proposed algorithms. The computational results in this paper show that the proposed DE outperforms the GRASP.

Algorithm of composing the schedule of construction and installation works

Science.gov (United States)

Nehaj, Rustam; Molotkov, Georgij; Rudchenko, Ivan; Grinev, Anatolij; Sekisov, Aleksandr

2017-10-01

An algorithm for scheduling works is developed, in which the priority of the work corresponds to the total weight of the subordinate works, the vertices of the graph, and it is proved that for graphs of the tree type the algorithm is optimal. An algorithm is synthesized to reduce the search for solutions when drawing up schedules of construction and installation works, allocating a subset with the optimal solution of the problem of the minimum power, which is determined by the structure of its initial data and numerical values. An algorithm for scheduling construction and installation work is developed, taking into account the schedule for the movement of brigades, which is characterized by the possibility to efficiently calculate the values of minimizing the time of work performance by the parameters of organizational and technological reliability through the use of the branch and boundary method. The program of the computational algorithm was compiled in the MatLAB-2008 program. For the initial data of the matrix, random numbers were taken, uniformly distributed in the range from 1 to 100. It takes 0.5; 2.5; 7.5; 27 minutes to solve the problem. Thus, the proposed method for estimating the lower boundary of the solution is sufficiently accurate and allows efficient solution of the minimax task of scheduling construction and installation works.

A new genetic algorithm for flexible job-shop scheduling problems

International Nuclear Information System (INIS)

Driss, Imen; Mouss, Kinza Nadia; Laggoun, Assia

2015-01-01

Flexible job-shop scheduling problem (FJSP), which is proved to be NP-hard, is an extension of the classical job-shop scheduling problem. In this paper, we propose a new genetic algorithm (NGA) to solve FJSP to minimize makespan. This new algorithm uses a new chromosome representation and adopts different strategies for crossover and mutation. The proposed algorithm is validated on a series of benchmark data sets and tested on data from a drug manufacturing company. Experimental results prove that the NGA is more efficient and competitive than some other existing algorithms.

A new genetic algorithm for flexible job-shop scheduling problems

Energy Technology Data Exchange (ETDEWEB)

Driss, Imen; Mouss, Kinza Nadia; Laggoun, Assia [University of Batna, Batna (Algeria)

2015-03-15

Flexible job-shop scheduling problem (FJSP), which is proved to be NP-hard, is an extension of the classical job-shop scheduling problem. In this paper, we propose a new genetic algorithm (NGA) to solve FJSP to minimize makespan. This new algorithm uses a new chromosome representation and adopts different strategies for crossover and mutation. The proposed algorithm is validated on a series of benchmark data sets and tested on data from a drug manufacturing company. Experimental results prove that the NGA is more efficient and competitive than some other existing algorithms.

Design Principles and Algorithms for Air Traffic Arrival Scheduling

Science.gov (United States)

Erzberger, Heinz; Itoh, Eri

2014-01-01

This report presents design principles and algorithms for building a real-time scheduler of arrival aircraft based on a first-come-first-served (FCFS) scheduling protocol. The algorithms provide the conceptual and computational foundation for the Traffic Management Advisor (TMA) of the Center/terminal radar approach control facilities (TRACON) automation system, which comprises a set of decision support tools for managing arrival traffic at major airports in the United States. The primary objective of the scheduler is to assign arrival aircraft to a favorable landing runway and schedule them to land at times that minimize delays. A further objective of the scheduler is to allocate delays between high-altitude airspace far away from the airport and low-altitude airspace near the airport. A method of delay allocation is described that minimizes the average operating cost in the presence of errors in controlling aircraft to a specified landing time. This report is a revision of an earlier paper first presented as part of an Advisory Group for Aerospace Research and Development (AGARD) lecture series in September 1995. The authors, during vigorous discussions over the details of this paper, felt it was important to the air-trafficmanagement (ATM) community to revise and extend the original 1995 paper, providing more detail and clarity and thereby allowing future researchers to understand this foundational work as the basis for the TMA's scheduling algorithms.

A new scheduling algorithm for parallel sparse LU factorization with static pivoting

Energy Technology Data Exchange (ETDEWEB)

Grigori, Laura; Li, Xiaoye S.

2002-08-20

In this paper we present a static scheduling algorithm for parallel sparse LU factorization with static pivoting. The algorithm is divided into mapping and scheduling phases, using the symmetric pruned graphs of L' and U to represent dependencies. The scheduling algorithm is designed for driving the parallel execution of the factorization on a distributed-memory architecture. Experimental results and comparisons with SuperLU{_}DIST are reported after applying this algorithm on real world application matrices on an IBM SP RS/6000 distributed memory machine.

Cloud computing task scheduling strategy based on improved differential evolution algorithm

Science.gov (United States)

Ge, Junwei; He, Qian; Fang, Yiqiu

2017-04-01

In order to optimize the cloud computing task scheduling scheme, an improved differential evolution algorithm for cloud computing task scheduling is proposed. Firstly, the cloud computing task scheduling model, according to the model of the fitness function, and then used improved optimization calculation of the fitness function of the evolutionary algorithm, according to the evolution of generation of dynamic selection strategy through dynamic mutation strategy to ensure the global and local search ability. The performance test experiment was carried out in the CloudSim simulation platform, the experimental results show that the improved differential evolution algorithm can reduce the cloud computing task execution time and user cost saving, good implementation of the optimal scheduling of cloud computing tasks.

SOLVING FLOWSHOP SCHEDULING PROBLEMS USING A DISCRETE AFRICAN WILD DOG ALGORITHM

Directory of Open Access Journals (Sweden)

M. K. Marichelvam

2013-04-01

Full Text Available The problem of m-machine permutation flowshop scheduling is considered in this paper. The objective is to minimize the makespan. The flowshop scheduling problem is a typical combinatorial optimization problem and has been proved to be strongly NP-hard. Hence, several heuristics and meta-heuristics were addressed by the researchers. In this paper, a discrete African wild dog algorithm is applied for solving the flowshop scheduling problems. Computational results using benchmark problems show that the proposed algorithm outperforms many other algorithms addressed in the literature.

Research on Scheduling Algorithm for Multi-satellite and Point Target Task on Swinging Mode

Science.gov (United States)

Wang, M.; Dai, G.; Peng, L.; Song, Z.; Chen, G.

2012-12-01

Nowadays, using satellite in space to observe ground is an important and major method to obtain ground information. With the development of the scientific technology in the field of space, many fields such as military and economic and other areas have more and more requirement of space technology because of the benefits of the satellite's widespread, timeliness and unlimited of area and country. And at the same time, because of the wide use of all kinds of satellites, sensors, repeater satellites and ground receiving stations, ground control system are now facing great challenge. Therefore, how to make the best value of satellite resources so as to make full use of them becomes an important problem of ground control system. Satellite scheduling is to distribute the resource to all tasks without conflict to obtain the scheduling result so as to complete as many tasks as possible to meet user's requirement under considering the condition of the requirement of satellites, sensors and ground receiving stations. Considering the size of the task, we can divide tasks into point task and area task. This paper only considers point targets. In this paper, a description of satellite scheduling problem and a chief introduction of the theory of satellite scheduling are firstly made. We also analyze the restriction of resource and task in scheduling satellites. The input and output flow of scheduling process are also chiefly described in the paper. On the basis of these analyses, we put forward a scheduling model named as multi-variable optimization model for multi-satellite and point target task on swinging mode. In the multi-variable optimization model, the scheduling problem is transformed the parametric optimization problem. The parameter we wish to optimize is the swinging angle of every time-window. In the view of the efficiency and accuracy, some important problems relating the satellite scheduling such as the angle relation between satellites and ground targets, positive

Application of cultural algorithm to generation scheduling of hydrothermal systems

International Nuclear Information System (INIS)

Yuan Xiaohui; Yuan Yanbin

2006-01-01

The daily generation scheduling of hydrothermal power systems plays an important role in the operation of electric power systems for economics and security, which is a large scale dynamic non-linear constrained optimization problem. It is difficult to solve using traditional optimization methods. This paper proposes a new cultural algorithm to solve the optimal daily generation scheduling of hydrothermal power systems. The approach takes the water transport delay time between connected reservoirs into consideration and can conveniently deal with the complicated hydraulic coupling simultaneously. An example is used to verify the correctness and effectiveness of the proposed cultural algorithm, comparing with both the Lagrange method and the genetic algorithm method. The simulation results demonstrate that the proposed algorithm has rapid convergence speed and higher solution precision. Thus, an effective method is provided to solve the optimal daily generation scheduling of hydrothermal systems

Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms

OpenAIRE

Luo, Zihan; Armour, Simon; McGeehan, Joe

2015-01-01

This paper shows how a genetic algorithm can be used as a method of obtaining the near-optimal solution of the resource block scheduling problem in a cooperative cellular network. An exhaustive search is initially implementedto guarantee that the optimal result, in terms of maximizing the bandwidth efficiency of the overall network, is found, and then the genetic algorithm with the properly selected termination conditions is used in the same network. The simulation results show that the genet...

Scheduling with Time Lags

NARCIS (Netherlands)

X. Zhang (Xiandong)

2010-01-01

textabstractScheduling is essential when activities need to be allocated to scarce resources over time. Motivated by the problem of scheduling barges along container terminals in the Port of Rotterdam, this thesis designs and analyzes algorithms for various on-line and off-line scheduling problems

Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system

International Nuclear Information System (INIS)

Logenthiran, T.; Srinivasan, Dipti; Khambadkone, Ashwin M.

2011-01-01

This paper proposes a multi-agent system for energy resource scheduling of an islanded power system with distributed resources, which consists of integrated microgrids and lumped loads. Distributed intelligent multi-agent technology is applied to make the power system more reliable, efficient and capable of exploiting and integrating alternative sources of energy. The algorithm behind the proposed energy resource scheduling has three stages. The first stage is to schedule each microgrid individually to satisfy its internal demand. The next stage involves finding the best possible bids for exporting power to the network and compete in a whole sale energy market. The final stage is to reschedule each microgrid individually to satisfy the total demand, which is the addition of internal demand and the demand from the results of the whole sale energy market simulation. The simulation results of a power system with distributed resources comprising three microgrids and five lumped loads show that the proposed multi-agent system allows efficient management of micro-sources with minimum operational cost. The case studies demonstrate that the system is successfully monitored, controlled and operated by means of the developed multi-agent system. (author)

Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system

Energy Technology Data Exchange (ETDEWEB)

Logenthiran, T.; Srinivasan, Dipti; Khambadkone, Ashwin M. [Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576 (Singapore)

2011-01-15

This paper proposes a multi-agent system for energy resource scheduling of an islanded power system with distributed resources, which consists of integrated microgrids and lumped loads. Distributed intelligent multi-agent technology is applied to make the power system more reliable, efficient and capable of exploiting and integrating alternative sources of energy. The algorithm behind the proposed energy resource scheduling has three stages. The first stage is to schedule each microgrid individually to satisfy its internal demand. The next stage involves finding the best possible bids for exporting power to the network and compete in a whole sale energy market. The final stage is to reschedule each microgrid individually to satisfy the total demand, which is the addition of internal demand and the demand from the results of the whole sale energy market simulation. The simulation results of a power system with distributed resources comprising three microgrids and five lumped loads show that the proposed multi-agent system allows efficient management of micro-sources with minimum operational cost. The case studies demonstrate that the system is successfully monitored, controlled and operated by means of the developed multi-agent system. (author)

Integrated multi-resource planning and scheduling in engineering project

Directory of Open Access Journals (Sweden)

Samer Ben Issa

2017-01-01

Full Text Available Planning and scheduling processes in project management are carried out sequentially in prac-tice, i.e. planning project activities first without visibility of resource limitation, and then schedul-ing the project according to these pre-planned activities. This is a need to integrate these two pro-cesses. In this paper, we use Branch and Bound approach for generating all the feasible and non-feasible project schedules with/without activity splitting, and with a new criterion called “the Minimum Moments of Resources Required around X-Y axes (MMORR”, we select the best feasible project schedule to integrate plan processing and schedule processing for engineering projects. The results illustrate that this integrated approach can effectively select the best feasible project schedule among alternatives, improves the resource utilization, and shortens the project lead time.

Algorithm for Public Electric Transport Schedule Control for Intelligent Embedded Devices

Science.gov (United States)

Alps, Ivars; Potapov, Andrey; Gorobetz, Mikhail; Levchenkov, Anatoly

2010-01-01

In this paper authors present heuristics algorithm for precise schedule fulfilment in city traffic conditions taking in account traffic lights. The algorithm is proposed for programmable controller. PLC is proposed to be installed in electric vehicle to control its motion speed and signals of traffic lights. Algorithm is tested using real controller connected to virtual devices and real functional models of real tram devices. Results of experiments show high precision of public transport schedule fulfilment using proposed algorithm.

Cross-Layer Scheduling and Resource Allocation for Heterogeneous Traffic in 3G LTE

Directory of Open Access Journals (Sweden)

Richard Musabe

2014-01-01

Full Text Available 3G long term evolution (LTE introduces stringent needs in order to provide different kinds of traffic with Quality of Service (QoS characteristics. The major problem with this nature of LTE is that it does not have any paradigm scheduling algorithm that will ideally control the assignment of resources which in turn will improve the user satisfaction. This has become an open subject and different scheduling algorithms have been proposed which are quite challenging and complex. To address this issue, in this paper, we investigate how our proposed algorithm improves the user satisfaction for heterogeneous traffic, that is, best-effort traffic such as file transfer protocol (FTP and real-time traffic such as voice over internet protocol (VoIP. Our proposed algorithm is formulated using the cross-layer technique. The goal of our proposed algorithm is to maximize the expected total user satisfaction (total-utility under different constraints. We compared our proposed algorithm with proportional fair (PF, exponential proportional fair (EXP-PF, and U-delay. Using simulations, our proposed algorithm improved the performance of real-time traffic based on throughput, VoIP delay, and VoIP packet loss ratio metrics while PF improved the performance of best-effort traffic based on FTP traffic received, FTP packet loss ratio, and FTP throughput metrics.

A Novel Assembly Line Scheduling Algorithm Based on CE-PSO

Directory of Open Access Journals (Sweden)

Xiaomei Hu

2015-01-01

Full Text Available With the widespread application of assembly line in enterprises, assembly line scheduling is an important problem in the production since it directly affects the productivity of the whole manufacturing system. The mathematical model of assembly line scheduling problem is put forward and key data are confirmed. A double objective optimization model based on equipment utilization and delivery time loss is built, and optimization solution strategy is described. Based on the idea of solution strategy, assembly line scheduling algorithm based on CE-PSO is proposed to overcome the shortcomings of the standard PSO. Through the simulation experiments of two examples, the validity of the assembly line scheduling algorithm based on CE-PSO is proved.

Optimization of Hierarchically Scheduled Heterogeneous Embedded Systems

DEFF Research Database (Denmark)

Pop, Traian; Pop, Paul; Eles, Petru

2005-01-01

We present an approach to the analysis and optimization of heterogeneous distributed embedded systems. The systems are heterogeneous not only in terms of hardware components, but also in terms of communication protocols and scheduling policies. When several scheduling policies share a resource......, they are organized in a hierarchy. In this paper, we address design problems that are characteristic to such hierarchically scheduled systems: assignment of scheduling policies to tasks, mapping of tasks to hardware components, and the scheduling of the activities. We present algorithms for solving these problems....... Our heuristics are able to find schedulable implementations under limited resources, achieving an efficient utilization of the system. The developed algorithms are evaluated using extensive experiments and a real-life example....

Online Algorithms for Parallel Job Scheduling and Strip Packing

NARCIS (Netherlands)

Hurink, Johann L.; Paulus, J.J.

We consider the online scheduling problem of parallel jobs on parallel machines, $P|online{−}list,m_j |C_{max}$. For this problem we present a 6.6623-competitive algorithm. This improves the best known 7-competitive algorithm for this problem. The presented algorithm also applies to the problem

Cooperative Scheduling of Imaging Observation Tasks for High-Altitude Airships Based on Propagation Algorithm

Directory of Open Access Journals (Sweden)

He Chuan

2012-01-01

Full Text Available The cooperative scheduling problem on high-altitude airships for imaging observation tasks is discussed. A constraint programming model is established by analyzing the main constraints, which takes the maximum task benefit and the minimum cruising distance as two optimization objectives. The cooperative scheduling problem of high-altitude airships is converted into a main problem and a subproblem by adopting hierarchy architecture. The solution to the main problem can construct the preliminary matching between tasks and observation resource in order to reduce the search space of the original problem. Furthermore, the solution to the sub-problem can detect the key nodes that each airship needs to fly through in sequence, so as to get the cruising path. Firstly, the task set is divided by using k-core neighborhood growth cluster algorithm (K-NGCA. Then, a novel swarm intelligence algorithm named propagation algorithm (PA is combined with the key node search algorithm (KNSA to optimize the cruising path of each airship and determine the execution time interval of each task. Meanwhile, this paper also provides the realization approach of the above algorithm and especially makes a detailed introduction on the encoding rules, search models, and propagation mechanism of the PA. Finally, the application results and comparison analysis show the proposed models and algorithms are effective and feasible.

Scheduling algorithms for rapid imaging using agile Cubesat constellations

Science.gov (United States)

Nag, Sreeja; Li, Alan S.; Merrick, James H.

2018-02-01

Distributed Space Missions such as formation flight and constellations, are being recognized as important Earth Observation solutions to increase measurement samples over space and time. Cubesats are increasing in size (27U, ∼40 kg in development) with increasing capabilities to host imager payloads. Given the precise attitude control systems emerging in the commercial market, Cubesats now have the ability to slew and capture images within short notice. We propose a modular framework that combines orbital mechanics, attitude control and scheduling optimization to plan the time-varying, full-body orientation of agile Cubesats in a constellation such that they maximize the number of observed images and observation time, within the constraints of Cubesat hardware specifications. The attitude control strategy combines bang-bang and PD control, with constraints such as power consumption, response time, and stability factored into the optimality computations and a possible extension to PID control to account for disturbances. Schedule optimization is performed using dynamic programming with two levels of heuristics, verified and improved upon using mixed integer linear programming. The automated scheduler is expected to run on ground station resources and the resultant schedules uplinked to the satellites for execution, however it can be adapted for onboard scheduling, contingent on Cubesat hardware and software upgrades. The framework is generalizable over small steerable spacecraft, sensor specifications, imaging objectives and regions of interest, and is demonstrated using multiple 20 kg satellites in Low Earth Orbit for two case studies - rapid imaging of Landsat's land and coastal images and extended imaging of global, warm water coral reefs. The proposed algorithm captures up to 161% more Landsat images than nadir-pointing sensors with the same field of view, on a 2-satellite constellation over a 12-h simulation. Integer programming was able to verify that

Cultural-Based Genetic Tabu Algorithm for Multiobjective Job Shop Scheduling

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Yuzhen Yang

2014-01-01

Full Text Available The job shop scheduling problem, which has been dealt with by various traditional optimization methods over the decades, has proved to be an NP-hard problem and difficult in solving, especially in the multiobjective field. In this paper, we have proposed a novel quadspace cultural genetic tabu algorithm (QSCGTA to solve such problem. This algorithm provides a different structure from the original cultural algorithm in containing double brief spaces and population spaces. These spaces deal with different levels of populations globally and locally by applying genetic and tabu searches separately and exchange information regularly to make the process more effective towards promising areas, along with modified multiobjective domination and transform functions. Moreover, we have presented a bidirectional shifting for the decoding process of job shop scheduling. The computational results we presented significantly prove the effectiveness and efficiency of the cultural-based genetic tabu algorithm for the multiobjective job shop scheduling problem.

Scheduling algorithms for saving energy and balancing load

Energy Technology Data Exchange (ETDEWEB)

Antoniadis, Antonios

2012-08-03

In this thesis we study problems of scheduling tasks in computing environments. We consider both the modern objective function of minimizing energy consumption, and the classical objective of balancing load across machines. We first investigate offline deadline-based scheduling in the setting of a single variable-speed processor that is equipped with a sleep state. The objective is that of minimizing the total energy consumption. Apart from settling the complexity of the problem by showing its NP-hardness, we provide a lower bound of 2 for general convex power functions, and a particular natural class of schedules called s{sub crit}-schedules. We also present an algorithmic framework for designing good approximation algorithms. For general convex power functions our framework improves the best known approximation-factor from 2 to 4/3. This factor can be reduced even further to 137/117 for a specific well-motivated class of power functions. Furthermore, we give tight bounds to show that our framework returns optimal s{sub crit}-schedules for the two aforementioned power-function classes. We then focus on the multiprocessor setting where each processor has the ability to vary its speed. Job migration is allowed, and we again consider classical deadline-based scheduling with the objective of energy minimization. We first study the offline problem and show that optimal schedules can be computed efficiently in polynomial time for any convex and non-decreasing power function. Our algorithm relies on repeated maximum flow computations. Regarding the online problem and power functions P(s) = s{sup {alpha}}, where s is the processor speed and {alpha} > 1 a constant, we extend the two well-known single-processor algorithms Optimal Available and Average Rate. We prove that Optimal Available is {alpha}{sup {alpha}}-competitive as in the single-processor case. For Average Rate we show a competitive factor of (2{alpha}){sup {alpha}}/2 + 1, i.e., compared to the single

Simulated Annealing Genetic Algorithm Based Schedule Risk Management of IT Outsourcing Project

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Fuqiang Lu

2017-01-01

Full Text Available IT outsourcing is an effective way to enhance the core competitiveness for many enterprises. But the schedule risk of IT outsourcing project may cause enormous economic loss to enterprise. In this paper, the Distributed Decision Making (DDM theory and the principal-agent theory are used to build a model for schedule risk management of IT outsourcing project. In addition, a hybrid algorithm combining simulated annealing (SA and genetic algorithm (GA is designed, namely, simulated annealing genetic algorithm (SAGA. The effect of the proposed model on the schedule risk management problem is analyzed in the simulation experiment. Meanwhile, the simulation results of the three algorithms GA, SA, and SAGA show that SAGA is the most superior one to the other two algorithms in terms of stability and convergence. Consequently, this paper provides the scientific quantitative proposal for the decision maker who needs to manage the schedule risk of IT outsourcing project.

Algorithms for classical and modern scheduling problems

OpenAIRE

Ott, Sebastian

2016-01-01

Subject of this thesis is the design and the analysis of algorithms for scheduling problems. In the first part, we focus on energy-efficient scheduling, where one seeks to minimize the energy needed for processing certain jobs via dynamic adjustments of the processing speed (speed scaling). We consider variations and extensions of the standard model introduced by Yao, Demers, and Shenker in 1995 [79], including the addition of a sleep state, the avoidance of preemption, and variable speed lim...

Scheduling Algorithms for Maximizing Throughput with Zero-Forcing Beamforming in a MIMO Wireless System

Science.gov (United States)

Foronda, Augusto; Ohta, Chikara; Tamaki, Hisashi

Dirty paper coding (DPC) is a strategy to achieve the region capacity of multiple input multiple output (MIMO) downlink channels and a DPC scheduler is throughput optimal if users are selected according to their queue states and current rates. However, DPC is difficult to implement in practical systems. One solution, zero-forcing beamforming (ZFBF) strategy has been proposed to achieve the same asymptotic sum rate capacity as that of DPC with an exhaustive search over the entire user set. Some suboptimal user group selection schedulers with reduced complexity based on ZFBF strategy (ZFBF-SUS) and proportional fair (PF) scheduling algorithm (PF-ZFBF) have also been proposed to enhance the throughput and fairness among the users, respectively. However, they are not throughput optimal, fairness and throughput decrease if each user queue length is different due to different users channel quality. Therefore, we propose two different scheduling algorithms: a throughput optimal scheduling algorithm (ZFBF-TO) and a reduced complexity scheduling algorithm (ZFBF-RC). Both are based on ZFBF strategy and, at every time slot, the scheduling algorithms have to select some users based on user channel quality, user queue length and orthogonality among users. Moreover, the proposed algorithms have to produce the rate allocation and power allocation for the selected users based on a modified water filling method. We analyze the schedulers complexity and numerical results show that ZFBF-RC provides throughput and fairness improvements compared to the ZFBF-SUS and PF-ZFBF scheduling algorithms.

CQPSO scheduling algorithm for heterogeneous multi-core DAG task model

Science.gov (United States)

Zhai, Wenzheng; Hu, Yue-Li; Ran, Feng

2017-07-01

Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was minimized. The experimental results show that the proposed algorithm has the advantage of optimization abilities, simple and feasible, fast convergence, and can be applied to the task scheduling optimization for other heterogeneous and distributed environment.

Computer-aided resource planning and scheduling for radiological services

Science.gov (United States)

Garcia, Hong-Mei C.; Yun, David Y.; Ge, Yiqun; Khan, Javed I.

1996-05-01

There exists tremendous opportunity in hospital-wide resource optimization based on system integration. This paper defines the resource planning and scheduling requirements integral to PACS, RIS and HIS integration. An multi-site case study is conducted to define the requirements. A well-tested planning and scheduling methodology, called Constrained Resource Planning model, has been applied to the chosen problem of radiological service optimization. This investigation focuses on resource optimization issues for minimizing the turnaround time to increase clinical efficiency and customer satisfaction, particularly in cases where the scheduling of multiple exams are required for a patient. How best to combine the information system efficiency and human intelligence in improving radiological services is described. Finally, an architecture for interfacing a computer-aided resource planning and scheduling tool with the existing PACS, HIS and RIS implementation is presented.

A Scheduling Algorithm for Minimizing the Packet Error Probability in Clusterized TDMA Networks

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Arash T. Toyserkani

2009-01-01

Full Text Available We consider clustered wireless networks, where transceivers in a cluster use a time-slotted mechanism (TDMA to access a wireless channel that is shared among several clusters. An approximate expression for the packet-loss probability is derived for networks with one or more mutually interfering clusters in Rayleigh fading environments, and the approximation is shown to be good for relevant scenarios. We then present a scheduling algorithm, based on Lagrangian duality, that exploits the derived packet-loss model in an attempt to minimize the average packet-loss probability in the network. Computer simulations of the proposed scheduling algorithm show that a significant increase in network throughput can be achieved compared to uncoordinated scheduling. Empirical trials also indicate that the proposed optimization algorithm almost always converges to an optimal schedule with a reasonable number of iterations. Thus, the proposed algorithm can also be used for bench-marking suboptimal scheduling algorithms.

Evolutionary Hybrid Particle Swarm Optimization Algorithm for Solving NP-Hard No-Wait Flow Shop Scheduling Problems

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Laxmi A. Bewoor

2017-10-01

Full Text Available The no-wait flow shop is a flowshop in which the scheduling of jobs is continuous and simultaneous through all machines without waiting for any consecutive machines. The scheduling of a no-wait flow shop requires finding an appropriate sequence of jobs for scheduling, which in turn reduces total processing time. The classical brute force method for finding the probabilities of scheduling for improving the utilization of resources may become trapped in local optima, and this problem can hence be observed as a typical NP-hard combinatorial optimization problem that requires finding a near optimal solution with heuristic and metaheuristic techniques. This paper proposes an effective hybrid Particle Swarm Optimization (PSO metaheuristic algorithm for solving no-wait flow shop scheduling problems with the objective of minimizing the total flow time of jobs. This Proposed Hybrid Particle Swarm Optimization (PHPSO algorithm presents a solution by the random key representation rule for converting the continuous position information values of particles to a discrete job permutation. The proposed algorithm initializes population efficiently with the Nawaz-Enscore-Ham (NEH heuristic technique and uses an evolutionary search guided by the mechanism of PSO, as well as simulated annealing based on a local neighborhood search to avoid getting stuck in local optima and to provide the appropriate balance of global exploration and local exploitation. Extensive computational experiments are carried out based on Taillard’s benchmark suite. Computational results and comparisons with existing metaheuristics show that the PHPSO algorithm outperforms the existing methods in terms of quality search and robustness for the problem considered. The improvement in solution quality is confirmed by statistical tests of significance.

A random-key encoded harmony search approach for energy-efficient production scheduling with shared resources

Science.gov (United States)

Garcia-Santiago, C. A.; Del Ser, J.; Upton, C.; Quilligan, F.; Gil-Lopez, S.; Salcedo-Sanz, S.

2015-11-01

When seeking near-optimal solutions for complex scheduling problems, meta-heuristics demonstrate good performance with affordable computational effort. This has resulted in a gravitation towards these approaches when researching industrial use-cases such as energy-efficient production planning. However, much of the previous research makes assumptions about softer constraints that affect planning strategies and about how human planners interact with the algorithm in a live production environment. This article describes a job-shop problem that focuses on minimizing energy consumption across a production facility of shared resources. The application scenario is based on real facilities made available by the Irish Center for Manufacturing Research. The formulated problem is tackled via harmony search heuristics with random keys encoding. Simulation results are compared to a genetic algorithm, a simulated annealing approach and a first-come-first-served scheduling. The superior performance obtained by the proposed scheduler paves the way towards its practical implementation over industrial production chains.

EFFICIENT MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR JOB SHOP SCHEDULING

Institute of Scientific and Technical Information of China (English)

Lei Deming; Wu Zhiming

2005-01-01

A new representation method is first presented based on priority rules. According to this method, each entry in the chromosome indicates that in the procedure of the Giffler and Thompson (GT) algorithm, the conflict occurring in the corresponding machine is resolved by the corresponding priority rule. Then crowding-measure multi-objective evolutionary algorithm (CMOEA) is designed,in which both archive maintenance and fitness assignment use crowding measure. Finally the comparisons between CMOEA and SPEA in solving 15 scheduling problems demonstrate that CMOEA is suitable to job shop scheduling.

Resource allocation in IT projects: using schedule optimization

Directory of Open Access Journals (Sweden)

Michael Chilton

2014-01-01

Full Text Available Resource allocation is the process of assigning resources to tasks throughout the life of a project. Despite sophisticated software packages devoted to keeping track of tasks, resources and resource assignments, it is often the case that project managers find some resources over-allocated and therefore unable to complete the assigned work in the allotted amount of time. Most scheduling software has provisions for leveling resources, but the techniques for doing so simply add time to the schedule and may cause delays in tasks that are critical to the project in meeting deadlines. This paper presents a software application that ensures that resources are properly balanced at the beginning of the project and eliminates the situation in which resources become over-allocated. It can be used in a multi-project environment and reused throughout the project as tasks, resource assignments and availability, and the project scope change. The application utilizes the bounded enumeration technique to formulate an optimal schedule for which both the task sequence and resource availability are taken into account. It is run on a database server to reduce the running time and make it a viable application for practitioners.

Low-Energy Real-Time OS Using Voltage Scheduling Algorithm for Variable Voltage Processors

OpenAIRE

Okuma, Takanori; Yasuura, Hiroto

2001-01-01

This paper presents a real-time OS based on $ mu $ITRON using proposed voltage scheduling algorithm for variable voltage processors which can vary supply voltage dynamically. The proposed voltage scheduling algorithms assign voltage level for each task dynamically in order to minimize energy consumption under timing constraints. Using the presented real-time OS, running tasks with low supply voltage leads to drastic energy reduction. In addition, the presented voltage scheduling algorithm is ...

Solving Flexible Job-Shop Scheduling Problem Using Gravitational Search Algorithm and Colored Petri Net

Directory of Open Access Journals (Sweden)

Behnam Barzegar

2012-01-01

Full Text Available Scheduled production system leads to avoiding stock accumulations, losses reduction, decreasing or even eliminating idol machines, and effort to better benefitting from machines for on time responding customer orders and supplying requested materials in suitable time. In flexible job-shop scheduling production systems, we could reduce time and costs by transferring and delivering operations on existing machines, that is, among NP-hard problems. The scheduling objective minimizes the maximal completion time of all the operations, which is denoted by Makespan. Different methods and algorithms have been presented for solving this problem. Having a reasonable scheduled production system has significant influence on improving effectiveness and attaining to organization goals. In this paper, new algorithm were proposed for flexible job-shop scheduling problem systems (FJSSP-GSPN that is based on gravitational search algorithm (GSA. In the proposed method, the flexible job-shop scheduling problem systems was modeled by color Petri net and CPN tool and then a scheduled job was programmed by GSA algorithm. The experimental results showed that the proposed method has reasonable performance in comparison with other algorithms.

Comparison of genetic algorithm and harmony search for generator maintenance scheduling

International Nuclear Information System (INIS)

Khan, L.; Mumtaz, S.; Khattak, A.

2012-01-01

GMS (Generator Maintenance Scheduling) ranks very high in decision making of power generation management. Generators maintenance schedule decides the time period of maintenance tasks and a reliable reserve margin is also maintained during this time period. In this paper, a comparison of GA (Genetic Algorithm) and US (Harmony Search) algorithm is presented to solve generators maintenance scheduling problem for WAPDA (Water And Power Development Authority) Pakistan. GA is a search procedure, which is used in search problems to compute exact and optimized solution. GA is considered as global search heuristic technique. HS algorithm is quite efficient, because the convergence rate of this algorithm is very fast. HS algorithm is based on the concept of music improvisation process of searching for a perfect state of harmony. The two algorithms generate feasible and optimal solutions and overcome the limitations of the conventional methods including extensive computational effort, which increases exponentially as the size of the problem increases. The proposed methods are tested, validated and compared on the WAPDA electric system. (author)

ADAPTATION OF JOHNSON SEQUENCING ALGORITHM FOR JOB SCHEDULING TO MINIMISE THE AVERAGE WAITING TIME IN CLOUD COMPUTING ENVIRONMENT

Directory of Open Access Journals (Sweden)

SOUVIK PAL

2016-09-01

Full Text Available Cloud computing is an emerging paradigm of Internet-centric business computing where Cloud Service Providers (CSPs are providing services to the customer according to their needs. The key perception behind cloud computing is on-demand sharing of resources available in the resource pool provided by CSP, which implies new emerging business model. The resources are provisioned when jobs arrive. The job scheduling and minimization of waiting time are the challenging issue in cloud computing. When a large number of jobs are requested, they have to wait for getting allocated to the servers which in turn may increase the queue length and also waiting time. This paper includes system design for implementation which is concerned with Johnson Scheduling Algorithm that provides the optimal sequence. With that sequence, service times can be obtained. The waiting time and queue length can be reduced using queuing model with multi-server and finite capacity which improves the job scheduling model.

Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines

OpenAIRE

Thiruvenkadam, T; Karthikeyani, V

2014-01-01

Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by...

Performance Evaluation of Bidding-Based Multi-Agent Scheduling Algorithms for Manufacturing Systems

Directory of Open Access Journals (Sweden)

Antonio Gordillo

2014-10-01

Full Text Available Artificial Intelligence techniques have being applied to many problems in manufacturing systems in recent years. In the specific field of manufacturing scheduling many studies have been published trying to cope with the complexity of the manufacturing environment. One of the most utilized approaches is (multi agent-based scheduling. Nevertheless, despite the large list of studies reported in this field, there is no resource or scientific study on the performance measure of this type of approach under very common and critical execution situations. This paper focuses on multi-agent systems (MAS based algorithms for task allocation, particularly in manufacturing applications. The goal is to provide a mechanism to measure the performance of agent-based scheduling approaches for manufacturing systems under key critical situations such as: dynamic environment, rescheduling, and priority change. With this mechanism it will be possible to simulate critical situations and to stress the system in order to measure the performance of a given agent-based scheduling method. The proposed mechanism is a pioneering approach for performance evaluation of bidding-based MAS approaches for manufacturing scheduling. The proposed method and evaluation methodology can be used to run tests in different manufacturing floors since it is independent of the workshop configuration. Moreover, the evaluation results presented in this paper show the key factors and scenarios that most affect the market-like MAS approaches for manufacturing scheduling.

Algorithms for Scheduling and Network Problems

Science.gov (United States)

1991-09-01

time. We already know, by Lemma 2.2.1, that WOPT = O(log( mpU )), so if we could solve this integer program optimally we would be done. However, the...Folydirat, 15:177-191, 1982. [6] I.S. Belov and Ya. N. Stolin. An algorithm in a single path operations scheduling problem. In Mathematical Economics and

Time-constrained project scheduling with adjacent resources

NARCIS (Netherlands)

Hurink, Johann L.; Kok, A.L.; Paulus, J.J.; Schutten, Johannes M.J.

We develop a decomposition method for the Time-Constrained Project Scheduling Problem (TCPSP) with adjacent resources. For adjacent resources the resource units are ordered and the units assigned to a job have to be adjacent. On top of that, adjacent resources are not required by single jobs, but by

Time-constrained project scheduling with adjacent resources

NARCIS (Netherlands)

Hurink, Johann L.; Kok, A.L.; Paulus, J.J.; Schutten, Johannes M.J.

2008-01-01

We develop a decomposition method for the Time-Constrained Project Scheduling Problem (TCPSP) with Adjacent Resources. For adjacent resources the resource units are ordered and the units assigned to a job have to be adjacent. On top of that, adjacent resources are not required by single jobs, but by

Adaptive resource allocation for efficient patient scheduling

NARCIS (Netherlands)

Vermeulen, Ivan B.; Bohte, Sander M.; Elkhuizen, Sylvia G.; Lameris, Han; Bakker, Piet J. M.; La Poutré, Han

2009-01-01

Efficient scheduling of patient appointments on expensive resources is a complex and dynamic task. A resource is typically used by several patient groups. To service these groups, resource capacity is often allocated per group, explicitly or implicitly. Importantly, due to fluctuations in demand,

A Generalized Ant Colony Algorithm for Job一shop Scheduling Problem

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ZHANG Hong-Guo

2017-02-01

Full Text Available Aiming at the problem of ant colony algorithm for solving Job一shop scheduling problem. Considering the complexity of the algorithm that uses disjunctive graph to describe the relationship between workpiece processing. To solve the problem of optimal solution，a generalized ant colony algorithm is proposed. Under the premise of considering constrained relationship between equipment and process，the pheromone update mechanism is applied to solve Job-shop scheduling problem，so as to improve the quality of the solution. In order to improve the search efficiency，according to the state transition rules of ant colony algorithm，this paper makes a detailed study on the selection and improvement of the parameters in the algorithm，and designs the pheromone update strategy. Experimental results show that a generalized ant colony algorithm is more feasible and more effective. Compared with other algorithms in the literature，the results prove that the algorithm improves in computing the optimal solution and convergence speed.

Day-ahead resource scheduling including demand response for electric vehicles

DEFF Research Database (Denmark)

Soares, Joao; Morais, Hugo; Sousa, Tiago

2014-01-01

Summary form only given. The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering...

An Adaptive Power Efficient Packet Scheduling Algorithm for Wimax Networks

OpenAIRE

R Murali Prasad; P. Satish Kumar

2010-01-01

Admission control schemes and scheduling algorithms are designed to offer QoS services in 802.16/802.16e networks and a number of studies have investigated these issues. But the channel condition and priority of traffic classes are very rarely considered in the existing scheduling algorithms. Although a number of energy saving mechanisms have been proposed for the IEEE 802.16e, to minimize the power consumption of IEEE 802.16e mobile stations with multiple real-time connections has not yet be...

A new distributed systems scheduling algorithm: a swarm intelligence approach

Science.gov (United States)

Haghi Kashani, Mostafa; Sarvizadeh, Raheleh; Jameii, Mahdi

2011-12-01

The scheduling problem in distributed systems is known as an NP-complete problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The task scheduling is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of distributed systems scheduling. With regard to load balancing efficiently, Artificial Bee Colony (ABC) has been applied as local search in the proposed memetic algorithm. The proposed method has been compared to existing memetic-Based approach in which Learning Automata method has been used as local search. The results demonstrated that the proposed method outperform the above mentioned method in terms of communication cost.

Scheduling of resource-constrained projects

CERN Document Server

Klein, Robert

2000-01-01

Project management has become a widespread instrument enabling organizations to efficiently master the challenges of steadily shortening product life cycles, global markets and decreasing profit margins. With projects increasing in size and complexity, their planning and control represents one of the most crucial management tasks. This is especially true for scheduling, which is concerned with establishing execution dates for the sub-activities to be performed in order to complete the project. The ability to manage projects where resources must be allocated between concurrent projects or even sub-activities of a single project requires the use of commercial project management software packages. However, the results yielded by the solution procedures included are often rather unsatisfactory. Scheduling of Resource-Constrained Projects develops more efficient procedures, which can easily be integrated into software packages by incorporated programming languages, and thus should be of great interest for practiti...

IMPACT OF BUFFER SIZE ON PQRS AND D-PQRS SCHEDULING ALGORITHMS

OpenAIRE

N. Narayanan Prasanth; Kannan Balasubramanian; R. Chithra Devi

2016-01-01

Most of the internet applications required high speed internet connectivity. Crosspoint Buffered Switches are widely used switching architectures and designing a scheduling algorithm is a major challenge. PQRS and D-PQRS are the two most successful schedulers used in Crosspoint Buffered Switches under unicast traffic. In this paper, we analysed the performance of PQRS and DPQRS algorithms by varying the crosspoint buffer size. Simulation result shows the delay performance of the switch increa...

Genetic algorithm and neural network hybrid approach for job-shop scheduling

OpenAIRE

Zhao, Kai; Yang, Shengxiang; Wang, Dingwei

1998-01-01

Copyright @ 1998 ACTA Press This paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate for searching optimal solutions, CSANN is used to obtain feasible solutions during the iteration of genetic algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop scheduling with respect to the quality of solutions and ...

Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Lvjiang Yin

2016-12-01

Full Text Available Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T, cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II, Strength Pareto Evolutionary Algorithm 2 (SPEA2, Multiobjective Particle Swarm Optimization (OMOPSO, and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D. Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.

A novel discrete PSO algorithm for solving job shop scheduling problem to minimize makespan

Science.gov (United States)

Rameshkumar, K.; Rajendran, C.

2018-02-01

In this work, a discrete version of PSO algorithm is proposed to minimize the makespan of a job-shop. A novel schedule builder has been utilized to generate active schedules. The discrete PSO is tested using well known benchmark problems available in the literature. The solution produced by the proposed algorithms is compared with best known solution published in the literature and also compared with hybrid particle swarm algorithm and variable neighborhood search PSO algorithm. The solution construction methodology adopted in this study is found to be effective in producing good quality solutions for the various benchmark job-shop scheduling problems.

Project Robust Scheduling Based on the Scattered Buffer Technology

Directory of Open Access Journals (Sweden)

Nansheng Pang

2018-04-01

Full Text Available The research object in this paper is the sub network formed by the predecessor’s affect on the solution activity. This paper is to study three types of influencing factors from the predecessors that lead to the delay of starting time of the solution activity on the longest path, and to analyze the influence degree on the delay of the solution activity’s starting time from different types of factors. On this basis, through the comprehensive analysis of various factors that influence the solution activity, this paper proposes a metric that is used to evaluate the solution robustness of the project scheduling, and this metric is taken as the optimization goal. This paper also adopts the iterative process to design a scattered buffer heuristics algorithm based on the robust scheduling of the time buffer. At the same time, the resource flow network is introduced in this algorithm, using the tabu search algorithm to solve baseline scheduling. For the generation of resource flow network in the baseline scheduling, this algorithm designs a resource allocation algorithm with the maximum use of the precedence relations. Finally, the algorithm proposed in this paper and some other algorithms in previous literature are taken into the simulation experiment; under the comparative analysis, the experimental results show that the algorithm proposed in this paper is reasonable and feasible.

Cooperated Bayesian algorithm for distributed scheduling problem

Institute of Scientific and Technical Information of China (English)

QIANG Lei; XIAO Tian-yuan

2006-01-01

This paper presents a new distributed Bayesian optimization algorithm (BOA) to overcome the efficiency problem when solving NP scheduling problems.The proposed approach integrates BOA into the co-evolutionary schema,which builds up a concurrent computing environment.A new search strategy is also introduced for local optimization process.It integrates the reinforcement learning(RL) mechanism into the BOA search processes,and then uses the mixed probability information from BOA (post-probability) and RL (pre-probability) to enhance the cooperation between different local controllers,which improves the optimization ability of the algorithm.The experiment shows that the new algorithm does better in both optimization (2.2%) and convergence (11.7%),compared with classic BOA.

Evaluation of Selected Resource Allocation and Scheduling Methods in Heterogeneous Many-Core Processors and Graphics Processing Units

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Ciznicki Milosz

2014-12-01

Full Text Available Heterogeneous many-core computing resources are increasingly popular among users due to their improved performance over homogeneous systems. Many developers have realized that heterogeneous systems, e.g. a combination of a shared memory multi-core CPU machine with massively parallel Graphics Processing Units (GPUs, can provide significant performance opportunities to a wide range of applications. However, the best overall performance can only be achieved if application tasks are efficiently assigned to different types of processor units in time taking into account their specific resource requirements. Additionally, one should note that available heterogeneous resources have been designed as general purpose units, however, with many built-in features accelerating specific application operations. In other words, the same algorithm or application functionality can be implemented as a different task for CPU or GPU. Nevertheless, from the perspective of various evaluation criteria, e.g. the total execution time or energy consumption, we may observe completely different results. Therefore, as tasks can be scheduled and managed in many alternative ways on both many-core CPUs or GPUs and consequently have a huge impact on the overall computing resources performance, there are needs for new and improved resource management techniques. In this paper we discuss results achieved during experimental performance studies of selected task scheduling methods in heterogeneous computing systems. Additionally, we present a new architecture for resource allocation and task scheduling library which provides a generic application programming interface at the operating system level for improving scheduling polices taking into account a diversity of tasks and heterogeneous computing resources characteristics.

Exact and Heuristic Algorithms for Runway Scheduling

Science.gov (United States)

Malik, Waqar A.; Jung, Yoon C.

2016-01-01

This paper explores the Single Runway Scheduling (SRS) problem with arrivals, departures, and crossing aircraft on the airport surface. Constraints for wake vortex separations, departure area navigation separations and departure time window restrictions are explicitly considered. The main objective of this research is to develop exact and heuristic based algorithms that can be used in real-time decision support tools for Air Traffic Control Tower (ATCT) controllers. The paper provides a multi-objective dynamic programming (DP) based algorithm that finds the exact solution to the SRS problem, but may prove unusable for application in real-time environment due to large computation times for moderate sized problems. We next propose a second algorithm that uses heuristics to restrict the search space for the DP based algorithm. A third algorithm based on a combination of insertion and local search (ILS) heuristics is then presented. Simulation conducted for the east side of Dallas/Fort Worth International Airport allows comparison of the three proposed algorithms and indicates that the ILS algorithm performs favorably in its ability to find efficient solutions and its computation times.

Performance Evaluation of New Joint EDF-RM Scheduling Algorithm for Real Time Distributed System

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Rashmi Sharma

2014-01-01

Full Text Available In Real Time System, the achievement of deadline is the main target of every scheduling algorithm. Earliest Deadline First (EDF, Rate Monotonic (RM, and least Laxity First are some renowned algorithms that work well in their own context. As we know, there is a very common problem Domino's effect in EDF that is generated due to overloading condition (EDF is not working well in overloading situation. Similarly, performance of RM is degraded in underloading condition. We can say that both algorithms are complements of each other. Deadline missing in both events happens because of their utilization bounding strategy. Therefore, in this paper we are proposing a new scheduling algorithm that carries through the drawback of both existing algorithms. Joint EDF-RM scheduling algorithm is implemented in global scheduler that permits task migration mechanism in between processors in the system. In order to check the improved behavior of proposed algorithm we perform simulation. Results are achieved and evaluated in terms of Success Ratio (SR, Average CPU Utilization (ECU, Failure Ratio (FR, and Maximum Tardiness parameters. In the end, the results are compared with the existing (EDF, RM, and D_R_EDF algorithms. It has been shown that the proposed algorithm performs better during overloading condition as well in underloading condition.

Weighted-Bit-Flipping-Based Sequential Scheduling Decoding Algorithms for LDPC Codes

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Qing Zhu

2013-01-01

Full Text Available Low-density parity-check (LDPC codes can be applied in a lot of different scenarios such as video broadcasting and satellite communications. LDPC codes are commonly decoded by an iterative algorithm called belief propagation (BP over the corresponding Tanner graph. The original BP updates all the variable-nodes simultaneously, followed by all the check-nodes simultaneously as well. We propose a sequential scheduling algorithm based on weighted bit-flipping (WBF algorithm for the sake of improving the convergence speed. Notoriously, WBF is a low-complexity and simple algorithm. We combine it with BP to obtain advantages of these two algorithms. Flipping function used in WBF is borrowed to determine the priority of scheduling. Simulation results show that it can provide a good tradeoff between FER performance and computation complexity for short-length LDPC codes.

Green cloud environment by using robust planning algorithm

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Jyoti Thaman

2017-11-01

Full Text Available Cloud computing provided a framework for seamless access to resources through network. Access to resources is quantified through SLA between service providers and users. Service provider tries to best exploit their resources and reduce idle times of the resources. Growing energy concerns further makes the life of service providers miserable. User’s requests are served by allocating users tasks to resources in Clouds and Grid environment through scheduling algorithms and planning algorithms. With only few Planning algorithms in existence rarely planning and scheduling algorithms are differentiated. This paper proposes a robust hybrid planning algorithm, Robust Heterogeneous-Earliest-Finish-Time (RHEFT for binding tasks to VMs. The allocation of tasks to VMs is based on a novel task matching algorithm called Interior Scheduling. The consistent performance of proposed RHEFT algorithm is compared with Heterogeneous-Earliest-Finish-Time (HEFT and Distributed HEFT (DHEFT for various parameters like utilization ratio, makespan, Speed-up and Energy Consumption. RHEFT’s consistent performance against HEFT and DHEFT has established the robustness of the hybrid planning algorithm through rigorous simulations.

Hypergraph+: An Improved Hypergraph-Based Task-Scheduling Algorithm for Massive Spatial Data Processing on Master-Slave Platforms

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Bo Cheng

2016-08-01

Full Text Available Spatial data processing often requires massive datasets, and the task/data scheduling efficiency of these applications has an impact on the overall processing performance. Among the existing scheduling strategies, hypergraph-based algorithms capture the data sharing pattern in a global way and significantly reduce total communication volume. Due to heterogeneous processing platforms, however, single hypergraph partitioning for later scheduling may be not optimal. Moreover, these scheduling algorithms neglect the overlap between task execution and data transfer that could further decrease execution time. In order to address these problems, an extended hypergraph-based task-scheduling algorithm, named Hypergraph+, is proposed for massive spatial data processing. Hypergraph+ improves upon current hypergraph scheduling algorithms in two ways: (1 It takes platform heterogeneity into consideration offering a metric function to evaluate the partitioning quality in order to derive the best task/file schedule; and (2 It can maximize the overlap between communication and computation. The GridSim toolkit was used to evaluate Hypergraph+ in an IDW spatial interpolation application on heterogeneous master-slave platforms. Experiments illustrate that the proposed Hypergraph+ algorithm achieves on average a 43% smaller makespan than the original hypergraph scheduling algorithm but still preserves high scheduling efficiency.

A multi-group and preemptable scheduling of cloud resource based on HTCondor

Science.gov (United States)

Jiang, Xiaowei; Zou, Jiaheng; Cheng, Yaodong; Shi, Jingyan

2017-10-01

Due to the features of virtual machine-flexibility, easy controlling and various system environments, more and more fields utilize the virtualization technology to construct the distributed system with the virtual resources, also including high energy physics. This paper introduce a method used in high energy physics that supports multiple resource group and preemptable cloud resource scheduling, combining virtual machine with HTCondor (a batch system). It makes resource controlling more flexible and more efficient and makes resource scheduling independent of job scheduling. Firstly, the resources belong to different experiment-groups, and the type of user-groups mapping to resource-groups(same as experiment-group) is one-to-one or many-to-one. In order to make the confused group simply to be managed, we designed the permission controlling component to ensure that the different resource-groups can get the suitable jobs. Secondly, for the purpose of elastically allocating resources for suitable resource-group, it is necessary to schedule resources like scheduling jobs. So this paper designs the cloud resource scheduling to maintain a resource queue and allocate an appropriate amount of virtual resources to the request resource-group. Thirdly, in some kind of situations, because of the resource occupied for a long time, resources need to be preempted. This paper adds the preemption function for the resource scheduling that implement resource preemption based on the group priority. Additionally, the way to preempting is soft that when virtual resources are preempted, jobs will not be killed but also be held and rematched later. It is implemented with the help of HTCondor, storing the held job information in scheduler, releasing the job to idle status and doing second matcher. In IHEP (institute of high energy physics), we have built a batch system based on HTCondor with a virtual resources pool based on Openstack. And this paper will show some cases of experiment JUNO

Unit-time scheduling problems with time dependent resources

NARCIS (Netherlands)

Tautenhahn, T.; Woeginger, G.

1997-01-01

We investigate the computational complexity of scheduling problems, where the operations consume certain amounts of renewable resources which are available in time-dependent quantities. In particular, we consider unit-time open shop problems and unit-time scheduling problems with identical parallel

A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem

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Jian Gao

2011-08-01

Full Text Available Distributed Permutation Flowshop Scheduling Problem (DPFSP is a newly proposed scheduling problem, which is a generalization of classical permutation flow shop scheduling problem. The DPFSP is NP-hard in general. It is in the early stages of studies on algorithms for solving this problem. In this paper, we propose a GA-based algorithm, denoted by GA_LS, for solving this problem with objective to minimize the maximum completion time. In the proposed GA_LS, crossover and mutation operators are designed to make it suitable for the representation of DPFSP solutions, where the set of partial job sequences is employed. Furthermore, GA_LS utilizes an efficient local search method to explore neighboring solutions. The local search method uses three proposed rules that move jobs within a factory or between two factories. Intensive experiments on the benchmark instances, extended from Taillard instances, are carried out. The results indicate that the proposed hybrid genetic algorithm can obtain better solutions than all the existing algorithms for the DPFSP, since it obtains better relative percentage deviation and differences of the results are also statistically significant. It is also seen that best-known solutions for most instances are updated by our algorithm. Moreover, we also show the efficiency of the GA_LS by comparing with similar genetic algorithms with the existing local search methods.

Resource Allocation and Outpatient Appointment Scheduling Using Simulation Optimization

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Carrie Ka Yuk Lin

2017-01-01

Full Text Available This paper studies the real-life problems of outpatient clinics having the multiple objectives of minimizing resource overtime, patient waiting time, and waiting area congestion. In the clinic, there are several patient classes, each of which follows different treatment procedure flow paths through a multiphase and multiserver queuing system with scarce staff and limited space. We incorporate the stochastic factors for the probabilities of the patients being diverted into different flow paths, patient punctuality, arrival times, procedure duration, and the number of accompanied visitors. We present a novel two-stage simulation-based heuristic algorithm to assess various tactical and operational decisions for optimizing the multiple objectives. In stage I, we search for a resource allocation plan, and in stage II, we determine a block appointment schedule by patient class and a service discipline for the daily operational level. We also explore the effects of the separate strategies and their integration to identify the best possible combination. The computational experiments are designed on the basis of data from a study of an ophthalmology clinic in a public hospital. Results show that our approach significantly mitigates the undesirable outcomes by integrating the strategies and increasing the resource flexibility at the bottleneck procedures without adding resources.

Effective Task Scheduling and IP Mapping Algorithm for Heterogeneous NoC-Based MPSoC

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Peng-Fei Yang

2014-01-01

Full Text Available Quality of task scheduling is critical to define the network communication efficiency and the performance of the entire NoC- (Network-on-Chip- based MPSoC (multiprocessor System-on-Chip. In this paper, the NoC-based MPSoC design process is favorably divided into two steps, that is, scheduling subtasks to processing elements (PEs of appropriate type and quantity and then mapping these PEs onto the switching nodes of NoC topology. When the task model is improved so that it reflects better the real intertask relations, optimized particle swarm optimization (PSO is utilized to achieve the first step with expected less task running and transfer cost as well as the least task execution time. By referring to the topology of NoC and the resultant communication diagram of the first step, the second step is done with the minimal expected network transmission delay as well as less resource consumption and even power consumption. The comparative experiments have shown the preferable resource and power consumption of the algorithm when it is actually adopted in a system design.

Artificial immune algorithm for multi-depot vehicle scheduling problems

Science.gov (United States)

Wu, Zhongyi; Wang, Donggen; Xia, Linyuan; Chen, Xiaoling

2008-10-01

In the fast-developing logistics and supply chain management fields, one of the key problems in the decision support system is that how to arrange, for a lot of customers and suppliers, the supplier-to-customer assignment and produce a detailed supply schedule under a set of constraints. Solutions to the multi-depot vehicle scheduling problems (MDVRP) help in solving this problem in case of transportation applications. The objective of the MDVSP is to minimize the total distance covered by all vehicles, which can be considered as delivery costs or time consumption. The MDVSP is one of nondeterministic polynomial-time hard (NP-hard) problem which cannot be solved to optimality within polynomial bounded computational time. Many different approaches have been developed to tackle MDVSP, such as exact algorithm (EA), one-stage approach (OSA), two-phase heuristic method (TPHM), tabu search algorithm (TSA), genetic algorithm (GA) and hierarchical multiplex structure (HIMS). Most of the methods mentioned above are time consuming and have high risk to result in local optimum. In this paper, a new search algorithm is proposed to solve MDVSP based on Artificial Immune Systems (AIS), which are inspirited by vertebrate immune systems. The proposed AIS algorithm is tested with 30 customers and 6 vehicles located in 3 depots. Experimental results show that the artificial immune system algorithm is an effective and efficient method for solving MDVSP problems.

Dynamic Scheduling for Cloud Reliability using Transportation Problem

OpenAIRE

P. Balasubramanie; S. K. Senthil Kumar

2012-01-01

Problem statement: Cloud is purely a dynamic environment and the existing task scheduling algorithms are mostly static and considered various parameters like time, cost, make span, speed, scalability, throughput, resource utilization, scheduling success rate and so on. Available scheduling algorithms are mostly heuristic in nature and more complex, time consuming and does not consider reliability and availability of the cloud computing environment. Therefore there is a need to implement a sch...

Flow shop scheduling algorithm to optimize warehouse activities

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P. Centobelli

2016-01-01

Full Text Available Successful flow-shop scheduling outlines a more rapid and efficient process of order fulfilment in warehouse activities. Indeed the way and the speed of order processing and, in particular, the operations concerning materials handling between the upper stocking area and a lower forward picking one must be optimized. The two activities, drops and pickings, have considerable impact on important performance parameters for Supply Chain wholesaler companies. In this paper, a new flow shop scheduling algorithm is formulated in order to process a greater number of orders by replacing the FIFO logic for the drops activities of a wholesaler company on a daily basis. The System Dynamics modelling and simulation have been used to simulate the actual scenario and the output solutions. Finally, a t-Student test validates the modelled algorithm, granting that it can be used for all wholesalers based on drop and picking activities.

A New Evolutionary Algorithm Based on Bacterial Evolution and Its Application for Scheduling A Flexible Manufacturing System

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Chandramouli Anandaraman

2012-01-01

Full Text Available A new evolutionary computation algorithm, Superbug algorithm, which simulates evolution of bacteria in a culture, is proposed. The algorithm is developed for solving large scale optimization problems such as scheduling, transportation and assignment problems. In this work, the algorithm optimizes machine schedules in a Flexible Manufacturing System (FMS by minimizing makespan. The FMS comprises of four machines and two identical Automated Guided Vehicles (AGVs. AGVs are used for carrying jobs between the Load/Unload (L/U station and the machines. Experimental results indicate the efficiency of the proposed algorithm in its optimization performance in scheduling is noticeably superior to other evolutionary algorithms when compared to the best results reported in the literature for FMS Scheduling.

Distributed Scheduling in Time Dependent Environments: Algorithms and Analysis

OpenAIRE

Shmuel, Ori; Cohen, Asaf; Gurewitz, Omer

2017-01-01

Consider the problem of a multiple access channel in a time dependent environment with a large number of users. In such a system, mostly due to practical constraints (e.g., decoding complexity), not all users can be scheduled together, and usually only one user may transmit at any given time. Assuming a distributed, opportunistic scheduling algorithm, we analyse the system's properties, such as delay, QoS and capacity scaling laws. Specifically, we start with analyzing the performance while \\...

Multiobjective genetic algorithm approaches to project scheduling under risk

OpenAIRE

Kılıç, Murat; Kilic, Murat

2003-01-01

In this thesis, project scheduling under risk is chosen as the topic of research. Project scheduling under risk is defined as a biobjective decision problem and is formulated as a 0-1 integer mathematical programming model. In this biobjective formulation, one of the objectives is taken as the expected makespan minimization and the other is taken as the expected cost minimization. As the solution approach to this biobjective formulation genetic algorithm (GA) is chosen. After carefully invest...

A Scheduling Algorithm for Cloud Computing System Based on the Driver of Dynamic Essential Path.

Science.gov (United States)

Xie, Zhiqiang; Shao, Xia; Xin, Yu

2016-01-01

To solve the problem of task scheduling in the cloud computing system, this paper proposes a scheduling algorithm for cloud computing based on the driver of dynamic essential path (DDEP). This algorithm applies a predecessor-task layer priority strategy to solve the problem of constraint relations among task nodes. The strategy assigns different priority values to every task node based on the scheduling order of task node as affected by the constraint relations among task nodes, and the task node list is generated by the different priority value. To address the scheduling order problem in which task nodes have the same priority value, the dynamic essential long path strategy is proposed. This strategy computes the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. The task node that has the longest dynamic essential path is scheduled first as the completion time of task graph is indirectly influenced by the finishing time of task nodes in the longest dynamic essential path. Finally, we demonstrate the proposed algorithm via simulation experiments using Matlab tools. The experimental results indicate that the proposed algorithm can effectively reduce the task Makespan in most cases and meet a high quality performance objective.

Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing

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Ahmad M. Manasrah

2018-01-01

Full Text Available Cloud computing environment provides several on-demand services and resource sharing for clients. Business processes are managed using the workflow technology over the cloud, which represents one of the challenges in using the resources in an efficient manner due to the dependencies between the tasks. In this paper, a Hybrid GA-PSO algorithm is proposed to allocate tasks to the resources efficiently. The Hybrid GA-PSO algorithm aims to reduce the makespan and the cost and balance the load of the dependent tasks over the heterogonous resources in cloud computing environments. The experiment results show that the GA-PSO algorithm decreases the total execution time of the workflow tasks, in comparison with GA, PSO, HSGA, WSGA, and MTCT algorithms. Furthermore, it reduces the execution cost. In addition, it improves the load balancing of the workflow application over the available resources. Finally, the obtained results also proved that the proposed algorithm converges to optimal solutions faster and with higher quality compared to other algorithms.

A recursive economic dispatch algorithm for assessing the cost of thermal generator schedules

International Nuclear Information System (INIS)

Wong, K.P.; Doan, K.

1992-01-01

This paper develops an efficient, recursive algorithm for determining the economic power dispatch of thermal generators within the unit commitment environment. A method for incorporating the operation limits of all on-line generators and limits due to ramping generators is developed in the paper. The developed algorithm is amenable for computer implementation using the artificial intelligence programming language, Prolog. The performance of the developed algorithm is demonstrated through its application to evaluate the costs of dispatching 13 thermal generators within a generator schedule in a 24-hour schedule horizon

Integrated Job Scheduling and Network Routing

DEFF Research Database (Denmark)

Gamst, Mette; Pisinger, David

2013-01-01

We consider an integrated job scheduling and network routing problem which appears in Grid Computing and production planning. The problem is to schedule a number of jobs at a finite set of machines, such that the overall profit of the executed jobs is maximized. Each job demands a number of resou...... indicate that the algorithm can be used as an actual scheduling algorithm in the Grid or as a tool for analyzing Grid performance when adding extra machines or jobs. © 2012 Wiley Periodicals, Inc.......We consider an integrated job scheduling and network routing problem which appears in Grid Computing and production planning. The problem is to schedule a number of jobs at a finite set of machines, such that the overall profit of the executed jobs is maximized. Each job demands a number...... of resources which must be sent to the executing machine through a network with limited capacity. A job cannot start before all of its resources have arrived at the machine. The scheduling problem is formulated as a Mixed Integer Program (MIP) and proved to be NP-hard. An exact solution approach using Dantzig...

Real-time Energy Resource Scheduling considering a Real Portuguese Scenario

DEFF Research Database (Denmark)

Silva, Marco; Sousa, Tiago; Morais, Hugo

2014-01-01

The development in power systems and the introduction of decentralized gen eration and Electric Vehicles (EVs), both connected to distribution networks, represents a major challenge in the planning and operation issues. This new paradigm requires a new energy resources management approach which...... scheduling in smart grids, considering day - ahead, hour - ahead and real - time scheduling. The case study considers a 33 - bus distribution network with high penetration of distributed energy resources . The wind generation profile is base d o n a rea l Portuguese wind farm . Four scenarios are presented...... taking into account 0, 1, 2 and 5 periods (hours or minutes) ahead of the scheduling period in the hour - ahead and real - time scheduling...

Scheduling for the National Hockey League Using a Multi-objective Evolutionary Algorithm

Science.gov (United States)

Craig, Sam; While, Lyndon; Barone, Luigi

We describe a multi-objective evolutionary algorithm that derives schedules for the National Hockey League according to three objectives: minimising the teams' total travel, promoting equity in rest time between games, and minimising long streaks of home or away games. Experiments show that the system is able to derive schedules that beat the 2008-9 NHL schedule in all objectives simultaneously, and that it returns a set of schedules that offer a range of trade-offs across the objectives.

Effects of resource management regimes on project schedule

NARCIS (Netherlands)

Al-Jibouri, Saad H.S.

2002-01-01

This paper deals with the problem of resource scheduling within given resource constraints on a sectionalised construction project. A computer model has been developed which realistically simulates the progress of projects. A project has been selected and divided into sections whereby section

A multipopulation PSO based memetic algorithm for permutation flow shop scheduling.

Science.gov (United States)

Liu, Ruochen; Ma, Chenlin; Ma, Wenping; Li, Yangyang

2013-01-01

The permutation flow shop scheduling problem (PFSSP) is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS) and individual improvement scheme (IIS). Then, the best particle of each subpopulation is selected to construct a probabilistic model by using estimation of distribution algorithm (EDA) and three particles are sampled from the probabilistic model to update the worst individual in each subpopulation. The best particle in the entire particle swarm is used to update the global optimal solution. The proposed MPSOMA is compared with two recently proposed algorithms, namely, PSO based memetic algorithm (PSOMA) and hybrid particle swarm optimization with estimation of distribution algorithm (PSOEDA), on 29 well-known PFFSPs taken from OR-library, and the experimental results show that it is an effective approach for the PFFSP.

A Multipopulation PSO Based Memetic Algorithm for Permutation Flow Shop Scheduling

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Ruochen Liu

2013-01-01

Full Text Available The permutation flow shop scheduling problem (PFSSP is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO based memetic algorithm (MPSOMA is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS and individual improvement scheme (IIS. Then, the best particle of each subpopulation is selected to construct a probabilistic model by using estimation of distribution algorithm (EDA and three particles are sampled from the probabilistic model to update the worst individual in each subpopulation. The best particle in the entire particle swarm is used to update the global optimal solution. The proposed MPSOMA is compared with two recently proposed algorithms, namely, PSO based memetic algorithm (PSOMA and hybrid particle swarm optimization with estimation of distribution algorithm (PSOEDA, on 29 well-known PFFSPs taken from OR-library, and the experimental results show that it is an effective approach for the PFFSP.

A Scheduling Algorithm for Time Bounded Delivery of Packets on the Internet

NARCIS (Netherlands)

I. Vaishnavi (Ishan)

2008-01-01

htmlabstractThis thesis aims to provide a better scheduling algorithm for Real-Time delivery of packets. A number of emerging applications such as VoIP, Tele-immersive environments, distributed media viewing and distributed gaming require real-time delivery of packets. Currently the scheduling

An optimal algorithm for preemptive on-line scheduling

NARCIS (Netherlands)

Chen, B.; Vliet, van A.; Woeginger, G.J.

1995-01-01

We investigate the problem of on-line scheduling jobs on m identical parallel machines where preemption is allowed. The goal is to minimize the makespan. We derive an approximation algorithm with worst-case guarantee mm/(mm - (m - 1)m) for every m 2, which increasingly tends to e/(e - 1) ˜ 1.58 as m

An Online Scheduling Algorithm with Advance Reservation for Large-Scale Data Transfers

Energy Technology Data Exchange (ETDEWEB)

Balman, Mehmet; Kosar, Tevfik

2010-05-20

Scientific applications and experimental facilities generate massive data sets that need to be transferred to remote collaborating sites for sharing, processing, and long term storage. In order to support increasingly data-intensive science, next generation research networks have been deployed to provide high-speed on-demand data access between collaborating institutions. In this paper, we present a practical model for online data scheduling in which data movement operations are scheduled in advance for end-to-end high performance transfers. In our model, data scheduler interacts with reservation managers and data transfer nodes in order to reserve available bandwidth to guarantee completion of jobs that are accepted and confirmed to satisfy preferred time constraint given by the user. Our methodology improves current systems by allowing researchers and higher level meta-schedulers to use data placement as a service where theycan plan ahead and reserve the scheduler time in advance for their data movement operations. We have implemented our algorithm and examined possible techniques for incorporation into current reservation frameworks. Performance measurements confirm that the proposed algorithm is efficient and scalable.

Preventive maintenance scheduling by variable dimension evolutionary algorithms

International Nuclear Information System (INIS)

Limbourg, Philipp; Kochs, Hans-Dieter

2006-01-01

Black box optimization strategies have been proven to be useful tools for solving complex maintenance optimization problems. There has been a considerable amount of research on the right choice of optimization strategies for finding optimal preventive maintenance schedules. Much less attention is turned to the representation of the schedule to the algorithm. Either the search space is represented as a binary string leading to highly complex combinatorial problem or maintenance operations are defined by regular intervals which may restrict the search space to suboptimal solutions. An adequate representation however is vitally important for result quality. This work presents several nonstandard input representations and compares them to the standard binary representation. An evolutionary algorithm with extensions to handle variable length genomes is used for the comparison. The results demonstrate that two new representations perform better than the binary representation scheme. A second analysis shows that the performance may be even more increased using modified genetic operators. Thus, the choice of alternative representations leads to better results in the same amount of time and without any loss of accuracy

Multiresource allocation and scheduling for periodic soft real-time applications

Science.gov (United States)

Gopalan, Kartik; Chiueh, Tzi-cker

2001-12-01

Real-time applications that utilize multiple system resources, such as CPU, disks, and network links, require coordinated scheduling of these resources in order to meet their end-to-end performance requirements. Most state-of-the-art operating systems support independent resource allocation and deadline-driven scheduling but lack coordination among multiple heterogeneous resources. This paper describes the design and implementation of an Integrated Real-time Resource Scheduler (IRS) that performs coordinated allocation and scheduling of multiple heterogeneous resources on the same machine for periodic soft real-time application. The principal feature of IRS is a heuristic multi-resource allocation algorithm that reserves multiple resources for real-time applications in a manner that can maximize the number of applications admitted into the system in the long run. At run-time, a global scheduler dispatches the tasks of the soft real-time application to individual resource schedulers according to the precedence constraints between tasks. The individual resource schedulers, which could be any deadline based schedulers, can make scheduling decisions locally and yet collectively satisfy a real-time application's performance requirements. The tightness of overall timing guarantees is ultimately determined by the properties of individual resource schedulers. However, IRS maximizes overall system resource utilization efficiency by coordinating deadline assignment across multiple tasks in a soft real-time application.

Washington's marine oil spill compensation schedule - simplified resource damage assessment

International Nuclear Information System (INIS)

Geselbracht, L.; Logan, R.

1993-01-01

The Washington State Preassessment Screening and Oil Spill Compensation Schedule Rule (Chapter 173-183 Washington Administrative Code), which simplifies natural resource damage assessment for many oil spill cases, became effective in May 1992. The approach described in the rule incorporates a number of preconstructed rankings that rate environmental sensitivity and the propensity of spilled oil to cause environmental harm. The rule also provides guidance regarding how damages calculated under the schedule should be reduced to take into account actions taken by the responsible party that reduce environmental injury. To apply the compensation schedule to marine estuarine spills, the resource trustees need only collect a limited amount of information such as type of product spilled, number of gallons spilled, compensation schedule subregions the spill entered, season of greatest spill impact, percent coverage of habitats affected by the spill, and actions taken by the responsible party. The result of adding a simplified tool to the existing assortment of damage assessment approaches is that resource trustees will now be able to assess damages for most oil spill cases and shift more effort than was possible in the past to resource restoration

Nonmyopic Sensor Scheduling and its Efficient Implementation for Target Tracking Applications

Directory of Open Access Journals (Sweden)

Morrell Darryl

2006-01-01

Full Text Available We propose two nonmyopic sensor scheduling algorithms for target tracking applications. We consider a scenario where a bearing-only sensor is constrained to move in a finite number of directions to track a target in a two-dimensional plane. Both algorithms provide the best sensor sequence by minimizing a predicted expected scheduler cost over a finite time-horizon. The first algorithm approximately computes the scheduler costs based on the predicted covariance matrix of the tracker error. The second algorithm uses the unscented transform in conjunction with a particle filter to approximate covariance-based costs or information-theoretic costs. We also propose the use of two branch-and-bound-based optimal pruning algorithms for efficient implementation of the scheduling algorithms. We design the first pruning algorithm by combining branch-and-bound with a breadth-first search and a greedy-search; the second pruning algorithm combines branch-and-bound with a uniform-cost search. Simulation results demonstrate the advantage of nonmyopic scheduling over myopic scheduling and the significant savings in computational and memory resources when using the pruning algorithms.

Preemptive Online Scheduling: Optimal Algorithms for All Speeds

Czech Academy of Sciences Publication Activity Database

Ebenlendr, Tomáš; Jawor, W.; Sgall, Jiří

2009-01-01

Roč. 53, č. 4 (2009), s. 504-522 ISSN 0178-4617 R&D Projects: GA MŠk(CZ) 1M0545; GA ČR GA201/05/0124; GA AV ČR IAA1019401 Institutional research plan: CEZ:AV0Z10190503 Keywords : anline algorithms * scheduling Subject RIV: IN - Informatics, Computer Science Impact factor: 0.917, year: 2009

Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment

Science.gov (United States)

Madni, Syed Hamid Hussain; Abd Latiff, Muhammad Shafie; Abdullahi, Mohammed; Usman, Mohammed Joda

2017-01-01

Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing. PMID:28467505

Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment.

Science.gov (United States)

Madni, Syed Hamid Hussain; Abd Latiff, Muhammad Shafie; Abdullahi, Mohammed; Abdulhamid, Shafi'i Muhammad; Usman, Mohammed Joda

2017-01-01

Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.

A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems

Science.gov (United States)

Thammano, Arit; Teekeng, Wannaporn

2015-05-01

The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.

Job scheduling in a heterogenous grid environment

Energy Technology Data Exchange (ETDEWEB)

Oliker, Leonid; Biswas, Rupak; Shan, Hongzhang; Smith, Warren

2004-02-11

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. However, a number of major technical hurdles must be overcome before this potential can be realized. One problem that is critical to effective utilization of computational grids is the efficient scheduling of jobs. This work addresses this problem by describing and evaluating a grid scheduling architecture and three job migration algorithms. The architecture is scalable and does not assume control of local site resources. The job migration policies use the availability and performance of computer systems, the network bandwidth available between systems, and the volume of input and output data associated with each job. An extensive performance comparison is presented using real workloads from leading computational centers. The results, based on several key metrics, demonstrate that the performance of our distributed migration algorithms is significantly greater than that of a local scheduling framework and comparable to a non-scalable global scheduling approach.

Schedule Optimization of Imaging Missions for Multiple Satellites and Ground Stations Using Genetic Algorithm

Science.gov (United States)

Lee, Junghyun; Kim, Heewon; Chung, Hyun; Kim, Haedong; Choi, Sujin; Jung, Okchul; Chung, Daewon; Ko, Kwanghee

2018-04-01

In this paper, we propose a method that uses a genetic algorithm for the dynamic schedule optimization of imaging missions for multiple satellites and ground systems. In particular, the visibility conflicts of communication and mission operation using satellite resources (electric power and onboard memory) are integrated in sequence. Resource consumption and restoration are considered in the optimization process. Image acquisition is an essential part of satellite missions and is performed via a series of subtasks such as command uplink, image capturing, image storing, and image downlink. An objective function for optimization is designed to maximize the usability by considering the following components: user-assigned priority, resource consumption, and image-acquisition time. For the simulation, a series of hypothetical imaging missions are allocated to a multi-satellite control system comprising five satellites and three ground stations having S- and X-band antennas. To demonstrate the performance of the proposed method, simulations are performed via three operation modes: general, commercial, and tactical.

Diversity Controlling Genetic Algorithm for Order Acceptance and Scheduling Problem

Directory of Open Access Journals (Sweden)

Cheng Chen

2014-01-01

Full Text Available Selection and scheduling are an important topic in production systems. To tackle the order acceptance and scheduling problem on a single machine with release dates, tardiness penalty, and sequence-dependent setup times, in this paper a diversity controlling genetic algorithm (DCGA is proposed, in which a diversified population is maintained during the whole search process through survival selection considering both the fitness and the diversity of individuals. To measure the similarity between individuals, a modified Hamming distance without considering the unaccepted orders in the chromosome is adopted. The proposed DCGA was validated on 1500 benchmark instances with up to 100 orders. Compared with the state-of-the-art algorithms, the experimental results show that DCGA improves the solution quality obtained significantly, in terms of the deviation from upper bound.

Optimal power system generation scheduling by multi-objective genetic algorithms with preferences

International Nuclear Information System (INIS)

Zio, E.; Baraldi, P.; Pedroni, N.

2009-01-01

Power system generation scheduling is an important issue both from the economical and environmental safety viewpoints. The scheduling involves decisions with regards to the units start-up and shut-down times and to the assignment of the load demands to the committed generating units for minimizing the system operation costs and the emission of atmospheric pollutants. As many other real-world engineering problems, power system generation scheduling involves multiple, conflicting optimization criteria for which there exists no single best solution with respect to all criteria considered. Multi-objective optimization algorithms, based on the principle of Pareto optimality, can then be designed to search for the set of nondominated scheduling solutions from which the decision-maker (DM) must a posteriori choose the preferred alternative. On the other hand, often, information is available a priori regarding the preference values of the DM with respect to the objectives. When possible, it is important to exploit this information during the search so as to focus it on the region of preference of the Pareto-optimal set. In this paper, ways are explored to use this preference information for driving a multi-objective genetic algorithm towards the preferential region of the Pareto-optimal front. Two methods are considered: the first one extends the concept of Pareto dominance by biasing the chromosome replacement step of the algorithm by means of numerical weights that express the DM' s preferences; the second one drives the search algorithm by changing the shape of the dominance region according to linear trade-off functions specified by the DM. The effectiveness of the proposed approaches is first compared on a case study of literature. Then, a nonlinear, constrained, two-objective power generation scheduling problem is effectively tackled

A Simulated Annealing-Based Heuristic Algorithm for Job Shop Scheduling to Minimize Lateness

Directory of Open Access Journals (Sweden)

Rui Zhang

2013-04-01

Full Text Available A decomposition-based optimization algorithm is proposed for solving large job shop scheduling problems with the objective of minimizing the maximum lateness. First, we use the constraint propagation theory to derive the orientation of a portion of disjunctive arcs. Then we use a simulated annealing algorithm to find a decomposition policy which satisfies the maximum number of oriented disjunctive arcs. Subsequently, each subproblem (corresponding to a subset of operations as determined by the decomposition policy is successively solved with a simulated annealing algorithm, which leads to a feasible solution to the original job shop scheduling problem. Computational experiments are carried out for adapted benchmark problems, and the results show the proposed algorithm is effective and efficient in terms of solution quality and time performance.

Application of Tabu Search Algorithm in Job Shop Scheduling

Directory of Open Access Journals (Sweden)

Betrianis Betrianis

2010-10-01

Full Text Available Tabu Search is one of local search methods which is used to solve the combinatorial optimization problem. This method aimed is to make the searching process of the best solution in a complex combinatorial optimization problem(np hard, ex : job shop scheduling problem, became more effective, in a less computational time but with no guarantee to optimum solution.In this paper, tabu search is used to solve the job shop scheduling problem consists of 3 (three cases, which is ordering package of September, October and November with objective of minimizing makespan (Cmax. For each ordering package, there is a combination for initial solution and tabu list length. These result then  compared with 4 (four other methods using basic dispatching rules such as Shortest Processing Time (SPT, Earliest Due Date (EDD, Most Work Remaining (MWKR dan First Come First Served (FCFS. Scheduling used Tabu Search Algorithm is sensitive for variables changes and gives makespan shorter than scheduling used by other four methods.

Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid

Directory of Open Access Journals (Sweden)

Ruey-Maw Chen

2011-01-01

Full Text Available The task scheduling problem has been widely studied for assigning resources to tasks in heterogeneous grid environment. Effective task scheduling is an important issue for the performance of grid computing. Meanwhile, the task scheduling problem is an NP-complete problem. Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO metaheuristic approach to efficiently solve the task scheduling problems in grid. Meanwhile, two promising heuristics based on multimode project scheduling are proposed to help in solving interesting scheduling problems. They are the best performance resource heuristic and the latest finish time heuristic. These two heuristics applied to the PSO scheme are for speeding up the search of the particle and improving the capability of finding a sound schedule. Moreover, both global communication topology and local ring communication topology are also investigated for efficient study of proposed scheme. Simulation results demonstrate that the proposed approach in this investigation can successfully solve the task-resource assignment problems in grid computing and similar scheduling problems.

M-GCF: Multicolor-Green Conflict Free Scheduling Algorithm for WSN

DEFF Research Database (Denmark)

Pawar, Pranav M.; Nielsen, Rasmus Hjorth; Prasad, Neeli R.

2012-01-01

division multiple access (TDMA) scheduling algorithm, Multicolor-Green Conflict Free (M-GCF), for WSNs. The proposed algorithm finds multiple conflict free slots across a three-hop neighbor view. The algorithm shows better slot sharing with fewer conflicts along with good energy efficiency, throughput...... and delay as compared with state-of-the-art solutions. The results also include the performance of M-GCF with varying traffic rates, which also shows good energy efficiency, throughput and delay. The contribution of this paper and the main reason for the improved performance with varying number of nodes...

A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan

International Nuclear Information System (INIS)

Lian Zhigang; Gu Xingsheng; Jiao Bin

2008-01-01

It is well known that the flow-shop scheduling problem (FSSP) is a branch of production scheduling and is NP-hard. Now, many different approaches have been applied for permutation flow-shop scheduling to minimize makespan, but current algorithms even for moderate size problems cannot be solved to guarantee optimality. Some literatures searching PSO for continuous optimization problems are reported, but papers searching PSO for discrete scheduling problems are few. In this paper, according to the discrete characteristic of FSSP, a novel particle swarm optimization (NPSO) algorithm is presented and successfully applied to permutation flow-shop scheduling to minimize makespan. Computation experiments of seven representative instances (Taillard) based on practical data were made, and comparing the NPSO with standard GA, we obtain that the NPSO is clearly more efficacious than standard GA for FSSP to minimize makespan

Workflow as a Service in the Cloud: Architecture and Scheduling Algorithms

Science.gov (United States)

Wang, Jianwu; Korambath, Prakashan; Altintas, Ilkay; Davis, Jim; Crawl, Daniel

2017-01-01

With more and more workflow systems adopting cloud as their execution environment, it becomes increasingly challenging on how to efficiently manage various workflows, virtual machines (VMs) and workflow execution on VM instances. To make the system scalable and easy-to-extend, we design a Workflow as a Service (WFaaS) architecture with independent services. A core part of the architecture is how to efficiently respond continuous workflow requests from users and schedule their executions in the cloud. Based on different targets, we propose four heuristic workflow scheduling algorithms for the WFaaS architecture, and analyze the differences and best usages of the algorithms in terms of performance, cost and the price/performance ratio via experimental studies. PMID:29399237

Workflow as a Service in the Cloud: Architecture and Scheduling Algorithms.

Science.gov (United States)

Wang, Jianwu; Korambath, Prakashan; Altintas, Ilkay; Davis, Jim; Crawl, Daniel

2014-01-01

With more and more workflow systems adopting cloud as their execution environment, it becomes increasingly challenging on how to efficiently manage various workflows, virtual machines (VMs) and workflow execution on VM instances. To make the system scalable and easy-to-extend, we design a Workflow as a Service (WFaaS) architecture with independent services. A core part of the architecture is how to efficiently respond continuous workflow requests from users and schedule their executions in the cloud. Based on different targets, we propose four heuristic workflow scheduling algorithms for the WFaaS architecture, and analyze the differences and best usages of the algorithms in terms of performance, cost and the price/performance ratio via experimental studies.

Power efficient dynamic resource scheduling algorithms for LTE

OpenAIRE

Han, C; Beh, KC; Nicolaou, M; Armour, SMD; Doufexi, A

2010-01-01

This paper presents a link level analysis of the rate and energy efficiency performance of the LTE downlink considering the unitary codebook based precoding scheme. In a multi-user environment, appropriate radio resource management strategies can be applied to the system to improve the performance gain by exploiting multi-user diversity in the time, frequency and space domains and the gains can be translated to energy reduction at the base station. Several existing and novel resource scheduli...

Nuclear power plant maintenance scheduling dilemma: a genetic algorithm approach

International Nuclear Information System (INIS)

Mahdavi, M.H.; Modarres, M.

2004-01-01

There are huge numbers of components scheduled for maintenance when a nuclear power plant is shut down. Among these components, a number of them are safety related which their operability as well as reliability when plant becomes up is main concerns. Not performing proper maintenance on this class of components/system would impose substantial risk on operating the NPP. In this paper a new approach based on genetic algorithms is presented to optimize the NPP maintenance schedule during shutdown. following this approach the cost incurred by maintenance activities for each schedule is balanced with the risk imposed by the maintenance scheduling plan to the plant operation status when it is up. The risk model implemented in the GA scheduler as its evaluation function is developed on the basis of the probabilistic risk assessment methodology. the Ga optimizers itself is shown to be superior compared to other optimization methods such as the monte carlo technique

Flexible job-shop scheduling based on genetic algorithm and simulation validation

Directory of Open Access Journals (Sweden)

Zhou Erming

2017-01-01

Full Text Available This paper selects flexible job-shop scheduling problem as the research object, and Constructs mathematical model aimed at minimizing the maximum makespan. Taking the transmission reverse gear production line of a transmission corporation as an example, genetic algorithm is applied for flexible jobshop scheduling problem to get the specific optimal scheduling results with MATLAB. DELMIA/QUEST based on 3D discrete event simulation is applied to construct the physical model of the production workshop. On the basis of the optimal scheduling results, the logical link of the physical model for the production workshop is established, besides, importing the appropriate process parameters to make virtual simulation on the production workshop. Finally, through analyzing the simulated results, it shows that the scheduling results are effective and reasonable.

A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling.

Science.gov (United States)

Hajri, S; Liouane, N; Hammadi, S; Borne, P

2000-01-01

Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational scheme, heuristic rules for creating the initial population, and a new methodology for mixing and computing genetic operator probabilities.

A Hierarchical Algorithm for Integrated Scheduling and Control With Applications to Power Systems

DEFF Research Database (Denmark)

Sokoler, Leo Emil; Dinesen, Peter Juhler; Jørgensen, John Bagterp

2016-01-01

The contribution of this paper is a hierarchical algorithm for integrated scheduling and control via model predictive control of hybrid systems. The controlled system is a linear system composed of continuous control, state, and output variables. Binary variables occur as scheduling decisions in ...

A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters

OpenAIRE

Weiwei Lin; Wentai Wu; James Z. Wang

2016-01-01

Cloud computing provides on-demand computing and storage services with high performance and high scalability. However, the rising energy consumption of cloud data centers has become a prominent problem. In this paper, we first introduce an energy-aware framework for task scheduling in virtual clusters. The framework consists of a task resource requirements prediction module, an energy estimate module, and a scheduler with a task buffer. Secondly, based on this framework, we propose a virtual ...

Solving multi-objective job shop scheduling problems using a non-dominated sorting genetic algorithm

Science.gov (United States)

Piroozfard, Hamed; Wong, Kuan Yew

2015-05-01

The efforts of finding optimal schedules for the job shop scheduling problems are highly important for many real-world industrial applications. In this paper, a multi-objective based job shop scheduling problem by simultaneously minimizing makespan and tardiness is taken into account. The problem is considered to be more complex due to the multiple business criteria that must be satisfied. To solve the problem more efficiently and to obtain a set of non-dominated solutions, a meta-heuristic based non-dominated sorting genetic algorithm is presented. In addition, task based representation is used for solution encoding, and tournament selection that is based on rank and crowding distance is applied for offspring selection. Swapping and insertion mutations are employed to increase diversity of population and to perform intensive search. To evaluate the modified non-dominated sorting genetic algorithm, a set of modified benchmarking job shop problems obtained from the OR-Library is used, and the results are considered based on the number of non-dominated solutions and quality of schedules obtained by the algorithm.

Reusable rocket engine preventive maintenance scheduling using genetic algorithm

International Nuclear Information System (INIS)

Chen, Tao; Li, Jiawen; Jin, Ping; Cai, Guobiao

2013-01-01

This paper deals with the preventive maintenance (PM) scheduling problem of reusable rocket engine (RRE), which is different from the ordinary repairable systems, by genetic algorithm. Three types of PM activities for RRE are considered and modeled by introducing the concept of effective age. The impacts of PM on all subsystems' aging processes are evaluated based on improvement factor model. Then the reliability of engine is formulated by considering the accumulated time effect. After that, optimization model subjected to reliability constraint is developed for RRE PM scheduling at fixed interval. The optimal PM combination is obtained by minimizing the total cost in the whole life cycle for a supposed engine. Numerical investigations indicate that the subsystem's intrinsic reliability characteristic and the improvement factor of maintain operations are the most important parameters in RRE's PM scheduling management

Hybrid Discrete Differential Evolution Algorithm for Lot Splitting with Capacity Constraints in Flexible Job Scheduling

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Xinli Xu

2013-01-01

Full Text Available A two-level batch chromosome coding scheme is proposed to solve the lot splitting problem with equipment capacity constraints in flexible job shop scheduling, which includes a lot splitting chromosome and a lot scheduling chromosome. To balance global search and local exploration of the differential evolution algorithm, a hybrid discrete differential evolution algorithm (HDDE is presented, in which the local strategy with dynamic random searching based on the critical path and a random mutation operator is developed. The performance of HDDE was experimented with 14 benchmark problems and the practical dye vat scheduling problem. The simulation results showed that the proposed algorithm has the strong global search capability and can effectively solve the practical lot splitting problems with equipment capacity constraints.

Analysis and Optimisation of Hierarchically Scheduled Multiprocessor Embedded Systems

DEFF Research Database (Denmark)

Pop, Traian; Pop, Paul; Eles, Petru

2008-01-01

We present an approach to the analysis and optimisation of heterogeneous multiprocessor embedded systems. The systems are heterogeneous not only in terms of hardware components, but also in terms of communication protocols and scheduling policies. When several scheduling policies share a resource......, they are organised in a hierarchy. In this paper, we first develop a holistic scheduling and schedulability analysis that determines the timing properties of a hierarchically scheduled system. Second, we address design problems that are characteristic to such hierarchically scheduled systems: assignment...... of scheduling policies to tasks, mapping of tasks to hardware components, and the scheduling of the activities. We also present several algorithms for solving these problems. Our heuristics are able to find schedulable implementations under limited resources, achieving an efficient utilisation of the system...

Parallel Multi-Objective Genetic Algorithm for Short-Term Economic Environmental Hydrothermal Scheduling

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Zhong-Kai Feng

2017-01-01

Full Text Available With the increasingly serious energy crisis and environmental pollution, the short-term economic environmental hydrothermal scheduling (SEEHTS problem is becoming more and more important in modern electrical power systems. In order to handle the SEEHTS problem efficiently, the parallel multi-objective genetic algorithm (PMOGA is proposed in the paper. Based on the Fork/Join parallel framework, PMOGA divides the whole population of individuals into several subpopulations which will evolve in different cores simultaneously. In this way, PMOGA can avoid the wastage of computational resources and increase the population diversity. Moreover, the constraint handling technique is used to handle the complex constraints in SEEHTS, and a selection strategy based on constraint violation is also employed to ensure the convergence speed and solution feasibility. The results from a hydrothermal system in different cases indicate that PMOGA can make the utmost of system resources to significantly improve the computing efficiency and solution quality. Moreover, PMOGA has competitive performance in SEEHTS when compared with several other methods reported in the previous literature, providing a new approach for the operation of hydrothermal systems.

Multi-agent Pareto appointment exchanging in hospital patient scheduling

NARCIS (Netherlands)

Vermeulen, I.B.; Bohté, S.M.; Somefun, D.J.A.; Poutré, La J.A.

2007-01-01

We present a dynamic and distributed approach to the hospital patient scheduling problem, in which patients can have multiple appointments that have to be scheduled to different resources. To efficiently solve this problem we develop a multi-agent Pareto-improvement appointment exchanging algorithm:

Evaluation of a Cross Layer Scheduling Algorithm for LTE Downlink

DEFF Research Database (Denmark)

Popovska Avramova, Andrijana; Yan, Ying; Dittmann, Lars

2013-01-01

the resource utilization. The algorithm makes decisions based on the channel conditions, the size of the transmission buffers and different quality of service demands. The simulation results show that the new algorithm improves the resource utilization and provides better guaranties for service quality....

An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

OpenAIRE

Yunqing Rao; Dezhong Qi; Jinling Li

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better ...

A review of metaheuristic scheduling techniques in cloud computing

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Mala Kalra

2015-11-01

Full Text Available Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on-demand access to shared pool of resources over Internet in a self-service, dynamically scalable and metered manner. Cloud computing is still in its infancy, so to reap its full benefits, much research is required across a broad array of topics. One of the important research issues which need to be focused for its efficient performance is scheduling. The goal of scheduling is to map tasks to appropriate resources that optimize one or more objectives. Scheduling in cloud computing belongs to a category of problems known as NP-hard problem due to large solution space and thus it takes a long time to find an optimal solution. There are no algorithms which may produce optimal solution within polynomial time to solve these problems. In cloud environment, it is preferable to find suboptimal solution, but in short period of time. Metaheuristic based techniques have been proved to achieve near optimal solutions within reasonable time for such problems. In this paper, we provide an extensive survey and comparative analysis of various scheduling algorithms for cloud and grid environments based on three popular metaheuristic techniques: Ant Colony Optimization (ACO, Genetic Algorithm (GA and Particle Swarm Optimization (PSO, and two novel techniques: League Championship Algorithm (LCA and BAT algorithm.

Routing and scheduling of hazardous materials shipments: algorithmic approaches to managing spent nuclear fuel transport

International Nuclear Information System (INIS)

Cox, R.G.

1984-01-01

Much controversy surrounds government regulation of routing and scheduling of Hazardous Materials Transportation (HMT). Increases in operating costs must be balanced against expected benefits from local HMT bans and curfews when promulgating or preempting HMT regulations. Algorithmic approaches for evaluating HMT routing and scheduling regulatory policy are described. A review of current US HMT regulatory policy is presented to provide a context for the analysis. Next, a multiobjective shortest path algorithm to find the set of efficient routes under conflicting objectives is presented. This algorithm generates all efficient routes under any partial ordering in a single pass through the network. Also, scheduling algorithms are presented to estimate the travel time delay due to HMT curfews along a route. Algorithms are presented assuming either deterministic or stochastic travel times between curfew cities and also possible rerouting to avoid such cities. These algorithms are applied to the case study of US highway transport of spent nuclear fuel from reactors to permanent repositories. Two data sets were used. One data set included the US Interstate Highway System (IHS) network with reactor locations, possible repository sites, and 150 heavily populated areas (HPAs). The other data set contained estimates of the population residing with 0.5 miles of the IHS and the Eastern US. Curfew delay is dramatically reduced by optimally scheduling departure times unless inter-HPA travel times are highly uncertain. Rerouting shipments to avoid HPAs is a less efficient approach to reducing delay

MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines

Science.gov (United States)

Ozmutlu, H. Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines.

Science.gov (United States)

Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.

Comparative study of heuristics algorithms in solving flexible job shop scheduling problem with condition based maintenance

Directory of Open Access Journals (Sweden)

Yahong Zheng

2014-05-01

Full Text Available Purpose: This paper focuses on a classic optimization problem in operations research, the flexible job shop scheduling problem (FJSP, to discuss the method to deal with uncertainty in a manufacturing system.Design/methodology/approach: In this paper, condition based maintenance (CBM, a kind of preventive maintenance, is suggested to reduce unavailability of machines. Different to the simultaneous scheduling algorithm (SSA used in the previous article (Neale & Cameron,1979, an inserting algorithm (IA is applied, in which firstly a pre-schedule is obtained through heuristic algorithm and then maintenance tasks are inserted into the pre-schedule scheme.Findings: It is encouraging that a new better solution for an instance in benchmark of FJSP is obtained in this research. Moreover, factually SSA used in literature for solving normal FJSPPM (FJSP with PM is not suitable for the dynamic FJSPPM. Through application in the benchmark of normal FJSPPM, it is found that although IA obtains inferior results compared to SSA used in literature, it performs much better in executing speed.Originality/value: Different to traditional scheduling of FJSP, uncertainty of machines is taken into account, which increases the complexity of the problem. An inserting algorithm (IA is proposed to solve the dynamic scheduling problem. It is stated that the quality of the final result depends much on the quality of the pre-schedule obtained during the procedure of solving a normal FJSP. In order to find the best solution of FJSP, a comparative study of three heuristics is carried out, the integrated GA, ACO and ABC. In the comparative study, we find that GA performs best in the three heuristic algorithms. Meanwhile, a new better solution for an instance in benchmark of FJSP is obtained in this research.

Two parameter-tuned metaheuristic algorithms for the multi-level lot sizing and scheduling problem

Directory of Open Access Journals (Sweden)

S.M.T. Fatemi Ghomi

2012-10-01

Full Text Available This paper addresses the problem of lot sizing and scheduling problem for n-products and m-machines in flow shop environment where setups among machines are sequence-dependent and can be carried over. Many products must be produced under capacity constraints and allowing backorders. Since lot sizing and scheduling problems are well-known strongly NP-hard, much attention has been given to heuristics and metaheuristics methods. This paper presents two metaheuristics algorithms namely, Genetic Algorithm (GA and Imperialist Competitive Algorithm (ICA. Moreover, Taguchi robust design methodology is employed to calibrate the parameters of the algorithms for different size problems. In addition, the parameter-tuned algorithms are compared against a presented lower bound on randomly generated problems. At the end, comprehensive numerical examples are presented to demonstrate the effectiveness of the proposed algorithms. The results showed that the performance of both GA and ICA are very promising and ICA outperforms GA statistically.

SPORT: An Algorithm for Divisible Load Scheduling with Result Collection on Heterogeneous Systems

Science.gov (United States)

Ghatpande, Abhay; Nakazato, Hidenori; Beaumont, Olivier; Watanabe, Hiroshi

Divisible Load Theory (DLT) is an established mathematical framework to study Divisible Load Scheduling (DLS). However, traditional DLT does not address the scheduling of results back to source (i. e., result collection), nor does it comprehensively deal with system heterogeneity. In this paper, the DLSRCHETS (DLS with Result Collection on HET-erogeneous Systems) problem is addressed. The few papers to date that have dealt with DLSRCHETS, proposed simplistic LIFO (Last In, First Out) and FIFO (First In, First Out) type of schedules as solutions to DLSRCHETS. In this paper, a new polynomial time heuristic algorithm, SPORT (System Parameters based Optimized Result Transfer), is proposed as a solution to the DLSRCHETS problem. With the help of simulations, it is proved that the performance of SPORT is significantly better than existing algorithms. The other major contributions of this paper include, for the first time ever, (a) the derivation of the condition to identify the presence of idle time in a FIFO schedule for two processors, (b) the identification of the limiting condition for the optimality of FIFO and LIFO schedules for two processors, and (c) the introduction of the concept of equivalent processor in DLS for heterogeneous systems with result collection.

Effective Iterated Greedy Algorithm for Flow-Shop Scheduling Problems with Time lags

Science.gov (United States)

ZHAO, Ning; YE, Song; LI, Kaidian; CHEN, Siyu

2017-05-01

Flow shop scheduling problem with time lags is a practical scheduling problem and attracts many studies. Permutation problem(PFSP with time lags) is concentrated but non-permutation problem(non-PFSP with time lags) seems to be neglected. With the aim to minimize the makespan and satisfy time lag constraints, efficient algorithms corresponding to PFSP and non-PFSP problems are proposed, which consist of iterated greedy algorithm for permutation(IGTLP) and iterated greedy algorithm for non-permutation (IGTLNP). The proposed algorithms are verified using well-known simple and complex instances of permutation and non-permutation problems with various time lag ranges. The permutation results indicate that the proposed IGTLP can reach near optimal solution within nearly 11% computational time of traditional GA approach. The non-permutation results indicate that the proposed IG can reach nearly same solution within less than 1% computational time compared with traditional GA approach. The proposed research combines PFSP and non-PFSP together with minimal and maximal time lag consideration, which provides an interesting viewpoint for industrial implementation.

Data analysis with the DIANA meta-scheduling approach

International Nuclear Information System (INIS)

Anjum, A; McClatchey, R; Willers, I

2008-01-01

The concepts, design and evaluation of the Data Intensive and Network Aware (DIANA) meta-scheduling approach for solving the challenges of data analysis being faced by CERN experiments are discussed in this paper. Our results suggest that data analysis can be made robust by employing fault tolerant and decentralized meta-scheduling algorithms supported in our DIANA meta-scheduler. The DIANA meta-scheduler supports data intensive bulk scheduling, is network aware and follows a policy centric meta-scheduling. In this paper, we demonstrate that a decentralized and dynamic meta-scheduling approach is an effective strategy to cope with increasing numbers of users, jobs and datasets. We present 'quality of service' related statistics for physics analysis through the application of a policy centric fair-share scheduling model. The DIANA meta-schedulers create a peer-to-peer hierarchy of schedulers to accomplish resource management that changes with evolving loads and is dynamic and adapts to the volatile nature of the resources

Resource-Optimal Scheduling Using Priced Timed Automata

DEFF Research Database (Denmark)

Larsen, Kim Guldstrand; Rasmussen, Jacob Illum; Subramani, K.

2004-01-01

In this paper, we show how the simple structure of the linear programs encountered during symbolic minimum-cost reachability analysis of priced timed automata can be exploited in order to substantially improve the performance of the current algorithm. The idea is rooted in duality of linear......-80 percent performance gain. As a main application area, we show how to solve energy-optimal task graph scheduling problems using the framework of priced timed automata....

Meta-heuristic algorithms for parallel identical machines scheduling problem with weighted late work criterion and common due date.

Science.gov (United States)

Xu, Zhenzhen; Zou, Yongxing; Kong, Xiangjie

2015-01-01

To our knowledge, this paper investigates the first application of meta-heuristic algorithms to tackle the parallel machines scheduling problem with weighted late work criterion and common due date ([Formula: see text]). Late work criterion is one of the performance measures of scheduling problems which considers the length of late parts of particular jobs when evaluating the quality of scheduling. Since this problem is known to be NP-hard, three meta-heuristic algorithms, namely ant colony system, genetic algorithm, and simulated annealing are designed and implemented, respectively. We also propose a novel algorithm named LDF (largest density first) which is improved from LPT (longest processing time first). The computational experiments compared these meta-heuristic algorithms with LDF, LPT and LS (list scheduling), and the experimental results show that SA performs the best in most cases. However, LDF is better than SA in some conditions, moreover, the running time of LDF is much shorter than SA.

Protocol-transparent resource sharing in hierarchically scheduled real-time systems

NARCIS (Netherlands)

Heuvel, van den M.M.H.P.; Bril, R.J.; Lukkien, J.J.

2010-01-01

Hierarchical scheduling frameworks (HSFs) provide means for composing complex real-time systems from well-defined, independently analyzed subsystems. To support resource sharing within two-level HSFs, three synchronization protocols based on the stack resource policy (SRP) have recently been

Multi-objective generation scheduling with hybrid energy resources

Science.gov (United States)

Trivedi, Manas

In economic dispatch (ED) of electric power generation, the committed generating units are scheduled to meet the load demand at minimum operating cost with satisfying all unit and system equality and inequality constraints. Generation of electricity from the fossil fuel releases several contaminants into the atmosphere. So the economic dispatch objective can no longer be considered alone due to the environmental concerns that arise from the emissions produced by fossil fueled electric power plants. This research is proposing the concept of environmental/economic generation scheduling with traditional and renewable energy sources. Environmental/economic dispatch (EED) is a multi-objective problem with conflicting objectives since emission minimization is conflicting with fuel cost minimization. Production and consumption of fossil fuel and nuclear energy are closely related to environmental degradation. This causes negative effects to human health and the quality of life. Depletion of the fossil fuel resources will also be challenging for the presently employed energy systems to cope with future energy requirements. On the other hand, renewable energy sources such as hydro and wind are abundant, inexhaustible and widely available. These sources use native resources and have the capacity to meet the present and the future energy demands of the world with almost nil emissions of air pollutants and greenhouse gases. The costs of fossil fuel and renewable energy are also heading in opposite directions. The economic policies needed to support the widespread and sustainable markets for renewable energy sources are rapidly evolving. The contribution of this research centers on solving the economic dispatch problem of a system with hybrid energy resources under environmental restrictions. It suggests an effective solution of renewable energy to the existing fossil fueled and nuclear electric utilities for the cheaper and cleaner production of electricity with hourly

Advertisement scheduling on commercial radio station using genetics algorithm

Science.gov (United States)

Purnamawati, S.; Nababan, E. B.; Tsani, B.; Taqyuddin, R.; Rahmat, R. F.

2018-03-01

On the commercial radio station, the advertising schedule is done manually, which resulted in ineffectiveness of ads schedule. Playback time consists of two types such as prime time and regular time. Radio Ads scheduling will be discussed in this research is based on ad playback schedule between 5am until 12am which rules every 15 minutes. It provides 3 slots ads with playback duration per ads maximum is 1 minute. If the radio broadcast time per day is 12 hours, then the maximum number of ads per day which can be aired is 76 ads. The other is the enactment of rules of prime time, namely the hours where the common people (listeners) have the greatest opportunity to listen to the radio, namely between the hours and hours of 4 am - 8 am, 6 pm - 10 pm. The number of screenings of the same ads on one day are limited to prime time ie 5 times, while for regular time is 8 times. Radio scheduling process is done using genetic algorithms which are composed of processes initialization chromosomes, selection, crossover and mutation. Study on chromosome 3 genes, each chromosome will be evaluated based on the value of fitness calculated based on the number of infractions that occurred on each individual chromosome. Where rule 1 is the number of screenings per ads must not be more than 5 times per day and rule 2 is there should never be two or more scheduling ads delivered on the same day and time. After fitness value of each chromosome is acquired, then the do the selection, crossover and mutation. From this research result, the optimal advertising schedule with schedule a whole day and ads data playback time ads with this level of accuracy: the average percentage was 83.79%.

Comparison of Multiobjective Evolutionary Algorithms for Operations Scheduling under Machine Availability Constraints

Directory of Open Access Journals (Sweden)

M. Frutos

2013-01-01

Full Text Available Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier.

基于ISM的动态优先级调度算法%Dynamic Priority Schedule Algorithm Based on ISM

Institute of Scientific and Technical Information of China (English)

余祖峰; 蔡启先; 刘明

2011-01-01

The EDF schedule algorithm, one of main real-time schedule algorithms of the embedded Linux operating system, can not solve the overload schedule.For this, the paper introduces SLAD algorithm and BACKSLASH algorithm, which have good performance of system load.According to thinking of ISM algorithm, it puts forward a kind of dynamic priority schedule algorithm.According to case of overloads within some time, the algorithm can adjust EDF algorithm and SLAD algorithm neatly, thus improves schedule efficiency of system in usual load and overload cases.Test results for real-time tasks Deadline Miss Ratio(DMR) show its improvement effect.%在嵌入式Linux操作系统的实时调度算法中,EDF调度算法不能解决负载过载问题.为此,引进对系统负载有着良好表现的SLAD算法和BACKSLASH算法.基于ISM算法思路,提出一种动态优先级调度算法.该算法能根据一段时间内负载过载的情况,灵活地调度EDF算法和SLAD算法,从面提高系统在正常负载和过载情况下的调度效率.对实时任务截止期错失率DMR指标的测试结果证明了其改进效果.

An Extended Genetic Algorithm for Distributed Integration of Fuzzy Process Planning and Scheduling

Directory of Open Access Journals (Sweden)

Shuai Zhang

2016-01-01

Full Text Available The distributed integration of process planning and scheduling (DIPPS aims to simultaneously arrange the two most important manufacturing stages, process planning and scheduling, in a distributed manufacturing environment. Meanwhile, considering its advantage corresponding to actual situation, the triangle fuzzy number (TFN is adopted in DIPPS to represent the machine processing and transportation time. In order to solve this problem and obtain the optimal or near-optimal solution, an extended genetic algorithm (EGA with innovative three-class encoding method, improved crossover, and mutation strategies is proposed. Furthermore, a local enhancement strategy featuring machine replacement and order exchange is also added to strengthen the local search capability on the basic process of genetic algorithm. Through the verification of experiment, EGA achieves satisfactory results all in a very short period of time and demonstrates its powerful performance in dealing with the distributed integration of fuzzy process planning and scheduling (DIFPPS.

Vectorization of a penalty function algorithm for well scheduling

Science.gov (United States)

Absar, I.

1984-01-01

In petroleum engineering, the oil production profiles of a reservoir can be simulated by using a finite gridded model. This profile is affected by the number and choice of wells which in turn is a result of various production limits and constraints including, for example, the economic minimum well spacing, the number of drilling rigs available and the time required to drill and complete a well. After a well is available it may be shut in because of excessive water or gas productions. In order to optimize the field performance a penalty function algorithm was developed for scheduling wells. For an example with some 343 wells and 15 different constraints, the scheduling routine vectorized for the CYBER 205 averaged 560 times faster performance than the scalar version.

Distributed Research Project Scheduling Based on Multi-Agent Methods

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Constanta Nicoleta Bodea

2011-01-01

Full Text Available Different project planning and scheduling approaches have been developed. The Operational Research (OR provides two major planning techniques: CPM (Critical Path Method and PERT (Program Evaluation and Review Technique. Due to projects complexity and difficulty to use classical methods, new approaches were developed. Artificial Intelligence (AI initially promoted the automatic planner concept, but model-based planning and scheduling methods emerged later on. The paper adresses the project scheduling optimization problem, when projects are seen as Complex Adaptive Systems (CAS. Taken into consideration two different approaches for project scheduling optimization: TCPSP (Time- Constrained Project Scheduling and RCPSP (Resource-Constrained Project Scheduling, the paper focuses on a multiagent implementation in MATLAB for TCSP. Using the research project as a case study, the paper includes a comparison between two multi-agent methods: Genetic Algorithm (GA and Ant Colony Algorithm (ACO.

Options for Parallelizing a Planning and Scheduling Algorithm

Science.gov (United States)

Clement, Bradley J.; Estlin, Tara A.; Bornstein, Benjamin D.

2011-01-01

Space missions have a growing interest in putting multi-core processors onboard spacecraft. For many missions processing power significantly slows operations. We investigate how continual planning and scheduling algorithms can exploit multi-core processing and outline different potential design decisions for a parallelized planning architecture. This organization of choices and challenges helps us with an initial design for parallelizing the CASPER planning system for a mesh multi-core processor. This work extends that presented at another workshop with some preliminary results.

Profit-based conventional resource scheduling with renewable energy penetration

Science.gov (United States)

Reddy, K. Srikanth; Panwar, Lokesh Kumar; Kumar, Rajesh; Panigrahi, B. K.

2017-08-01

Technological breakthroughs in renewable energy technologies (RETs) enabled them to attain grid parity thereby making them potential contenders for existing conventional resources. To examine the market participation of RETs, this paper formulates a scheduling problem accommodating energy market participation of wind- and solar-independent power producers (IPPs) treating both conventional and RETs as identical entities. Furthermore, constraints pertaining to penetration and curtailments of RETs are restructured. Additionally, an appropriate objective function for profit incurred by conventional resource IPPs through reserve market participation as a function of renewable energy curtailment is also proposed. The proposed concept is simulated with a test system comprising 10 conventional generation units in conjunction with solar photovoltaic (SPV) and wind energy generators (WEG). The simulation results indicate that renewable energy integration and its curtailment limits influence the market participation or scheduling strategies of conventional resources in both energy and reserve markets. Furthermore, load and reliability parameters are also affected.

Constraint-based scheduling applying constraint programming to scheduling problems

CERN Document Server

Baptiste, Philippe; Nuijten, Wim

2001-01-01

Constraint Programming is a problem-solving paradigm that establishes a clear distinction between two pivotal aspects of a problem: (1) a precise definition of the constraints that define the problem to be solved and (2) the algorithms and heuristics enabling the selection of decisions to solve the problem. It is because of these capabilities that Constraint Programming is increasingly being employed as a problem-solving tool to solve scheduling problems. Hence the development of Constraint-Based Scheduling as a field of study. The aim of this book is to provide an overview of the most widely used Constraint-Based Scheduling techniques. Following the principles of Constraint Programming, the book consists of three distinct parts: The first chapter introduces the basic principles of Constraint Programming and provides a model of the constraints that are the most often encountered in scheduling problems. Chapters 2, 3, 4, and 5 are focused on the propagation of resource constraints, which usually are responsibl...

An FMS Dynamic Production Scheduling Algorithm Considering Cutting Tool Failure and Cutting Tool Life

International Nuclear Information System (INIS)

Setiawan, A; Wangsaputra, R; Halim, A H; Martawirya, Y Y

2016-01-01

This paper deals with Flexible Manufacturing System (FMS) production rescheduling due to unavailability of cutting tools caused either of cutting tool failure or life time limit. The FMS consists of parallel identical machines integrated with an automatic material handling system and it runs fully automatically. Each machine has a same cutting tool configuration that consists of different geometrical cutting tool types on each tool magazine. The job usually takes two stages. Each stage has sequential operations allocated to machines considering the cutting tool life. In the real situation, the cutting tool can fail before the cutting tool life is reached. The objective in this paper is to develop a dynamic scheduling algorithm when a cutting tool is broken during unmanned and a rescheduling needed. The algorithm consists of four steps. The first step is generating initial schedule, the second step is determination the cutting tool failure time, the third step is determination of system status at cutting tool failure time and the fourth step is the rescheduling for unfinished jobs. The approaches to solve the problem are complete-reactive scheduling and robust-proactive scheduling. The new schedules result differences starting time and completion time of each operations from the initial schedule. (paper)

Clustering and Genetic Algorithm Based Hybrid Flowshop Scheduling with Multiple Operations

Directory of Open Access Journals (Sweden)

Yingfeng Zhang

2014-01-01

Full Text Available This research is motivated by a flowshop scheduling problem of our collaborative manufacturing company for aeronautic products. The heat-treatment stage (HTS and precision forging stage (PFS of the case are selected as a two-stage hybrid flowshop system. In HTS, there are four parallel machines and each machine can process a batch of jobs simultaneously. In PFS, there are two machines. Each machine can install any module of the four modules for processing the workpeices with different sizes. The problem is characterized by many constraints, such as batching operation, blocking environment, and setup time and working time limitations of modules, and so forth. In order to deal with the above special characteristics, the clustering and genetic algorithm is used to calculate the good solution for the two-stage hybrid flowshop problem. The clustering is used to group the jobs according to the processing ranges of the different modules of PFS. The genetic algorithm is used to schedule the optimal sequence of the grouped jobs for the HTS and PFS. Finally, a case study is used to demonstrate the efficiency and effectiveness of the designed genetic algorithm.

Optimal Intermittent Dose Schedules for Chemotherapy Using Genetic Algorithm

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Nadia ALAM

2013-08-01

Full Text Available In this paper, a design method for optimal cancer chemotherapy schedules via genetic algorithm (GA is presented. The design targets the key objective of chemotherapy to minimize the size of cancer tumor after a predefined time with keeping toxic side effects in limit. This is a difficult target to achieve using conventional clinical methods due to poor therapeutic indices of existing anti-cancer drugs. Moreover, there are clinical limitations in treatment administration to maintain continuous treatment. Besides, carefully decided rest periods are recommended to for patient’s comfort. Three intermittent drug scheduling schemes are presented in this paper where GA is used to optimize the dose quantities and timings by satisfying several treatment constraints. All three schemes are found to be effective in total elimination of cancer tumor after an agreed treatment length. The number of cancer cells is found zero at the end of the treatment for all three cases with tolerable toxicity. Finally, two of the schemes, “Fixed interval variable dose (FIVD and “Periodic dose” that are periodic in characteristic have been emphasized due to their additional simplicity in administration along with friendliness to patients. responses to the designed treatment schedules. Therefore the proposed design method is capable of planning effective, simple, patient friendly and acceptable chemotherapy schedules.

Wireless-Uplinks-Based Energy-Efficient Scheduling in Mobile Cloud Computing

Directory of Open Access Journals (Sweden)

Xing Liu

2015-01-01

Full Text Available Mobile cloud computing (MCC combines cloud computing and mobile internet to improve the computational capabilities of resource-constrained mobile devices (MDs. In MCC, mobile users could not only improve the computational capability of MDs but also save operation consumption by offloading the mobile applications to the cloud. However, MCC faces the problem of energy efficiency because of time-varying channels when the offloading is being executed. In this paper, we address the issue of energy-efficient scheduling for wireless uplink in MCC. By introducing Lyapunov optimization, we first propose a scheduling algorithm that can dynamically choose channel to transmit data based on queue backlog and channel statistics. Then, we show that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in a channel-aware MCC system. Simulation results show that the proposed scheduling algorithm can reduce the time average energy consumption for offloading compared to the existing algorithm.

Transmission Scheduling and Routing Algorithms for Delay Tolerant Networks

Science.gov (United States)

Dudukovich, Rachel; Raible, Daniel E.

2016-01-01

The challenges of data processing, transmission scheduling and routing within a space network present a multi-criteria optimization problem. Long delays, intermittent connectivity, asymmetric data rates and potentially high error rates make traditional networking approaches unsuitable. The delay tolerant networking architecture and protocols attempt to mitigate many of these issues, yet transmission scheduling is largely manually configured and routes are determined by a static contact routing graph. A high level of variability exists among the requirements and environmental characteristics of different missions, some of which may allow for the use of more opportunistic routing methods. In all cases, resource allocation and constraints must be balanced with the optimization of data throughput and quality of service. Much work has been done researching routing techniques for terrestrial-based challenged networks in an attempt to optimize contact opportunities and resource usage. This paper examines several popular methods to determine their potential applicability to space networks.

Resource management and scheduling policy based on grid for AIoT

Science.gov (United States)

Zou, Yiqin; Quan, Li

2017-07-01

This paper has a research on resource management and scheduling policy based on grid technology for Agricultural Internet of Things (AIoT). Facing the situation of a variety of complex and heterogeneous agricultural resources in AIoT, it is difficult to represent them in a unified way. But from an abstract perspective, there are some common models which can express their characteristics and features. Based on this, we proposed a high-level model called Agricultural Resource Hierarchy Model (ARHM), which can be used for modeling various resources. It introduces the agricultural resource modeling method based on this model. Compared with traditional application-oriented three-layer model, ARHM can hide the differences of different applications and make all applications have a unified interface layer and be implemented without distinction. Furthermore, it proposes a Web Service Resource Framework (WSRF)-based resource management method and the encapsulation structure for it. Finally, it focuses on the discussion of multi-agent-based AG resource scheduler, which is a collaborative service provider pattern in multiple agricultural production domains.

Proposed algorithm to improve job shop production scheduling using ant colony optimization method

Science.gov (United States)

Pakpahan, Eka KA; Kristina, Sonna; Setiawan, Ari

2017-12-01

This paper deals with the determination of job shop production schedule on an automatic environment. On this particular environment, machines and material handling system are integrated and controlled by a computer center where schedule were created and then used to dictate the movement of parts and the operations at each machine. This setting is usually designed to have an unmanned production process for a specified interval time. We consider here parts with various operations requirement. Each operation requires specific cutting tools. These parts are to be scheduled on machines each having identical capability, meaning that each machine is equipped with a similar set of cutting tools therefore is capable of processing any operation. The availability of a particular machine to process a particular operation is determined by the remaining life time of its cutting tools. We proposed an algorithm based on the ant colony optimization method and embedded them on matlab software to generate production schedule which minimize the total processing time of the parts (makespan). We test the algorithm on data provided by real industry and the process shows a very short computation time. This contributes a lot to the flexibility and timelines targeted on an automatic environment.

A Single-Machine Two-Agent Scheduling Problem by a Branch-and-Bound and Three Simulated Annealing Algorithms

Directory of Open Access Journals (Sweden)

Shangchia Liu

2015-01-01

Full Text Available In the field of distributed decision making, different agents share a common processing resource, and each agent wants to minimize a cost function depending on its jobs only. These issues arise in different application contexts, including real-time systems, integrated service networks, industrial districts, and telecommunication systems. Motivated by its importance on practical applications, we consider two-agent scheduling on a single machine where the objective is to minimize the total completion time of the jobs of the first agent with the restriction that an upper bound is allowed the total completion time of the jobs for the second agent. For solving the proposed problem, a branch-and-bound and three simulated annealing algorithms are developed for the optimal solution, respectively. In addition, the extensive computational experiments are also conducted to test the performance of the algorithms.

A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems

Directory of Open Access Journals (Sweden)

Hamed Piroozfard

2016-01-01

Full Text Available Scheduling is considered as an important topic in production management and combinatorial optimization in which it ubiquitously exists in most of the real-world applications. The attempts of finding optimal or near optimal solutions for the job shop scheduling problems are deemed important, because they are characterized as highly complex and NP-hard problems. This paper describes the development of a hybrid genetic algorithm for solving the nonpreemptive job shop scheduling problems with the objective of minimizing makespan. In order to solve the presented problem more effectively, an operation-based representation was used to enable the construction of feasible schedules. In addition, a new knowledge-based operator was designed based on the problem’s characteristics in order to use machines’ idle times to improve the solution quality, and it was developed in the context of function evaluation. A machine based precedence preserving order-based crossover was proposed to generate the offspring. Furthermore, a simulated annealing based neighborhood search technique was used to improve the local exploitation ability of the algorithm and to increase its population diversity. In order to prove the efficiency and effectiveness of the proposed algorithm, numerous benchmarked instances were collected from the Operations Research Library. Computational results of the proposed hybrid genetic algorithm demonstrate its effectiveness.

A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling

Directory of Open Access Journals (Sweden)

M. Fera

2018-09-01

Full Text Available Additive Manufacturing (AM is a process of joining materials to make objects from 3D model data, usually layer by layer, as opposed to subtractive manufacturing methodologies. Selective Laser Melting, commercially known as Direct Metal Laser Sintering (DMLS®, is the most diffused additive process in today’s manufacturing industry. Introduction of a DMLS® machine in a production department has remarkable effects not only on industrial design but also on production planning, for example, on machine scheduling. Scheduling for a traditional single machine can employ consolidated models. Scheduling of an AM machine presents new issues because it must consider the capability of producing different geometries, simultaneously. The aim of this paper is to provide a mathematical model for an AM/SLM machine scheduling. The complexity of the model is NP-HARD, so possible solutions must be found by metaheuristic algorithms, e.g., Genetic Algorithms. Genetic Algorithms solve sequential optimization problems by handling vectors; in the present paper, we must modify them to handle a matrix. The effectiveness of the proposed algorithms will be tested on a test case formed by a 30 Part Number production plan with a high variability in complexity, distinct due dates and low production volumes.

An Optimizing Space Data-Communications Scheduling Method and Algorithm with Interference Mitigation, Generalized for a Broad Class of Optimization Problems

Science.gov (United States)

Rash, James

2014-01-01

NASA's space data-communications infrastructure-the Space Network and the Ground Network-provide scheduled (as well as some limited types of unscheduled) data-communications services to user spacecraft. The Space Network operates several orbiting geostationary platforms (the Tracking and Data Relay Satellite System (TDRSS)), each with its own servicedelivery antennas onboard. The Ground Network operates service-delivery antennas at ground stations located around the world. Together, these networks enable data transfer between user spacecraft and their mission control centers on Earth. Scheduling data-communications events for spacecraft that use the NASA communications infrastructure-the relay satellites and the ground stations-can be accomplished today with software having an operational heritage dating from the 1980s or earlier. An implementation of the scheduling methods and algorithms disclosed and formally specified herein will produce globally optimized schedules with not only optimized service delivery by the space data-communications infrastructure but also optimized satisfaction of all user requirements and prescribed constraints, including radio frequency interference (RFI) constraints. Evolutionary algorithms, a class of probabilistic strategies for searching large solution spaces, is the essential technology invoked and exploited in this disclosure. Also disclosed are secondary methods and algorithms for optimizing the execution efficiency of the schedule-generation algorithms themselves. The scheduling methods and algorithms as presented are adaptable to accommodate the complexity of scheduling the civilian and/or military data-communications infrastructure within the expected range of future users and space- or ground-based service-delivery assets. Finally, the problem itself, and the methods and algorithms, are generalized and specified formally. The generalized methods and algorithms are applicable to a very broad class of combinatorial

A Hybrid Genetic Algorithm to Minimize Total Tardiness for Unrelated Parallel Machine Scheduling with Precedence Constraints

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Chunfeng Liu

2013-01-01

Full Text Available The paper presents a novel hybrid genetic algorithm (HGA for a deterministic scheduling problem where multiple jobs with arbitrary precedence constraints are processed on multiple unrelated parallel machines. The objective is to minimize total tardiness, since delays of the jobs may lead to punishment cost or cancellation of orders by the clients in many situations. A priority rule-based heuristic algorithm, which schedules a prior job on a prior machine according to the priority rule at each iteration, is suggested and embedded to the HGA for initial feasible schedules that can be improved in further stages. Computational experiments are conducted to show that the proposed HGA performs well with respect to accuracy and efficiency of solution for small-sized problems and gets better results than the conventional genetic algorithm within the same runtime for large-sized problems.

Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

International Nuclear Information System (INIS)

Kim, Dong Yun; Seong, Poong Hyun

1996-01-01

In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller

Using Coevolution Genetic Algorithm with Pareto Principles to Solve Project Scheduling Problem under Duration and Cost Constraints

Directory of Open Access Journals (Sweden)

Alexandr Victorovich Budylskiy

2014-06-01

Full Text Available This article considers the multicriteria optimization approach using the modified genetic algorithm to solve the project-scheduling problem under duration and cost constraints. The work contains the list of choices for solving this problem. The multicriteria optimization approach is justified here. The study describes the Pareto principles, which are used in the modified genetic algorithm. We identify the mathematical model of the project-scheduling problem. We introduced the modified genetic algorithm, the ranking strategies, the elitism approaches. The article includes the example.

An Efficient Randomized Algorithm for Real-Time Process Scheduling in PicOS Operating System

Science.gov (United States)

Helmy*, Tarek; Fatai, Anifowose; Sallam, El-Sayed

PicOS is an event-driven operating environment designed for use with embedded networked sensors. More specifically, it is designed to support the concurrency in intensive operations required by networked sensors with minimal hardware requirements. Existing process scheduling algorithms of PicOS; a commercial tiny, low-footprint, real-time operating system; have their associated drawbacks. An efficient, alternative algorithm, based on a randomized selection policy, has been proposed, demonstrated, confirmed for efficiency and fairness, on the average, and has been recommended for implementation in PicOS. Simulations were carried out and performance measures such as Average Waiting Time (AWT) and Average Turn-around Time (ATT) were used to assess the efficiency of the proposed randomized version over the existing ones. The results prove that Randomized algorithm is the best and most attractive for implementation in PicOS, since it is most fair and has the least AWT and ATT on average over the other non-preemptive scheduling algorithms implemented in this paper.

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

OpenAIRE

Amjad, Muhammad Kamal; Butt, Shahid Ikramullah; Kousar, Rubeena; Ahmad, Riaz; Agha, Mujtaba Hassan; Faping, Zhang; Anjum, Naveed; Asgher, Umer

2018-01-01

Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in...

Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicles driving schedules

Energy Technology Data Exchange (ETDEWEB)

Cardoso, Goncalo [Technical Univ. of Lisbon (Portugal); Stadler, Michael [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Center for Energy and Innovation Technologies (Austria); Bozchalui, Mohammed C. [NEC Laboratories American Inc., Irving, TX (United States); Sharma, Ratnesh [NEC Laboratories American Inc., Irving, TX (United States); Marnay, Chris [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Barbosa-Povoa, Ana [Technical Univ. of Lisbon (Portugal); Ferrao, Paulo [Technical Univ. of Lisbon (Portugal)

2013-12-06

The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained using the stochastic formulation of the problem.

A Heuristics Approach for Classroom Scheduling Using Genetic Algorithm Technique

Science.gov (United States)

Ahmad, Izah R.; Sufahani, Suliadi; Ali, Maselan; Razali, Siti N. A. M.

2018-04-01

Reshuffling and arranging classroom based on the capacity of the audience, complete facilities, lecturing time and many more may lead to a complexity of classroom scheduling. While trying to enhance the productivity in classroom planning, this paper proposes a heuristic approach for timetabling optimization. A new algorithm was produced to take care of the timetabling problem in a university. The proposed of heuristics approach will prompt a superior utilization of the accessible classroom space for a given time table of courses at the university. Genetic Algorithm through Java programming languages were used in this study and aims at reducing the conflicts and optimizes the fitness. The algorithm considered the quantity of students in each class, class time, class size, time accessibility in each class and lecturer who in charge of the classes.

MaGate Simulator: A Simulation Environment for a Decentralized Grid Scheduler

Science.gov (United States)

Huang, Ye; Brocco, Amos; Courant, Michele; Hirsbrunner, Beat; Kuonen, Pierre

This paper presents a simulator for of a decentralized modular grid scheduler named MaGate. MaGate’s design emphasizes scheduler interoperability by providing intelligent scheduling serving the grid community as a whole. Each MaGate scheduler instance is able to deal with dynamic scheduling conditions, with continuously arriving grid jobs. Received jobs are either allocated on local resources, or delegated to other MaGates for remote execution. The proposed MaGate simulator is based on GridSim toolkit and Alea simulator, and abstracts the features and behaviors of complex fundamental grid elements, such as grid jobs, grid resources, and grid users. Simulation of scheduling tasks is supported by a grid network overlay simulator executing distributed ant-based swarm intelligence algorithms to provide services such as group communication and resource discovery. For evaluation, a comparison of behaviors of different collaborative policies among a community of MaGates is provided. Results support the use of the proposed approach as a functional ready grid scheduler simulator.

Scheduling multimedia services in cloud computing environment

Science.gov (United States)

Liu, Yunchang; Li, Chunlin; Luo, Youlong; Shao, Yanling; Zhang, Jing

2018-02-01

Currently, security is a critical factor for multimedia services running in the cloud computing environment. As an effective mechanism, trust can improve security level and mitigate attacks within cloud computing environments. Unfortunately, existing scheduling strategy for multimedia service in the cloud computing environment do not integrate trust mechanism when making scheduling decisions. In this paper, we propose a scheduling scheme for multimedia services in multi clouds. At first, a novel scheduling architecture is presented. Then, We build a trust model including both subjective trust and objective trust to evaluate the trust degree of multimedia service providers. By employing Bayesian theory, the subjective trust degree between multimedia service providers and users is obtained. According to the attributes of QoS, the objective trust degree of multimedia service providers is calculated. Finally, a scheduling algorithm integrating trust of entities is proposed by considering the deadline, cost and trust requirements of multimedia services. The scheduling algorithm heuristically hunts for reasonable resource allocations and satisfies the requirement of trust and meets deadlines for the multimedia services. Detailed simulated experiments demonstrate the effectiveness and feasibility of the proposed trust scheduling scheme.

Hydro-Thermal-Wind Generation Scheduling Considering Economic and Environmental Factors Using Heuristic Algorithms

Directory of Open Access Journals (Sweden)

Suresh K. Damodaran

2018-02-01

Full Text Available Hydro-thermal-wind generation scheduling (HTWGS with economic and environmental factors is a multi-objective complex nonlinear power system optimization problem with many equality and inequality constraints. The objective of the problem is to generate an hour-by-hour optimum schedule of hydro-thermal-wind power plants to attain the least emission of pollutants from thermal plants and a reduced generation cost of thermal and wind plants for a 24-h period, satisfying the system constraints. The paper presents a detailed framework of the HTWGS problem and proposes a modified particle swarm optimization (MPSO algorithm for evolving a solution. The competency of selected heuristic algorithms, representing different heuristic groups, viz. the binary coded genetic algorithm (BCGA, particle swarm optimization (PSO, improved harmony search (IHS, and JAYA algorithm, for searching for an optimal solution to HTWGS considering economic and environmental factors was investigated in a trial system consisting of a multi-stream cascaded system with four reservoirs, three thermal plants, and two wind plants. Appropriate mathematical models were used for representing the water discharge, generation cost, and pollutant emission of respective power plants incorporated in the system. Statistical analysis was performed to check the consistency and reliability of the proposed algorithm. The simulation results indicated that the proposed MPSO algorithm provided a better solution to the problem of HTWGS, with a reduced generation cost and the least emission, when compared with the other heuristic algorithms considered.

Adaptive Radio Resource Allocation in Hierarchical QoS Scheduling for IEEE 802.16 Systems

DEFF Research Database (Denmark)

Wang, Hua; Dittmann, Lars

2007-01-01

Future mobile communication systems such as IEEE 802.16 are expected to deliver a variety of multimedia services with diverse QoS requirements. To guarantee the QoS provision, appropriate scheduler architecture and scheduling algorithms have to be carefully designed. In this paper, we propose...

A Local Search Algorithm for the Flow Shop Scheduling Problem with Release Dates

Directory of Open Access Journals (Sweden)

Tao Ren

2015-01-01

Full Text Available This paper discusses the flow shop scheduling problem to minimize the makespan with release dates. By resequencing the jobs, a modified heuristic algorithm is obtained for handling large-sized problems. Moreover, based on some properties, a local search scheme is provided to improve the heuristic to gain high-quality solution for moderate-sized problems. A sequence-independent lower bound is presented to evaluate the performance of the algorithms. A series of simulation results demonstrate the effectiveness of the proposed algorithms.

Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm

Science.gov (United States)

Ausaf, Muhammad Farhan; Gao, Liang; Li, Xinyu

2015-12-01

For increasing the overall performance of modern manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling (IPPS) problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS (MOIPPS) problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatching rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.

Cognitive radio resource allocation based on coupled chaotic genetic algorithm

International Nuclear Information System (INIS)

Zu Yun-Xiao; Zhou Jie; Zeng Chang-Chang

2010-01-01

A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed

Short term economic emission power scheduling of hydrothermal energy systems using improved water cycle algorithm

International Nuclear Information System (INIS)

Haroon, S.S.; Malik, T.N.

2017-01-01

Due to the increasing environmental concerns, the demand of clean and green energy and concern of atmospheric pollution is increasing. Hence, the power utilities are forced to limit their emissions within the prescribed limits. Therefore, the minimization of fuel cost as well as exhaust gas emissions is becoming an important and challenging task in the short-term scheduling of hydro-thermal energy systems. This paper proposes a novel algorithm known as WCA-ER (Water Cycle Algorithm with Evaporation Rate) to inspect the short term EEPSHES (Economic Emission Power Scheduling of Hydrothermal Energy Systems). WCA has its ancestries from the natural hydrologic cycle i.e. the raining process forms streams and these streams start flowing towards the rivers which finally flow towards the sea. The worth of WCA-ER has been tested on the standard economic emission power scheduling of hydrothermal energy test system consisting of four hydropower and three thermal plants. The problem has been investigated for the three case studies (i) ECS (Economic Cost Scheduling), (ii) ES (Economic Emission Scheduling) and (iii) ECES (Economic Cost and Emission Scheduling). The results obtained show that WCA-ER is superior to many other methods in the literature in bringing lower fuel cost and emissions. (author)

Optimal Scheduling for Retrieval Jobs in Double-Deep AS/RS by Evolutionary Algorithms

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Kuo-Yang Wu

2013-01-01

Full Text Available We investigate the optimal scheduling of retrieval jobs for double-deep type Automated Storage and Retrieval Systems (AS/RS in the Flexible Manufacturing System (FMS used in modern industrial production. Three types of evolutionary algorithms, the Genetic Algorithm (GA, the Immune Genetic Algorithm (IGA, and the Particle Swarm Optimization (PSO algorithm, are implemented to obtain the optimal assignments. The objective is to minimize the working distance, that is, the shortest retrieval time travelled by the Storage and Retrieval (S/R machine. Simulation results and comparisons show the advantages and feasibility of the proposed methods.

A Dynamic Resource Scheduling Method Based on Fuzzy Control Theory in Cloud Environment

Directory of Open Access Journals (Sweden)

Zhijia Chen

2015-01-01

Full Text Available The resources in cloud environment have features such as large-scale, diversity, and heterogeneity. Moreover, the user requirements for cloud computing resources are commonly characterized by uncertainty and imprecision. Hereby, to improve the quality of cloud computing service, not merely should the traditional standards such as cost and bandwidth be satisfied, but also particular emphasis should be laid on some extended standards such as system friendliness. This paper proposes a dynamic resource scheduling method based on fuzzy control theory. Firstly, the resource requirements prediction model is established. Then the relationships between resource availability and the resource requirements are concluded. Afterwards fuzzy control theory is adopted to realize a friendly match between user needs and resources availability. Results show that this approach improves the resources scheduling efficiency and the quality of service (QoS of cloud computing.

Improved Space Surveillance Network (SSN) Scheduling using Artificial Intelligence Techniques

Science.gov (United States)

Stottler, D.

There are close to 20,000 cataloged manmade objects in space, the large majority of which are not active, functioning satellites. These are tracked by phased array and mechanical radars and ground and space-based optical telescopes, collectively known as the Space Surveillance Network (SSN). A better SSN schedule of observations could, using exactly the same legacy sensor resources, improve space catalog accuracy through more complementary tracking, provide better responsiveness to real-time changes, better track small debris in low earth orbit (LEO) through efficient use of applicable sensors, efficiently track deep space (DS) frequent revisit objects, handle increased numbers of objects and new types of sensors, and take advantage of future improved communication and control to globally optimize the SSN schedule. We have developed a scheduling algorithm that takes as input the space catalog and the associated covariance matrices and produces a globally optimized schedule for each sensor site as to what objects to observe and when. This algorithm is able to schedule more observations with the same sensor resources and have those observations be more complementary, in terms of the precision with which each orbit metric is known, to produce a satellite observation schedule that, when executed, minimizes the covariances across the entire space object catalog. If used operationally, the results would be significantly increased accuracy of the space catalog with fewer lost objects with the same set of sensor resources. This approach inherently can also trade-off fewer high priority tasks against more lower-priority tasks, when there is benefit in doing so. Currently the project has completed a prototyping and feasibility study, using open source data on the SSN's sensors, that showed significant reduction in orbit metric covariances. The algorithm techniques and results will be discussed along with future directions for the research.

A genetic algorithm-based job scheduling model for big data analytics.

Science.gov (United States)

Lu, Qinghua; Li, Shanshan; Zhang, Weishan; Zhang, Lei

Big data analytics (BDA) applications are a new category of software applications that process large amounts of data using scalable parallel processing infrastructure to obtain hidden value. Hadoop is the most mature open-source big data analytics framework, which implements the MapReduce programming model to process big data with MapReduce jobs. Big data analytics jobs are often continuous and not mutually separated. The existing work mainly focuses on executing jobs in sequence, which are often inefficient and consume high energy. In this paper, we propose a genetic algorithm-based job scheduling model for big data analytics applications to improve the efficiency of big data analytics. To implement the job scheduling model, we leverage an estimation module to predict the performance of clusters when executing analytics jobs. We have evaluated the proposed job scheduling model in terms of feasibility and accuracy.

Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid

OpenAIRE

Chen, Ruey-Maw; Wang, Chuin-Mu

2011-01-01

The task scheduling problem has been widely studied for assigning resources to tasks in heterogeneous grid environment. Effective task scheduling is an important issue for the performance of grid computing. Meanwhile, the task scheduling problem is an NP-complete problem. Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO) metaheuristic approach to efficiently solve the task scheduling problems in grid. Meanwhile, two promising heuristics based on multimo...

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

Directory of Open Access Journals (Sweden)

Maryam Mousavi

Full Text Available Flexible manufacturing system (FMS enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs. An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA, particle swarm optimization (PSO, and hybrid GA-PSO to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

Science.gov (United States)

Mousavi, Maryam; Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

2017-01-01

Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

Optimization of Task Scheduling Algorithm through QoS Parameters for Cloud Computing

Directory of Open Access Journals (Sweden)

Monika

2016-01-01

Full Text Available Cloud computing is an incipient innovation which broadly spreads among analysts. It furnishes clients with foundation, stage and programming as enhancement which is easily available by means of web. A cloud is a sort of parallel and conveyed framework comprising of a gathering of virtualized PCs that are utilized to execute various tasks to accomplish good execution time, accomplish due date and usage of its assets. The scheduling issue can be seen as the finding an ideal task of assignments over the accessible arrangement of assets with the goal that we can accomplish the wanted objectives for tasks. This paper presents an optimal algorithm for scheduling tasks to get their waiting time as a QoS parameter. The algorithm is simulated using Cloudsim simulator and experiments are carried out to help clients to make sense of the bottleneck of utilizing no. of virtual machine parallely.

VM Capacity-Aware Scheduling within Budget Constraints in IaaS Clouds.

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Vasileios Thanasias

Full Text Available Recently, cloud computing has drawn significant attention from both industry and academia, bringing unprecedented changes to computing and information technology. The infrastructure-as-a-Service (IaaS model offers new abilities such as the elastic provisioning and relinquishing of computing resources in response to workload fluctuations. However, because the demand for resources dynamically changes over time, the provisioning of resources in a way that a given budget is efficiently utilized while maintaining a sufficing performance remains a key challenge. This paper addresses the problem of task scheduling and resource provisioning for a set of tasks running on IaaS clouds; it presents novel provisioning and scheduling algorithms capable of executing tasks within a given budget, while minimizing the slowdown due to the budget constraint. Our simulation study demonstrates a substantial reduction up to 70% in the overall task slowdown rate by the proposed algorithms.

VM Capacity-Aware Scheduling within Budget Constraints in IaaS Clouds.

Science.gov (United States)

Thanasias, Vasileios; Lee, Choonhwa; Hanif, Muhammad; Kim, Eunsam; Helal, Sumi

2016-01-01

Recently, cloud computing has drawn significant attention from both industry and academia, bringing unprecedented changes to computing and information technology. The infrastructure-as-a-Service (IaaS) model offers new abilities such as the elastic provisioning and relinquishing of computing resources in response to workload fluctuations. However, because the demand for resources dynamically changes over time, the provisioning of resources in a way that a given budget is efficiently utilized while maintaining a sufficing performance remains a key challenge. This paper addresses the problem of task scheduling and resource provisioning for a set of tasks running on IaaS clouds; it presents novel provisioning and scheduling algorithms capable of executing tasks within a given budget, while minimizing the slowdown due to the budget constraint. Our simulation study demonstrates a substantial reduction up to 70% in the overall task slowdown rate by the proposed algorithms.

Day-ahead resource scheduling of a renewable energy based virtual power plant

International Nuclear Information System (INIS)

Zamani, Ali Ghahgharaee; Zakariazadeh, Alireza; Jadid, Shahram

2016-01-01

Highlights: • Simultaneous energy and reserve scheduling of a VPP. • Aggregate uncertainties of electricity prices, renewable generation and load demand. • Develop a stochastic scheduling model using the point estimate method. - Abstract: The evolution of energy markets is accelerating in the direction of a greater reliance upon distributed energy resources (DERs). To manage this increasing two-way complexity, virtual power plants (VPPs) are being deployed today all over the world. In this paper, a probabilistic model for optimal day ahead scheduling of electrical and thermal energy resources in a VPP is proposed where participation of energy storage systems and demand response programs (DRPs) are also taken into account. In the proposed model, energy and reserve is simultaneously scheduled considering the uncertainties of market prices, electrical demand and intermittent renewable power generation. The Point Estimate Method (PEM) is applied in order to model the uncertainties of VPP’s scheduling problem. Moreover, the optimal reserve scheduling of VPP is presented which efficiently decreases VPP’s risk facing the unexpected fluctuations of uncertain parameters at the power delivery time. The results demonstrated that implementation of demand response programs (DRPs) would decrease total operation costs of VPP as well as its dependency on the upstream network.

Real-Time Scheduling for Preventing Information Leakage with Preemption Overheads

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BAEK, H.

2017-05-01

Full Text Available Real-time systems (RTS are characterized by tasks executing in a timely manner to meet its deadlines as a real-time constraint. Most studies of RTS have focused on these criteria as primary design points. However, recent increases in security threats to various real-time systems have shown that enhanced security support must be included as an important design point, retro-fitting such support to existing systems as necessary. In this paper, we propose a new pre-flush technique referred to as flush task reservation for FP scheduling (FTR-FP to conditionally sanitize the state of resources shared by real-time tasks by invoking a flush task (FT in order to mitigate information leakage/corruption of real-time systems. FTR-FP extends existing works exploiting FTs to be applicable more general scheduling algorithms and security model. We also propose modifications to existing real-time scheduling algorithms to implement a pre-flush technique as a security constraint, and analysis technique to verify schedulability of the real-time scheduling. For better analytic capability, our analysis technique provides a count of the precise number of preemptions that a task experiences offline. Our evaluation results demonstrate that our proposed schedulability analysis improves the performance of existing scheduling algorithms in terms of schedulability and preemption cost.

A Reputation-based Distributed District Scheduling Algorithm for Smart Grids

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D. Borra

2015-05-01

Full Text Available In this paper we develop and test a distributed algorithm providing Energy Consumption Schedules (ECS in smart grids for a residential district. The goal is to achieve a given aggregate load prole. The NP-hard constrained optimization problem reduces to a distributed unconstrained formulation by means of Lagrangian Relaxation technique, and a meta-heuristic algorithm based on a Quantum inspired Particle Swarm with Levy flights. A centralized iterative reputation-reward mechanism is proposed for end-users to cooperate to avoid power peaks and reduce global overload, based on random distributions simulating human behaviors and penalties on the eective ECS diering from the suggested ECS. Numerical results show the protocols eectiveness.

Two-stage stochastic day-ahead optimal resource scheduling in a distribution network with intensive use of distributed energy resources

DEFF Research Database (Denmark)

Sousa, Tiago; Ghazvini, Mohammad Ali Fotouhi; Morais, Hugo

2015-01-01

The integration of renewable sources and electric vehicles will introduce new uncertainties to the optimal resource scheduling, namely at the distribution level. These uncertainties are mainly originated by the power generated by renewables sources and by the electric vehicles charge requirements....... This paper proposes a two-state stochastic programming approach to solve the day-ahead optimal resource scheduling problem. The case study considers a 33-bus distribution network with 66 distributed generation units and 1000 electric vehicles....

Improved teaching-learning-based and JAYA optimization algorithms for solving flexible flow shop scheduling problems

Science.gov (United States)

Buddala, Raviteja; Mahapatra, Siba Sankar

2017-11-01

Flexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having `g' operations is performed on `g' operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem becomes a flexible flow shop problem (FFSP). FFSP which contains all the complexities involved in a simple flow shop and parallel machine scheduling problems is a well-known NP-hard (Non-deterministic polynomial time) problem. Owing to high computational complexity involved in solving these problems, it is not always possible to obtain an optimal solution in a reasonable computation time. To obtain near-optimal solutions in a reasonable computation time, a large variety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solving FFSP is rather tricky and time consuming. To address this limitation, teaching-learning-based optimization (TLBO) and JAYA algorithm are chosen for the study because these are not only recent meta-heuristics but they do not require tuning of algorithm-specific parameters. Although these algorithms seem to be elegant, they lose solution diversity after few iterations and get trapped at the local optima. To alleviate such drawback, a new local search procedure is proposed in this paper to improve the solution quality. Further, mutation strategy (inspired from genetic algorithm) is incorporated in the basic algorithm to maintain solution diversity in the population. Computational experiments have been conducted on standard benchmark problems to calculate makespan and computational time. It is found that the rate of convergence of TLBO is superior to JAYA. From the results, it is found that TLBO and JAYA outperform many algorithms reported in the literature and can be treated as efficient methods for solving the FFSP.

Semi-online preemptive scheduling: one algorithm for all variants

Czech Academy of Sciences Publication Activity Database

Ebenlendr, Tomáš; Sgall, J.

2011-01-01

Roč. 48, č. 3 (2011), s. 577-613 ISSN 1432-4350. [26th International Symposium on Theoretical Aspects of Computer Science. Freiburg, 26.02.2009-28.02.2009] R&D Projects: GA AV ČR IAA100190902; GA MŠk(CZ) 1M0545 Institutional research plan: CEZ:AV0Z10190503 Keywords : online algorithms * scheduling * preemption * linear program Subject RIV: BA - General Mathematics Impact factor: 0.442, year: 2011 http://www.springerlink.com/content/k66u6tv1l7731654/

Scheduling in Heterogeneous Grid Environments: The Effects of DataMigration

Energy Technology Data Exchange (ETDEWEB)

Oliker, Leonid; Biswas, Rupak; Shan, Hongzhang; Smith, Warren

2004-01-01

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. However, a number of major technical hurdles must be overcome before this goal can be fully realized. One problem critical to the effective utilization of computational grids is efficient job scheduling. Our prior work addressed this challenge by defining a grid scheduling architecture and several job migration strategies. The focus of this study is to explore the impact of data migration under a variety of demanding grid conditions. We evaluate our grid scheduling algorithms by simulating compute servers, various groupings of servers into sites, and inter-server networks, using real workloads obtained from leading supercomputing centers. Several key performance metrics are used to compare the behavior of our algorithms against reference local and centralized scheduling schemes. Results show the tremendous benefits of grid scheduling, even in the presence of input/output data migration - while highlighting the importance of utilizing communication-aware scheduling schemes.

Rolling scheduling of electric power system with wind power based on improved NNIA algorithm

Science.gov (United States)

Xu, Q. S.; Luo, C. J.; Yang, D. J.; Fan, Y. H.; Sang, Z. X.; Lei, H.

2017-11-01

This paper puts forth a rolling modification strategy for day-ahead scheduling of electric power system with wind power, which takes the operation cost increment of unit and curtailed wind power of power grid as double modification functions. Additionally, an improved Nondominated Neighbor Immune Algorithm (NNIA) is proposed for solution. The proposed rolling scheduling model has further improved the operation cost of system in the intra-day generation process, enhanced the system’s accommodation capacity of wind power, and modified the key transmission section power flow in a rolling manner to satisfy the security constraint of power grid. The improved NNIA algorithm has defined an antibody preference relation model based on equal incremental rate, regulation deviation constraints and maximum & minimum technical outputs of units. The model can noticeably guide the direction of antibody evolution, and significantly speed up the process of algorithm convergence to final solution, and enhance the local search capability.

Chemotherapy appointment scheduling under uncertainty using mean-risk stochastic integer programming.

Science.gov (United States)

Alvarado, Michelle; Ntaimo, Lewis

2018-03-01

Oncology clinics are often burdened with scheduling large volumes of cancer patients for chemotherapy treatments under limited resources such as the number of nurses and chairs. These cancer patients require a series of appointments over several weeks or months and the timing of these appointments is critical to the treatment's effectiveness. Additionally, the appointment duration, the acuity levels of each appointment, and the availability of clinic nurses are uncertain. The timing constraints, stochastic parameters, rising treatment costs, and increased demand of outpatient oncology clinic services motivate the need for efficient appointment schedules and clinic operations. In this paper, we develop three mean-risk stochastic integer programming (SIP) models, referred to as SIP-CHEMO, for the problem of scheduling individual chemotherapy patient appointments and resources. These mean-risk models are presented and an algorithm is devised to improve computational speed. Computational results were conducted using a simulation model and results indicate that the risk-averse SIP-CHEMO model with the expected excess mean-risk measure can decrease patient waiting times and nurse overtime when compared to deterministic scheduling algorithms by 42 % and 27 %, respectively.

Job schedulers for Big data processing in Hadoop environment: testing real-life schedulers using benchmark programs

Directory of Open Access Journals (Sweden)

Mohd Usama

2017-11-01

Full Text Available At present, big data is very popular, because it has proved to be much successful in many fields such as social media, E-commerce transactions, etc. Big data describes the tools and technologies needed to capture, manage, store, distribute, and analyze petabyte or larger-sized datasets having different structures with high speed. Big data can be structured, unstructured, or semi structured. Hadoop is an open source framework that is used to process large amounts of data in an inexpensive and efficient way, and job scheduling is a key factor for achieving high performance in big data processing. This paper gives an overview of big data and highlights the problems and challenges in big data. It then highlights Hadoop Distributed File System (HDFS, Hadoop MapReduce, and various parameters that affect the performance of job scheduling algorithms in big data such as Job Tracker, Task Tracker, Name Node, Data Node, etc. The primary purpose of this paper is to present a comparative study of job scheduling algorithms along with their experimental results in Hadoop environment. In addition, this paper describes the advantages, disadvantages, features, and drawbacks of various Hadoop job schedulers such as FIFO, Fair, capacity, Deadline Constraints, Delay, LATE, Resource Aware, etc, and provides a comparative study among these schedulers.

Missed deadline notification in best-effort schedulers

Science.gov (United States)

Banachowski, Scott A.; Wu, Joel; Brandt, Scott A.

2003-12-01

It is common to run multimedia and other periodic, soft real-time applications on general-purpose computer systems. These systems use best-effort scheduling algorithms that cannot guarantee applications will receive responsive scheduling to meet deadline or timing requirements. We present a simple mechanism called Missed Deadline Notification (MDN) that allows applications to notify the system when they do not receive their desired level of responsiveness. Consisting of a single system call with no arguments, this simple interface allows the operating system to provide better support for soft real-time applications without any a priori information about their timing or resource needs. We implemented MDN in three different schedulers: Linux, BEST, and BeRate. We describe these implementations and their performance when running real-time applications and discuss policies to prevent applications from abusing MDN to gain extra resources.

An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

Directory of Open Access Journals (Sweden)

Yunqing Rao

2013-01-01

Full Text Available For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.

Science.gov (United States)

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony--hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

Dynamic workload balancing of parallel applications with user-level scheduling on the Grid

CERN Document Server

Korkhov, Vladimir V; Krzhizhanovskaya, Valeria V

2009-01-01

This paper suggests a hybrid resource management approach for efficient parallel distributed computing on the Grid. It operates on both application and system levels, combining user-level job scheduling with dynamic workload balancing algorithm that automatically adapts a parallel application to the heterogeneous resources, based on the actual resource parameters and estimated requirements of the application. The hybrid environment and the algorithm for automated load balancing are described, the influence of resource heterogeneity level is measured, and the speedup achieved with this technique is demonstrated for different types of applications and resources.

The Combinatorial Multi-Mode Resource Constrained Multi-Project Scheduling Problem

Directory of Open Access Journals (Sweden)

Denis Pinha

2016-11-01

Full Text Available This paper presents the formulation and solution of the Combinatorial Multi-Mode Resource Constrained Multi-Project Scheduling Problem. The focus of the proposed method is not on finding a single optimal solution, instead on presenting multiple feasible solutions, with cost and duration information to the project manager. The motivation for developing such an approach is due in part to practical situations where the definition of optimal changes on a regular basis. The proposed approach empowers the project manager to determine what is optimal, on a given day, under the current constraints, such as, change of priorities, lack of skilled worker. The proposed method utilizes a simulation approach to determine feasible solutions, under the current constraints. Resources can be non-consumable, consumable, or doubly constrained. The paper also presents a real-life case study dealing with scheduling of ship repair activities.

An Algorithm for the Weighted Earliness-Tardiness Unconstrained Project Scheduling Problem

Science.gov (United States)

Afshar Nadjafi, Behrouz; Shadrokh, Shahram

This research considers a project scheduling problem with the object of minimizing weighted earliness-tardiness penalty costs, taking into account a deadline for the project and precedence relations among the activities. An exact recursive method has been proposed for solving the basic form of this problem. We present a new depth-first branch and bound algorithm for extended form of the problem, which time value of money is taken into account by discounting the cash flows. The algorithm is extended with two bounding rules in order to reduce the size of the branch and bound tree. Finally, some test problems are solved and computational results are reported.

Optimization of operating schedule of machines in granite industry using evolutionary algorithms

International Nuclear Information System (INIS)

Loganthurai, P.; Rajasekaran, V.; Gnanambal, K.

2014-01-01

Highlights: • Operating time of machines in granite industries was studied. • Operating time has been optimized using evolutionary algorithms such as PSO, DE. • The maximum demand has been reduced. • Hence the electricity cost of the industry and feeder stress have been reduced. - Abstract: Electrical energy consumption cost plays an important role in the production cost of any industry. The electrical energy consumption cost is calculated as two part tariff, the first part is maximum demand cost and the second part is energy consumption cost or unit cost (kW h). The maximum demand cost can be reduced without affecting the production. This paper focuses on the reduction of maximum demand by proper operating schedule of major equipments. For this analysis, various granite industries are considered. The major equipments in granite industries are cutting machine, polishing machine and compressor. To reduce the maximum demand, the operating time of polishing machine is rescheduled by optimization techniques such as Differential Evolution (DE) and particle swarm optimization (PSO). The maximum demand costs are calculated before and after rescheduling. The results show that if the machines are optimally operated, the cost is reduced. Both DE and PSO algorithms reduce the maximum demand cost at the same rate for all the granite industries. However, the optimum scheduling obtained by DE reduces the feeder power flow than the PSO scheduling

Comparison of a constraint directed search to a genetic algorithm in a scheduling application

International Nuclear Information System (INIS)

Abbott, L.

1993-01-01

Scheduling plutonium containers for blending is a time-intensive operation. Several constraints must be taken into account; including the number of containers in a dissolver run, the size of each dissolver run, and the size and target purity of the blended mixture formed from these runs. Two types of algorithms have been used to solve this problem: a constraint directed search and a genetic algorithm. This paper discusses the implementation of these two different approaches to the problem and the strengths and weaknesses of each algorithm

Reasoning abstractly about resources

Science.gov (United States)

Clement, B.; Barrett, A.

2001-01-01

r describes a way to schedule high level activities before distributing them across multiple rovers in order to coordinate the resultant use of shared resources regardless of how each rover decides how to perform its activities. We present an algorithm for summarizing the metric resource requirements of an abstract activity based n the resource usages of its potential refinements.

Generator scheduling under competitive environment using Memory Management Algorithm

Directory of Open Access Journals (Sweden)

A. Amudha

2013-09-01

Full Text Available This paper presents a new approach for Real-Time Application of Profit Based Unit Commitment using Memory Management Algorithm. The main objective of the restructured system is to maximize its own profit without the responsibility of satisfying the forecasted demand. The Profit Based Unit Commitment (PBUC is solved by Memory Management Algorithm (MMA in Real-Time Application. MMA approach is introduced in this paper considering power and reserve generation. The proposed method MMA uses Best Fit and Worst Fit allocation for generator scheduling in order to receive the maximum profit by considering the softer demand. Also, this method gives an idea regarding how much power and reserve should be sold in markets. The proposed approach has been tested on a power system with 2, 3, and 10 generating units. Simulation results of the proposed approach have been compared with the existing methods.

Research on Arrival/Departure Scheduling of Flights on Multirunways Based on Genetic Algorithm

Directory of Open Access Journals (Sweden)

Hang Zhou

2014-01-01

Full Text Available Aiming at the phenomenon of a large number of flight delays in the terminal area makes a reasonable scheduling for the approach and departure flights, which will minimize flight delay losses and improve runway utilization. This paper considered factors such as operating conditions and safety interval of multi runways; the maximum throughput and minimum flight delay losses as well as robustness were taken as objective functions; the model of optimization scheduling of approach and departure flights was established. Finally, the genetic algorithm was introduced to solve the model. The results showed that, in the program whose advance is not counted as a loss, its runway throughput is improved by 18.4%, the delay losses are reduced by 85.8%, and the robustness is increased by 20% compared with the results of FCFS (first come first served algorithm, while, compared with the program whose advance is counted as a loss, the runway throughput is improved by 15.16%, flight delay losses are decreased by 75.64%, and the robustness is also increased by 20%. The algorithm can improve the efficiency and reduce delay losses effectively and reduce the workload of controllers, thereby improving economic results.

Constraint-based job shop scheduling with ILOG SCHEDULER

NARCIS (Netherlands)

Nuijten, W.P.M.; Le Pape, C.

1998-01-01

We introduce constraint-based scheduling and discuss its main principles. An approximation algorithm based on tree search is developed for the job shop scheduling problem using ILOG SCHEDULER. A new way of calculating lower bounds on the makespan of the job shop scheduling problem is presented and

Algorithm for complete enumeration based on a stroke graph to solve the supply network configuration and operations scheduling problem

Directory of Open Access Journals (Sweden)

Julien Maheut

2013-07-01

Full Text Available Purpose: The purpose of this paper is to present an algorithm that solves the supply network configuration and operations scheduling problem in a mass customization company that faces alternative operations for one specific tool machine order in a multiplant context. Design/methodology/approach: To achieve this objective, the supply chain network configuration and operations scheduling problem is presented. A model based on stroke graphs allows the design of an algorithm that enumerates all the feasible solutions. The algorithm considers the arrival of a new customized order proposal which has to be inserted into a scheduled program. A selection function is then used to choose the solutions to be simulated in a specific simulation tool implemented in a Decision Support System. Findings and Originality/value: The algorithm itself proves efficient to find all feasible solutions when alternative operations must be considered. The stroke structure is successfully used to schedule operations when considering more than one manufacturing and supply option in each step. Research limitations/implications: This paper includes only the algorithm structure for a one-by-one, sequenced introduction of new products into the list of units to be manufactured. Therefore, the lotsizing process is done on a lot-per-lot basis. Moreover, the validation analysis is done through a case study and no generalization can be done without risk. Practical implications: The result of this research would help stakeholders to determine all the feasible and practical solutions for their problem. It would also allow to assessing the total costs and delivery times of each solution. Moreover, the Decision Support System proves useful to assess alternative solutions. Originality/value: This research offers a simple algorithm that helps solve the supply network configuration problem and, simultaneously, the scheduling problem by considering alternative operations. The proposed system

Scheduling Two-Sided Transformations Using Tile Algorithms on Multicore Architectures

Directory of Open Access Journals (Sweden)

Hatem Ltaief

2010-01-01

Full Text Available The objective of this paper is to describe, in the context of multicore architectures, three different scheduler implementations for the two-sided linear algebra transformations, in particular the Hessenberg and Bidiagonal reductions which are the first steps for the standard eigenvalue problems and the singular value decompositions respectively. State-of-the-art dense linear algebra softwares, such as the LAPACK and ScaLAPACK libraries, suffer performance losses on multicore processors due to their inability to fully exploit thread-level parallelism. At the same time the fine-grain dataflow model gains popularity as a paradigm for programming multicore architectures. Buttari et al. (Parellel Comput. Syst. Appl. 35 (2009, 38–53 introduced the concept of tile algorithms in which parallelism is no longer hidden inside Basic Linear Algebra Subprograms but is brought to the fore to yield much better performance. Along with efficient scheduling mechanisms for data-driven execution, these tile two-sided reductions achieve high performance computing by reaching up to 75% of the DGEMM peak on a 12000×12000 matrix with 16 Intel Tigerton 2.4 GHz processors. The main drawback of the tile algorithms approach for two-sided transformations is that the full reduction cannot be obtained in one stage. Other methods have to be considered to further reduce the band matrices to the required forms.

PLAStiCC: Predictive Look-Ahead Scheduling for Continuous dataflows on Clouds

Energy Technology Data Exchange (ETDEWEB)

Kumbhare, Alok [Univ. of Southern California, Los Angeles, CA (United States); Simmhan, Yogesh [Indian Inst. of Technology (IIT), Bangalore (India); Prasanna, Viktor K. [Univ. of Southern California, Los Angeles, CA (United States)

2014-05-27

Scalable stream processing and continuous dataflow systems are gaining traction with the rise of big data due to the need for processing high velocity data in near real time. Unlike batch processing systems such as MapReduce and workflows, static scheduling strategies fall short for continuous dataflows due to the variations in the input data rates and the need for sustained throughput. The elastic resource provisioning of cloud infrastructure is valuable to meet the changing resource needs of such continuous applications. However, multi-tenant cloud resources introduce yet another dimension of performance variability that impacts the application’s throughput. In this paper we propose PLAStiCC, an adaptive scheduling algorithm that balances resource cost and application throughput using a prediction-based look-ahead approach. It not only addresses variations in the input data rates but also the underlying cloud infrastructure. In addition, we also propose several simpler static scheduling heuristics that operate in the absence of accurate performance prediction model. These static and adaptive heuristics are evaluated through extensive simulations using performance traces obtained from public and private IaaS clouds. Our results show an improvement of up to 20% in the overall profit as compared to the reactive adaptation algorithm.

Scheduling the scheduling task : a time management perspective on scheduling

NARCIS (Netherlands)

Larco Martinelli, J.A.; Wiers, V.C.S.; Fransoo, J.C.

2013-01-01

Time is the most critical resource at the disposal of schedulers. Hence, an adequate management of time from the schedulers may impact positively on the scheduler’s productivity and responsiveness to uncertain scheduling environments. This paper presents a field study of how schedulers make use of

Downlink scheduling using non-orthogonal uplink beams

KAUST Repository

Eltayeb, Mohammed E.

2014-04-01

Opportunistic schedulers rely on the feedback of the channel state information of users in order to perform user selection and downlink scheduling. This feedback increases with the number of users, and can lead to inefficient use of network resources and scheduling delays. We tackle the problem of feedback design, and propose a novel class of nonorthogonal codes to feed back channel state information. Users with favorable channel conditions simultaneously transmit their channel state information via non-orthogonal beams to the base station. The proposed formulation allows the base station to identify the strong users via a simple correlation process. After deriving the minimum required code length and closed-form expressions for the feedback load and downlink capacity, we show that i) the proposed algorithm reduces the feedback load while matching the achievable rate of full feedback algorithms operating over a noiseless feedback channel, and ii) the proposed codes are superior to the Gaussian codes.

Downlink scheduling using non-orthogonal uplink beams

KAUST Repository

Eltayeb, Mohammed E.; Al-Naffouri, Tareq Y.; Bahrami, Hamid Reza Talesh

2014-01-01

Opportunistic schedulers rely on the feedback of the channel state information of users in order to perform user selection and downlink scheduling. This feedback increases with the number of users, and can lead to inefficient use of network resources and scheduling delays. We tackle the problem of feedback design, and propose a novel class of nonorthogonal codes to feed back channel state information. Users with favorable channel conditions simultaneously transmit their channel state information via non-orthogonal beams to the base station. The proposed formulation allows the base station to identify the strong users via a simple correlation process. After deriving the minimum required code length and closed-form expressions for the feedback load and downlink capacity, we show that i) the proposed algorithm reduces the feedback load while matching the achievable rate of full feedback algorithms operating over a noiseless feedback channel, and ii) the proposed codes are superior to the Gaussian codes.

Computing Bounds on Resource Levels for Flexible Plans

Science.gov (United States)

Muscvettola, Nicola; Rijsman, David

2009-01-01

A new algorithm efficiently computes the tightest exact bound on the levels of resources induced by a flexible activity plan (see figure). Tightness of bounds is extremely important for computations involved in planning because tight bounds can save potentially exponential amounts of search (through early backtracking and detection of solutions), relative to looser bounds. The bound computed by the new algorithm, denoted the resource-level envelope, constitutes the measure of maximum and minimum consumption of resources at any time for all fixed-time schedules in the flexible plan. At each time, the envelope guarantees that there are two fixed-time instantiations one that produces the minimum level and one that produces the maximum level. Therefore, the resource-level envelope is the tightest possible resource-level bound for a flexible plan because any tighter bound would exclude the contribution of at least one fixed-time schedule. If the resource- level envelope can be computed efficiently, one could substitute looser bounds that are currently used in the inner cores of constraint-posting scheduling algorithms, with the potential for great improvements in performance. What is needed to reduce the cost of computation is an algorithm, the measure of complexity of which is no greater than a low-degree polynomial in N (where N is the number of activities). The new algorithm satisfies this need. In this algorithm, the computation of resource-level envelopes is based on a novel combination of (1) the theory of shortest paths in the temporal-constraint network for the flexible plan and (2) the theory of maximum flows for a flow network derived from the temporal and resource constraints. The measure of asymptotic complexity of the algorithm is O(N O(maxflow(N)), where O(x) denotes an amount of computing time or a number of arithmetic operations proportional to a number of the order of x and O(maxflow(N)) is the measure of complexity (and thus of cost) of a maximumflow

Adaptive Subframe Partitioning and Efficient Packet Scheduling in OFDMA Cellular System with Fixed Decode-and-Forward Relays

Science.gov (United States)

Wang, Liping; Ji, Yusheng; Liu, Fuqiang

The integration of multihop relays with orthogonal frequency-division multiple access (OFDMA) cellular infrastructures can meet the growing demands for better coverage and higher throughput. Resource allocation in the OFDMA two-hop relay system is more complex than that in the conventional single-hop OFDMA system. With time division between transmissions from the base station (BS) and those from relay stations (RSs), fixed partitioning of the BS subframe and RS subframes can not adapt to various traffic demands. Moreover, single-hop scheduling algorithms can not be used directly in the two-hop system. Therefore, we propose a semi-distributed algorithm called ASP to adjust the length of every subframe adaptively, and suggest two ways to extend single-hop scheduling algorithms into multihop scenarios: link-based and end-to-end approaches. Simulation results indicate that the ASP algorithm increases system utilization and fairness. The max carrier-to-interference ratio (Max C/I) and proportional fairness (PF) scheduling algorithms extended using the end-to-end approach obtain higher throughput than those using the link-based approach, but at the expense of more overhead for information exchange between the BS and RSs. The resource allocation scheme using ASP and end-to-end PF scheduling achieves a tradeoff between system throughput maximization and fairness.

A hybrid quantum-inspired genetic algorithm for multiobjective flow shop scheduling.

Science.gov (United States)

Li, Bin-Bin; Wang, Ling

2007-06-01

This paper proposes a hybrid quantum-inspired genetic algorithm (HQGA) for the multiobjective flow shop scheduling problem (FSSP), which is a typical NP-hard combinatorial optimization problem with strong engineering backgrounds. On the one hand, a quantum-inspired GA (QGA) based on Q-bit representation is applied for exploration in the discrete 0-1 hyperspace by using the updating operator of quantum gate and genetic operators of Q-bit. Moreover, random-key representation is used to convert the Q-bit representation to job permutation for evaluating the objective values of the schedule solution. On the other hand, permutation-based GA (PGA) is applied for both performing exploration in permutation-based scheduling space and stressing exploitation for good schedule solutions. To evaluate solutions in multiobjective sense, a randomly weighted linear-sum function is used in QGA, and a nondominated sorting technique including classification of Pareto fronts and fitness assignment is applied in PGA with regard to both proximity and diversity of solutions. To maintain the diversity of the population, two trimming techniques for population are proposed. The proposed HQGA is tested based on some multiobjective FSSPs. Simulation results and comparisons based on several performance metrics demonstrate the effectiveness of the proposed HQGA.

Voltage scheduling for low power/energy

Science.gov (United States)

Manzak, Ali

2001-07-01

Power considerations have become an increasingly dominant factor in the design of both portable and desk-top systems. An effective way to reduce power consumption is to lower the supply voltage since voltage is quadratically related to power. This dissertation considers the problem of lowering the supply voltage at (i) the system level and at (ii) the behavioral level. At the system level, the voltage of the variable voltage processor is dynamically changed with the work load. Processors with limited sized buffers as well as those with very large buffers are considered. Given the task arrival times, deadline times, execution times, periods and switching activities, task scheduling algorithms that minimize energy or peak power are developed for the processors equipped with very large buffers. A relation between the operating voltages of the tasks for minimum energy/power is determined using the Lagrange multiplier method, and an iterative algorithm that utilizes this relation is developed. Experimental results show that the voltage assignment obtained by the proposed algorithm is very close (0.1% error) to that of the optimal energy assignment and the optimal peak power (1% error) assignment. Next, on-line and off-fine minimum energy task scheduling algorithms are developed for processors with limited sized buffers. These algorithms have polynomial time complexity and present optimal (off-line) and close-to-optimal (on-line) solutions. A procedure to calculate the minimum buffer size given information about the size of the task (maximum, minimum), execution time (best case, worst case) and deadlines is also presented. At the behavioral level, resources operating at multiple voltages are used to minimize power while maintaining the throughput. Such a scheme has the advantage of allowing modules on the critical paths to be assigned to the highest voltage levels (thus meeting the required timing constraints) while allowing modules on non-critical paths to be assigned

An Optimal Scheduling Algorithm with a Competitive Factor for Real-Time Systems

Science.gov (United States)

1991-07-29

real - time systems in which the value of a task is proportional to its computation time. The system obtains the value of a given task if the task completes by its deadline. Otherwise, the system obtains no value for the task. When such a system is underloaded (i.e. there exists a schedule for which all tasks meet their deadlines), Dertouzos [6] showed that the earliest deadline first algorithm will achieve 100% of the possible value. We consider the case of a possibly overloaded system and present an algorithm which: 1. behaves like the earliest deadline first

Decoupling algorithms from schedules for easy optimization of image processing pipelines

OpenAIRE

Adams, Andrew; Paris, Sylvain; Levoy, Marc; Ragan-Kelley, Jonathan Millar; Amarasinghe, Saman P.; Durand, Fredo

2012-01-01

Using existing programming tools, writing high-performance image processing code requires sacrificing readability, portability, and modularity. We argue that this is a consequence of conflating what computations define the algorithm, with decisions about storage and the order of computation. We refer to these latter two concerns as the schedule, including choices of tiling, fusion, recomputation vs. storage, vectorization, and parallelism. We propose a representation for feed-forward imagi...

A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling

Directory of Open Access Journals (Sweden)

Qianwang Deng

2017-01-01

Full Text Available Flexible job-shop scheduling problem (FJSP is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II for multiobjective FJSP (MO-FJSP with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N, in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.

A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling.

Science.gov (United States)

Deng, Qianwang; Gong, Guiliang; Gong, Xuran; Zhang, Like; Liu, Wei; Ren, Qinghua

2017-01-01

Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N , in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.

Anesthesiology Nurse Scheduling using Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Leopoldo Altamirano

2012-02-01

Full Text Available In this article we present an approach designed to solve a real world problem: the Anesthesiology Nurse Scheduling Problem (ANSP at a public French hospital. The anesthesiology nurses are one of the most shared resources in the hospital and we attempt to find a fair/balanced schedule for them, taking into account a set of constraints and the nursesarsquo; stated preferences, concerning the different shifts. We propose a particle swarm optimization algorithm to solve the ANSP. Finally, we compare our technique with previous results obtained using integer programming.

A Scheduling Algorithm for the Distributed Student Registration System in Transaction-Intensive Environment

Science.gov (United States)

Li, Wenhao

2011-01-01

Distributed workflow technology has been widely used in modern education and e-business systems. Distributed web applications have shown cross-domain and cooperative characteristics to meet the need of current distributed workflow applications. In this paper, the author proposes a dynamic and adaptive scheduling algorithm PCSA (Pre-Calculated…

A note on simulated annealing to computer laboratory scheduling ...

African Journals Online (AJOL)

The concepts, principles and implementation of simulated Annealing as a modem heuristic technique is presented. Simulated Annealing algorithm is used in solving real life problem of Computer Laboratory scheduling in order to maximize the use of scarce and insufficient resources. KEY WORDS: Simulated Annealing ...

Low-Complexity Scheduling and Power Adaptation for Coordinated Cloud-Radio Access Networks

KAUST Repository

Douik, Ahmed; Dahrouj, Hayssam; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

2017-01-01

In practical wireless systems, the successful implementation of resource allocation techniques strongly depends on the algorithmic complexity. Consider a cloud-radio access network (CRAN), where the central cloud is responsible for scheduling

A Practical Framework to Study Low-Power Scheduling Algorithms on Real-Time and Embedded Systems

Directory of Open Access Journals (Sweden)

Jian (Denny Lin

2014-05-01

Full Text Available With the advanced technology used to design VLSI (Very Large Scale Integration circuits, low-power and energy-efficiency have played important roles for hardware and software implementation. Real-time scheduling is one of the fields that has attracted extensive attention to design low-power, embedded/real-time systems. The dynamic voltage scaling (DVS and CPU shut-down are the two most popular techniques used to design the algorithms. In this paper, we firstly review the fundamental advances in the research of energy-efficient, real-time scheduling. Then, a unified framework with a real Intel PXA255 Xscale processor, namely real-energy, is designed, which can be used to measure the real performance of the algorithms. We conduct a case study to evaluate several classical algorithms by using the framework. The energy efficiency and the quantitative difference in their performance, as well as the practical issues found in the implementation of these algorithms are discussed. Our experiments show a gap between the theoretical and real results. Our framework not only gives researchers a tool to evaluate their system designs, but also helps them to bridge this gap in their future works.

An improved Harmony Search algorithm for optimal scheduling of the diesel generators in oil rig platforms

Energy Technology Data Exchange (ETDEWEB)

Yadav, Parikshit; Kumar, Rajesh; Panda, S.K.; Chang, C.S. [Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576 (Singapore)

2011-02-15

Harmony Search (HS) algorithm is music based meta-heuristic optimization method which is analogous with the music improvisation process where musician continue to polish the pitches in order to obtain better harmony. The paper focuses on the optimal scheduling of the generators to reduce the fuel consumption in the oil rig platform. The accurate modeling of the specific fuel consumption is significant in this optimization. The specific fuel consumption has been modeled using cubic spline interpolation. The SFC curve is non-linear and discrete in nature, hence conventional methods fail to give optimal solution. HS algorithm has been used for optimal scheduling of the generators of both equal and unequal rating. Furthermore an Improved Harmony Search (IHS) method for generating new solution vectors that enhances accuracy and convergence rate of HS has been employed. The paper also focuses on the impacts of constant parameters on Harmony Search algorithm. Numerical results show that the IHS method has good convergence property. Moreover, the fuel consumption for IHS algorithm is lower when compared to HS and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems. (author)

An Improved Harmony Search algorithm for optimal scheduling of the diesel generators in oil rig platforms

International Nuclear Information System (INIS)

Yadav, Parikshit; Kumar, Rajesh; Panda, S.K.; Chang, C.S.

2011-01-01

Harmony Search (HS) algorithm is music based meta-heuristic optimization method which is analogous with the music improvisation process where musician continue to polish the pitches in order to obtain better harmony. The paper focuses on the optimal scheduling of the generators to reduce the fuel consumption in the oil rig platform. The accurate modeling of the specific fuel consumption is significant in this optimization. The specific fuel consumption has been modeled using cubic spline interpolation. The SFC curve is non-linear and discrete in nature, hence conventional methods fail to give optimal solution. HS algorithm has been used for optimal scheduling of the generators of both equal and unequal rating. Furthermore an Improved Harmony Search (IHS) method for generating new solution vectors that enhances accuracy and convergence rate of HS has been employed. The paper also focuses on the impacts of constant parameters on Harmony Search algorithm. Numerical results show that the IHS method has good convergence property. Moreover, the fuel consumption for IHS algorithm is lower when compared to HS and other heuristic or deterministic methods and is a powerful search algorithm for various engineering optimization problems.

Scheduling language and algorithm development study. Appendix: Study approach and activity summary

Science.gov (United States)

1974-01-01

The approach and organization of the study to develop a high level computer programming language and a program library are presented. The algorithm and problem modeling analyses are summarized. The approach used to identify and specify the capabilities required in the basic language is described. Results of the analyses used to define specifications for the scheduling module library are presented.

Distributed Algorithms for Time Optimal Reachability Analysis

DEFF Research Database (Denmark)

Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand

2016-01-01

. We propose distributed computing to accelerate time optimal reachability analysis. We develop five distributed state exploration algorithms, implement them in \\uppaal enabling it to exploit the compute resources of a dedicated model-checking cluster. We experimentally evaluate the implemented...... algorithms with four models in terms of their ability to compute near- or proven-optimal solutions, their scalability, time and memory consumption and communication overhead. Our results show that distributed algorithms work much faster than sequential algorithms and have good speedup in general.......Time optimal reachability analysis is a novel model based technique for solving scheduling and planning problems. After modeling them as reachability problems using timed automata, a real-time model checker can compute the fastest trace to the goal states which constitutes a time optimal schedule...

Preventive Maintenance Scheduling for Multicogeneration Plants with Production Constraints Using Genetic Algorithms

Directory of Open Access Journals (Sweden)

Khaled Alhamad

2015-01-01

Full Text Available This paper describes a method developed to schedule the preventive maintenance tasks of the generation and desalination units in separate and linked cogeneration plants provided that all the necessary maintenance and production constraints are satisfied. The proposed methodology is used to generate two preventing maintenance schedules, one for electricity and the other for distiller. Two types of crossover operators were adopted, 2-point and 4-point. The objective function of the model is to maximize the available number of operational units in each plant. The results obtained were satisfying the problem parameters. However, 4-point slightly produce better solution than 2-point ones for both electricity and water distiller. The performance as well as the effectiveness of the genetic algorithm in solving preventive maintenance scheduling is applied and tested on a real system of 21 units for electricity and 21 units for water. The results presented here show a great potential for utility applications for effective energy management over a time horizon of 52 weeks. The model presented is an effective decision tool that optimizes the solution of the maintenance scheduling problem for cogeneration plants under maintenance and production constraints.

A Novel Strategy Using Factor Graphs and the Sum-Product Algorithm for Satellite Broadcast Scheduling Problems

Science.gov (United States)

Chen, Jung-Chieh

This paper presents a low complexity algorithmic framework for finding a broadcasting schedule in a low-altitude satellite system, i. e., the satellite broadcast scheduling (SBS) problem, based on the recent modeling and computational methodology of factor graphs. Inspired by the huge success of the low density parity check (LDPC) codes in the field of error control coding, in this paper, we transform the SBS problem into an LDPC-like problem through a factor graph instead of using the conventional neural network approaches to solve the SBS problem. Based on a factor graph framework, the soft-information, describing the probability that each satellite will broadcast information to a terminal at a specific time slot, is exchanged among the local processing in the proposed framework via the sum-product algorithm to iteratively optimize the satellite broadcasting schedule. Numerical results show that the proposed approach not only can obtain optimal solution but also enjoys the low complexity suitable for integral-circuit implementation.

Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

International Nuclear Information System (INIS)

Dong Yun Kim; Poong Hyun Seong; .

1997-01-01

In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate gains, which minimize the error of system. The proposed algorithm can reduce the time and effort required for obtaining the fuzzy rules through the intelligent learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller. (author)

Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicle driving schedules

International Nuclear Information System (INIS)

Cardoso, G.; Stadler, M.; Bozchalui, M.C.; Sharma, R.; Marnay, C.; Barbosa-Póvoa, A.; Ferrão, P.

2014-01-01

The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained using the stochastic formulation of the problem. - Highlights: • This paper introduces a new EV aggregator model in the DER-CAM model and expands it with a stochastic formulation. • The model is used to analyze the impact of EVs in DER investment decisions in a large office building. • The uncertainty in EV driving patterns is considered through scenarios based on data from a daily commute driving survey. • Results indicate that EVs have a significant impact in optimal DER decisions, particularly when looking at short payback periods. • Furthermore, results indicate that uncertainty in EV driving schedules has little impact on DER investment decisions

FFTF and Advanced Reactors Transition Program Resource Loaded Schedule

Energy Technology Data Exchange (ETDEWEB)

GANTT, D.A.

2000-10-31

This Resource Load Schedule (RLS) addresses two missions. The Advanced Reactors Transition (ART) mission, funded by DOE-EM, is to transition assigned, surplus facilities to a safe and compliant, low-cost, stable, deactivated condition (requiring minimal surveillance and maintenance) pending eventual reuse or D&D. Facilities to be transitioned include the 309 Building Plutonium Recycle Test Reactor (PRTR) and Nuclear Energy Legacy facilities. This mission is funded through the Environmental Management (EM) Project Baseline Summary (PBS) RL-TP11, ''Advanced Reactors Transition.'' The second mission, the Fast Flux Test Facility (FFTF) Project, is funded through budget requests submitted to the Office of Nuclear Energy, Science and Technology (DOE-NE). The FFTF Project mission is maintaining the FFTF, the Fuels and Materials Examination Facility (FMEF), and affiliated 400 Area buildings in a safe and compliant standby condition. This mission is to preserve the condition of the plant hardware, software, and personnel in a manner not to preclude a plant restart. This revision of the Resource Loaded Schedule (RLS) is based upon the technical scope in the latest revision of the following project and management plans: Fast Flux Test Facility Standby Plan (Reference 1); Hanford Site Sodium Management Plan (Reference 2); and 309 Building Transition Plan (Reference 4). The technical scope, cost, and schedule baseline is also in agreement with the concurrent revision to the ART Fiscal Year (FY) 2001 Multi-Year Work Plan (MYWP), which is available in an electronic version (only) on the Hanford Local Area Network, within the ''Hanford Data Integrator (HANDI)'' application.

Multi-Satellite Observation Scheduling for Large Area Disaster Emergency Response

Science.gov (United States)

Niu, X. N.; Tang, H.; Wu, L. X.

2018-04-01

an optimal imaging plan, plays a key role in coordinating multiple satellites to monitor the disaster area. In the paper, to generate imaging plan dynamically according to the disaster relief, we propose a dynamic satellite task scheduling method for large area disaster response. First, an initial robust scheduling scheme is generated by a robust satellite scheduling model in which both the profit and the robustness of the schedule are simultaneously maximized. Then, we use a multi-objective optimization model to obtain a series of decomposing schemes. Based on the initial imaging plan, we propose a mixed optimizing algorithm named HA_NSGA-II to allocate the decomposing results thus to obtain an adjusted imaging schedule. A real disaster scenario, i.e., 2008 Wenchuan earthquake, is revisited in terms of rapid response using satellite resources and used to evaluate the performance of the proposed method with state-of-the-art approaches. We conclude that our satellite scheduling model can optimize the usage of satellite resources so as to obtain images in disaster response in a more timely and efficient manner.

Real-time energy resources scheduling considering short-term and very short-term wind forecast

Energy Technology Data Exchange (ETDEWEB)

Silva, Marco; Sousa, Tiago; Morais, Hugo; Vale, Zita [Polytechnic of Porto (Portugal). GECAD - Knowledge Engineering and Decision Support Research Center

2012-07-01

This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process the update of generation and consumption operation and of the storage and electric vehicles storage status are used. Besides the new operation conditions, the most accurate forecast values of wind generation and of consumption using results of short-term and very short-term methods are used. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented. (orig.)

Asymptotic Analysis of SPTA-Based Algorithms for No-Wait Flow Shop Scheduling Problem with Release Dates

Directory of Open Access Journals (Sweden)

Tao Ren

2014-01-01

Full Text Available We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms.

Asymptotic analysis of SPTA-based algorithms for no-wait flow shop scheduling problem with release dates.

Science.gov (United States)

Ren, Tao; Zhang, Chuan; Lin, Lin; Guo, Meiting; Xie, Xionghang

2014-01-01

We address the scheduling problem for a no-wait flow shop to optimize total completion time with release dates. With the tool of asymptotic analysis, we prove that the objective values of two SPTA-based algorithms converge to the optimal value for sufficiently large-sized problems. To further enhance the performance of the SPTA-based algorithms, an improvement scheme based on local search is provided for moderate scale problems. New lower bound is presented for evaluating the asymptotic optimality of the algorithms. Numerical simulations demonstrate the effectiveness of the proposed algorithms.

Resource-Constrained Optimal Scheduling of Synchronous Dataflow Graphs via Timed Automata

NARCIS (Netherlands)

Ahmad, W.; de Groote, Robert; Holzenspies, P.K.F.; Stoelinga, Mariëlle Ida Antoinette; van de Pol, Jan Cornelis

Synchronous dataflow (SDF) graphs are a widely used formalism for modelling, analysing and realising streaming applications, both on a single processor and in a multiprocessing context. Efficient schedules are essential to obtain maximal throughput under the constraint of available resources. This

Scheduling algorithms for automatic control systems for technological processes

Science.gov (United States)

Chernigovskiy, A. S.; Tsarev, R. Yu; Kapulin, D. V.

2017-01-01

Wide use of automatic process control systems and the usage of high-performance systems containing a number of computers (processors) give opportunities for creation of high-quality and fast production that increases competitiveness of an enterprise. Exact and fast calculations, control computation, and processing of the big data arrays - all of this requires the high level of productivity and, at the same time, minimum time of data handling and result receiving. In order to reach the best time, it is necessary not only to use computing resources optimally, but also to design and develop the software so that time gain will be maximal. For this purpose task (jobs or operations), scheduling techniques for the multi-machine/multiprocessor systems are applied. Some of basic task scheduling methods for the multi-machine process control systems are considered in this paper, their advantages and disadvantages come to light, and also some usage considerations, in case of the software for automatic process control systems developing, are made.

A branch-and-price algorithm for the long-term home care scheduling problem

DEFF Research Database (Denmark)

Gamst, Mette; Jensen, Thomas Sejr

2012-01-01

In several countries, home care is provided for certain citizens living at home. The long-term home care scheduling problem is to generate work plans such that a high quality of service is maintained, the work hours of the employees are respected, and the overall cost is kept as low as possible. We...... propose a branchand-price algorithm for the long-term home care scheduling problem. The pricing problem generates a one-day plan for an employee, and the master problem merges the plans with respect to regularity constraints. The method is capable of generating plans with up to 44 visits during one week....

Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Weizhe Zhang

2014-01-01

Full Text Available Energy consumption in computer systems has become a more and more important issue. High energy consumption has already damaged the environment to some extent, especially in heterogeneous multiprocessors. In this paper, we first formulate and describe the energy-aware real-time task scheduling problem in heterogeneous multiprocessors. Then we propose a particle swarm optimization (PSO based algorithm, which can successfully reduce the energy cost and the time for searching feasible solutions. Experimental results show that the PSO-based energy-aware metaheuristic uses 40%–50% less energy than the GA-based and SFLA-based algorithms and spends 10% less time than the SFLA-based algorithm in finding the solutions. Besides, it can also find 19% more feasible solutions than the SFLA-based algorithm.

A Mutualism Quantum Genetic Algorithm to Optimize the Flow Shop Scheduling with Pickup and Delivery Considerations

Directory of Open Access Journals (Sweden)

Jinwei Gu

2015-01-01

Full Text Available A mutualism quantum genetic algorithm (MQGA is proposed for an integrated supply chain scheduling with the materials pickup, flow shop scheduling, and the finished products delivery. The objective is to minimize the makespan, that is, the arrival time of the last finished product to the customer. In MQGA, a new symbiosis strategy named mutualism is proposed to adjust the size of each population dynamically by regarding the mutual influence relation of the two subpopulations. A hybrid Q-bit coding method and a local speeding-up method are designed to increase the diversity of genes, and a checking routine is carried out to ensure the feasibility of each solution; that is, the total physical space of each delivery batch could not exceed the capacity of the vehicle. Compared with the modified genetic algorithm (MGA and the quantum-inspired genetic algorithm (QGA, the effectiveness and efficiency of the MQGA are validated by numerical experiments.

Hybrid Pareto artificial bee colony algorithm for multi-objective single machine group scheduling problem with sequence-dependent setup times and learning effects.

Science.gov (United States)

Yue, Lei; Guan, Zailin; Saif, Ullah; Zhang, Fei; Wang, Hao

2016-01-01

Group scheduling is significant for efficient and cost effective production system. However, there exist setup times between the groups, which require to decrease it by sequencing groups in an efficient way. Current research is focused on a sequence dependent group scheduling problem with an aim to minimize the makespan in addition to minimize the total weighted tardiness simultaneously. In most of the production scheduling problems, the processing time of jobs is assumed as fixed. However, the actual processing time of jobs may be reduced due to "learning effect". The integration of sequence dependent group scheduling problem with learning effects has been rarely considered in literature. Therefore, current research considers a single machine group scheduling problem with sequence dependent setup times and learning effects simultaneously. A novel hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic algorithm is proposed for current problem to get Pareto solutions. Furthermore, five different sizes of test problems (small, small medium, medium, large medium, large) are tested using proposed HPABC. Taguchi method is used to tune the effective parameters of the proposed HPABC for each problem category. The performance of HPABC is compared with three famous multi objective optimization algorithms, improved strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm II (NSGAII) and particle swarm optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity and quality for almost all the instances of the different sizes of problems.

Real-time distributed scheduling algorithm for supporting QoS over WDM networks

Science.gov (United States)

Kam, Anthony C.; Siu, Kai-Yeung

1998-10-01

Most existing or proposed WDM networks employ circuit switching, typically with one session having exclusive use of one entire wavelength. Consequently they are not suitable for data applications involving bursty traffic patterns. The MIT AON Consortium has developed an all-optical LAN/MAN testbed which provides time-slotted WDM service and employs fast-tunable transceivers in each optical terminal. In this paper, we explore extensions of this service to achieve fine-grained statistical multiplexing with different virtual circuits time-sharing the wavelengths in a fair manner. In particular, we develop a real-time distributed protocol for best-effort traffic over this time-slotted WDM service with near-optical fairness and throughput characteristics. As an additional design feature, our protocol supports the allocation of guaranteed bandwidths to selected connections. This feature acts as a first step towards supporting integrated services and quality-of-service guarantees over WDM networks. To achieve high throughput, our approach is based on scheduling transmissions, as opposed to collision- based schemes. Our distributed protocol involves one MAN scheduler and several LAN schedulers (one per LAN) in a master-slave arrangement. Because of propagation delays and limits on control channel capacities, all schedulers are designed to work with partial, delayed traffic information. Our distributed protocol is of the `greedy' type to ensure fast execution in real-time in response to dynamic traffic changes. It employs a hybrid form of rate and credit control for resource allocation. We have performed extensive simulations, which show that our protocol allocates resources (transmitters, receivers, wavelengths) fairly with high throughput, and supports bandwidth guarantees.

Improved Cloud resource allocation: how INDIGO-DataCloud is overcoming the current limitations in Cloud schedulers

Science.gov (United States)

Lopez Garcia, Alvaro; Zangrando, Lisa; Sgaravatto, Massimo; Llorens, Vincent; Vallero, Sara; Zaccolo, Valentina; Bagnasco, Stefano; Taneja, Sonia; Dal Pra, Stefano; Salomoni, Davide; Donvito, Giacinto

2017-10-01

Performing efficient resource provisioning is a fundamental aspect for any resource provider. Local Resource Management Systems (LRMS) have been used in data centers for decades in order to obtain the best usage of the resources, providing their fair usage and partitioning for the users. In contrast, current cloud schedulers are normally based on the immediate allocation of resources on a first-come, first-served basis, meaning that a request will fail if there are no resources (e.g. OpenStack) or it will be trivially queued ordered by entry time (e.g. OpenNebula). Moreover, these scheduling strategies are based on a static partitioning of the resources, meaning that existing quotas cannot be exceeded, even if there are idle resources allocated to other projects. This is a consequence of the fact that cloud instances are not associated with a maximum execution time and leads to a situation where the resources are under-utilized. These facts have been identified by the INDIGO-DataCloud project as being too simplistic for accommodating scientific workloads in an efficient way, leading to an underutilization of the resources, a non desirable situation in scientific data centers. In this work, we will present the work done in the scheduling area during the first year of the INDIGO project and the foreseen evolutions.

Improving the quality of e-commerce web service: what is important for the request scheduling algorithm?

Science.gov (United States)

Suchacka, Grazyna

2005-02-01

The paper concerns a new research area that is Quality of Web Service (QoWS). The need for QoWS is motivated by a still growing number of Internet users, by a steady development and diversification of Web services, and especially by popularization of e-commerce applications. The goal of the paper is a critical analysis of the literature concerning scheduling algorithms for e-commerce Web servers. The paper characterizes factors affecting the load of the Web servers and discusses ways of improving their efficiency. Crucial QoWS requirements of the business Web server are identified: serving requests before their individual deadlines, supporting user session integrity, supporting different classes of users and minimizing a number of rejected requests. It is justified that meeting these requirements and implementing them in an admission control (AC) and scheduling algorithm for the business Web server is crucial to the functioning of e-commerce Web sites and revenue generated by them. The paper presents results of the literature analysis and discusses algorithms that implement these important QoWS requirements. The analysis showed that very few algorithms take into consideration the above mentioned factors and that there is a need for designing an algorithm implementing them.

An Enhanced Discrete Artificial Bee Colony Algorithm to Minimize the Total Flow Time in Permutation Flow Shop Scheduling with Limited Buffers

Directory of Open Access Journals (Sweden)

Guanlong Deng

2016-01-01

Full Text Available This paper presents an enhanced discrete artificial bee colony algorithm for minimizing the total flow time in the flow shop scheduling problem with buffer capacity. First, the solution in the algorithm is represented as discrete job permutation to directly convert to active schedule. Then, we present a simple and effective scheme called best insertion for the employed bee and onlooker bee and introduce a combined local search exploring both insertion and swap neighborhood. To validate the performance of the presented algorithm, a computational campaign is carried out on the Taillard benchmark instances, and computations and comparisons show that the proposed algorithm is not only capable of solving the benchmark set better than the existing discrete differential evolution algorithm and iterated greedy algorithm, but also capable of performing better than two recently proposed discrete artificial bee colony algorithms.

Production and Resource Scheduling in Mass Customization with Dependent Setup Consideration

DEFF Research Database (Denmark)

Nielsen, Izabela Ewa; Bocewicz, G.; Do, Ngoc Anh Dung

2014-01-01

will contribute to the success of mass customization. This paper addresses the problem of production and resource scheduling for a production system with dependent setup and internal transportation such as AGVs in a mass customization environment. A constraint-programming-based methodology is developed to satisfy...

Generation and exploration of aggregation abstractions for scheduling and resource allocation

Science.gov (United States)

Lowry, Michael R.; Linden, Theodore A.

1993-01-01

This paper presents research on the abstraction of computational theories for scheduling and resource allocation. The paper describes both theory and methods for the automated generation of aggregation abstractions and approximations in which detailed resource allocation constraints are replaced by constraints between aggregate demand and capacity. The interaction of aggregation abstraction generation with the more thoroughly investigated abstractions of weakening operator preconditions is briefly discussed. The purpose of generating abstract theories for aggregated demand and resources includes: answering queries about aggregate properties, such as gross feasibility; reducing computational costs by using the solution of aggregate problems to guide the solution of detailed problems; facilitating reformulating theories to approximate problems for which there are efficient problem-solving methods; and reducing computational costs of scheduling by providing more opportunities for variable and value-ordering heuristics to be effective. Experiments are being developed to characterize the properties of aggregations that make them cost effective. Both abstract and concrete theories are represented in a variant of first-order predicate calculus, which is a parameterized multi-sorted logic that facilitates specification of large problems. A particular problem is conceptually represented as a set of ground sentences that is consistent with a quantified theory.

Mathematical Model and Algorithm for the Reefer Mechanic Scheduling Problem at Seaports

Directory of Open Access Journals (Sweden)

Jiantong Zhang

2017-01-01

Full Text Available With the development of seaborne logistics, the international trade of goods transported in refrigerated containers is growing fast. Refrigerated containers, also known as reefers, are used in transportation of temperature sensitive cargo, such as perishable fruits. This trend brings new challenges to terminal managers, that is, how to efficiently arrange mechanics to plug and unplug power for the reefers (i.e., tasks at yards. This work investigates the reefer mechanics scheduling problem at container ports. To minimize the sum of the total tardiness of all tasks and the total working distance of all mechanics, we formulate a mathematical model. For the resolution of this problem, we propose a DE algorithm which is combined with efficient heuristics, local search strategies, and parameter adaption scheme. The proposed algorithm is tested and validated through numerical experiments. Computational results demonstrate the effectiveness and efficiency of the proposed algorithm.

Human resource recommendation algorithm based on ensemble learning and Spark

Science.gov (United States)

Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie

2017-08-01

Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.

Maximizing the nurses' preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm

Science.gov (United States)

Jafari, Hamed; Salmasi, Nasser

2015-09-01

The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign shifts to the nurses in order to satisfy the hospital's demand during the planning horizon by considering different objective functions. In this research, we focus on maximizing the nurses' preferences for working shifts and weekends off by considering several important factors such as hospital's policies, labor laws, governmental regulations, and the status of nurses at the end of the previous planning horizon in one of the largest hospitals in Iran i.e., Milad Hospital. Due to the shortage of available nurses, at first, the minimum total number of required nurses is determined. Then, a mathematical programming model is proposed to solve the problem optimally. Since the proposed research problem is NP-hard, a meta-heuristic algorithm based on simulated annealing (SA) is applied to heuristically solve the problem in a reasonable time. An initial feasible solution generator and several novel neighborhood structures are applied to enhance performance of the SA algorithm. Inspired from our observations in Milad hospital, random test problems are generated to evaluate the performance of the SA algorithm. The results of computational experiments indicate that the applied SA algorithm provides solutions with average percentage gap of 5.49 % compared to the upper bounds obtained from the mathematical model. Moreover, the applied SA algorithm provides significantly better solutions in a reasonable time than the schedules provided by the head nurses.

Culture belief based multi-objective hybrid differential evolutionary algorithm in short term hydrothermal scheduling

International Nuclear Information System (INIS)

Zhang Huifeng; Zhou Jianzhong; Zhang Yongchuan; Lu Youlin; Wang Yongqiang

2013-01-01

Highlights: ► Culture belief is integrated into multi-objective differential evolution. ► Chaotic sequence is imported to improve evolutionary population diversity. ► The priority of convergence rate is proved in solving hydrothermal problem. ► The results show the quality and potential of proposed algorithm. - Abstract: A culture belief based multi-objective hybrid differential evolution (CB-MOHDE) is presented to solve short term hydrothermal optimal scheduling with economic emission (SHOSEE) problem. This problem is formulated for compromising thermal cost and emission issue while considering its complicated non-linear constraints with non-smooth and non-convex characteristics. The proposed algorithm integrates a modified multi-objective differential evolutionary algorithm into the computation model of culture algorithm (CA) as well as some communication protocols between population space and belief space, three knowledge structures in belief space are redefined according to these problem-solving characteristics, and in the differential evolution a chaotic factor is embedded into mutation operator for avoiding the premature convergence by enlarging the search scale when the search trajectory reaches local optima. Furthermore, a new heuristic constraint-handling technique is utilized to handle those complex equality and inequality constraints of SHOSEE problem. After the application on hydrothermal scheduling system, the efficiency and stability of the proposed CB-MOHDE is verified by its more desirable results in comparison to other method established recently, and the simulation results also reveal that CB-MOHDE can be a promising alternative for solving SHOSEE.

Simulated Annealing-Based Ant Colony Algorithm for Tugboat Scheduling Optimization

Directory of Open Access Journals (Sweden)

Qi Xu

2012-01-01

Full Text Available As the “first service station” for ships in the whole port logistics system, the tugboat operation system is one of the most important systems in port logistics. This paper formulated the tugboat scheduling problem as a multiprocessor task scheduling problem (MTSP after analyzing the characteristics of tugboat operation. The model considers factors of multianchorage bases, different operation modes, and three stages of operations (berthing/shifting-berth/unberthing. The objective is to minimize the total operation times for all tugboats in a port. A hybrid simulated annealing-based ant colony algorithm is proposed to solve the addressed problem. By the numerical experiments without the shifting-berth operation, the effectiveness was verified, and the fact that more effective sailing may be possible if tugboats return to the anchorage base timely was pointed out; by the experiments with the shifting-berth operation, one can see that the objective is most sensitive to the proportion of the shifting-berth operation, influenced slightly by the tugboat deployment scheme, and not sensitive to the handling operation times.

A PMBGA to Optimize the Selection of Rules for Job Shop Scheduling Based on the Giffler-Thompson Algorithm

Directory of Open Access Journals (Sweden)

Rui Zhang

2012-01-01

Full Text Available Most existing research on the job shop scheduling problem has been focused on the minimization of makespan (i.e., the completion time of the last job. However, in the fiercely competitive market nowadays, delivery punctuality is more important for maintaining a high service reputation. So in this paper, we aim at solving job shop scheduling problems with the total weighted tardiness objective. Several dispatching rules are adopted in the Giffler-Thompson algorithm for constructing active schedules. It is noticeable that the rule selections for scheduling consecutive operations are not mutually independent but actually interrelated. Under such circumstances, a probabilistic model-building genetic algorithm (PMBGA is proposed to optimize the sequence of selected rules. First, we use Bayesian networks to model the distribution characteristics of high-quality solutions in the population. Then, the new generation of individuals is produced by sampling the established Bayesian network. Finally, some elitist individuals are further improved by a special local search module based on parameter perturbation. The superiority of the proposed approach is verified by extensive computational experiments and comparisons.

Tools for Analyzing Computing Resource Management Strategies and Algorithms for SDR Clouds

Science.gov (United States)

Marojevic, Vuk; Gomez-Miguelez, Ismael; Gelonch, Antoni

2012-09-01

Software defined radio (SDR) clouds centralize the computing resources of base stations. The computing resource pool is shared between radio operators and dynamically loads and unloads digital signal processing chains for providing wireless communications services on demand. Each new user session request particularly requires the allocation of computing resources for executing the corresponding SDR transceivers. The huge amount of computing resources of SDR cloud data centers and the numerous session requests at certain hours of a day require an efficient computing resource management. We propose a hierarchical approach, where the data center is divided in clusters that are managed in a distributed way. This paper presents a set of computing resource management tools for analyzing computing resource management strategies and algorithms for SDR clouds. We use the tools for evaluating a different strategies and algorithms. The results show that more sophisticated algorithms can achieve higher resource occupations and that a tradeoff exists between cluster size and algorithm complexity.

A novel downlink scheduling strategy for traffic communication system based on TD-LTE technology.

Science.gov (United States)

Chen, Ting; Zhao, Xiangmo; Gao, Tao; Zhang, Licheng

2016-01-01

There are many existing classical scheduling algorithms which can obtain better system throughput and user equality, however, they are not designed for traffic transportation environment, which cannot consider whether the transmission performance of various information flows could meet comprehensive requirements of traffic safety and delay tolerance. This paper proposes a novel downlink scheduling strategy for traffic communication system based on TD-LTE technology, which can perform two classification mappings for various information flows in the eNodeB: firstly, associate every information flow packet with traffic safety importance weight according to its relevance to the traffic safety; secondly, associate every traffic information flow with service type importance weight according to its quality of service (QoS) requirements. Once the connection is established, at every scheduling moment, scheduler would decide the scheduling order of all buffers' head of line packets periodically according to the instant value of scheduling importance weight function, which calculated by the proposed algorithm. From different scenario simulations, it can be verified that the proposed algorithm can provide superior differentiated transmission service and reliable QoS guarantee to information flows with different traffic safety levels and service types, which is more suitable for traffic transportation environment compared with the existing popularity PF algorithm. With the limited wireless resource, information flow closed related to traffic safety will always obtain priority scheduling right timely, which can help the passengers' journey more safe. Moreover, the proposed algorithm cannot only obtain good flow throughput and user fairness which are almost equal to those of the PF algorithm without significant differences, but also provide better realtime transmission guarantee to realtime information flow.

Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

International Nuclear Information System (INIS)

Kim, Dong Yun

1997-02-01

In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate adequate gains, which minimize the error of system. The proposed algorithm can reduce the time and efforts required for obtaining the fuzzy rules through the intelligent learning function. The evolutionary programming algorithm is modified and adopted as the method in order to find the optimal gains which are used as the initial gains of FGS with learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller

Enhanced first-in-first-out-based round-robin multicast scheduling algorithm for input-queued switches

DEFF Research Database (Denmark)

Yu, Hao; Ruepp, Sarah Renée; Berger, Michael Stübert

2011-01-01

This study focuses on the multicast scheduling for M × N input-queued switches. An enhanced first-in-first-out -based round-robin multicast scheduling algorithm is proposed with a function of searching deeper into queues to reduce the head-of-line (HOL) blocking problem and thereby the multicast...... out on the decision matrix to reduce the number of transmission for each cell. To reduce the HOL blocking problem, a complement matrix is constructed based on the traffic matrix and the decision matrix, and a process of searching deeper into the queues is carried out to find cells that can be sent...... to the idle outputs. Simulation results show that the proposed function of searching deeper into the queues can alleviate the HOL blocking and as a result reduce the multicast latency significantly. Under both balanced and unbalanced multicast traffic, the proposed algorithm is able to maintain a stable...

A hybrid multi-objective cultural algorithm for short-term environmental/economic hydrothermal scheduling

International Nuclear Information System (INIS)

Lu Youlin; Zhou Jianzhong; Qin Hui; Wang Ying; Zhang Yongchuan

2011-01-01

Research highlights: → Multi-objective optimization model of short-term environmental/economic hydrothermal scheduling. → A hybrid multi-objective cultural algorithm (HMOCA) is presented. → New heuristic constraint handling methods are proposed. → Better quality solutions by reducing fuel cost and emission effects simultaneously are obtained. -- Abstract: The short-term environmental/economic hydrothermal scheduling (SEEHS) with the consideration of multiple objectives is a complicated non-linear constrained optimization problem with non-smooth and non-convex characteristics. In this paper, a multi-objective optimization model of SEEHS is proposed to consider the minimal of fuel cost and emission effects synthetically, and the transmission loss, the water transport delays between connected reservoirs as well as the valve-point effects of thermal plants are taken into consideration to formulate the problem precisely. Meanwhile, a hybrid multi-objective cultural algorithm (HMOCA) is presented to deal with SEEHS problem by optimizing both two objectives simultaneously. The proposed method integrated differential evolution (DE) algorithm into the framework of cultural algorithm model to implement the evolution of population space, and two knowledge structures in belief space are redefined according to the characteristics of DE and SEEHS problem to avoid premature convergence effectively. Moreover, in order to deal with the complicated constraints effectively, new heuristic constraint handling methods without any penalty factor settings are proposed in this paper. The feasibility and effectiveness of the proposed HMOCA method are demonstrated by two case studies of a hydrothermal power system. The simulation results reveal that, compared with other methods established recently, HMOCA can get better quality solutions by reducing fuel cost and emission effects simultaneously.

Updating Linear Schedules with Lowest Cost: a Linear Programming Model

Science.gov (United States)

Biruk, Sławomir; Jaśkowski, Piotr; Czarnigowska, Agata

2017-10-01

Many civil engineering projects involve sets of tasks repeated in a predefined sequence in a number of work areas along a particular route. A useful graphical representation of schedules of such projects is time-distance diagrams that clearly show what process is conducted at a particular point of time and in particular location. With repetitive tasks, the quality of project performance is conditioned by the ability of the planner to optimize workflow by synchronizing the works and resources, which usually means that resources are planned to be continuously utilized. However, construction processes are prone to risks, and a fully synchronized schedule may expire if a disturbance (bad weather, machine failure etc.) affects even one task. In such cases, works need to be rescheduled, and another optimal schedule should be built for the changed circumstances. This typically means that, to meet the fixed completion date, durations of operations have to be reduced. A number of measures are possible to achieve such reduction: working overtime, employing more resources or relocating resources from less to more critical tasks, but they all come at a considerable cost and affect the whole project. The paper investigates the problem of selecting the measures that reduce durations of tasks of a linear project so that the cost of these measures is kept to the minimum and proposes an algorithm that could be applied to find optimal solutions as the need to reschedule arises. Considering that civil engineering projects, such as road building, usually involve less process types than construction projects, the complexity of scheduling problems is lower, and precise optimization algorithms can be applied. Therefore, the authors put forward a linear programming model of the problem and illustrate its principle of operation with an example.

An Interference-Aware Traffic-Priority-Based Link Scheduling Algorithm for Interference Mitigation in Multiple Wireless Body Area Networks

Directory of Open Access Journals (Sweden)

Thien T. T. Le

2016-12-01

Full Text Available Currently, wireless body area networks (WBANs are effectively used for health monitoring services. However, in cases where WBANs are densely deployed, interference among WBANs can cause serious degradation of network performance and reliability. Inter-WBAN interference can be reduced by scheduling the communication links of interfering WBANs. In this paper, we propose an interference-aware traffic-priority-based link scheduling (ITLS algorithm to overcome inter-WBAN interference in densely deployed WBANs. First, we model a network with multiple WBANs as an interference graph where node-level interference and traffic priority are taken into account. Second, we formulate link scheduling for multiple WBANs as an optimization model where the objective is to maximize the throughput of the entire network while ensuring the traffic priority of sensor nodes. Finally, we propose the ITLS algorithm for multiple WBANs on the basis of the optimization model. High spatial reuse is also achieved in the proposed ITLS algorithm. The proposed ITLS achieves high spatial reuse while considering traffic priority, packet length, and the number of interfered sensor nodes. Our simulation results show that the proposed ITLS significantly increases spatial reuse and network throughput with lower delay by mitigating inter-WBAN interference.

ROBUST-HYBRID GENETIC ALGORITHM FOR A FLOW-SHOP SCHEDULING PROBLEM (A Case Study at PT FSCM Manufacturing Indonesia

Directory of Open Access Journals (Sweden)

Johan Soewanda

2007-01-01

Full Text Available This paper discusses the application of Robust Hybrid Genetic Algorithm to solve a flow-shop scheduling problem. The proposed algorithm attempted to reach minimum makespan. PT. FSCM Manufacturing Indonesia Plant 4's case was used as a test case to evaluate the performance of the proposed algorithm. The proposed algorithm was compared to Ant Colony, Genetic-Tabu, Hybrid Genetic Algorithm, and the company's algorithm. We found that Robust Hybrid Genetic produces statistically better result than the company's, but the same as Ant Colony, Genetic-Tabu, and Hybrid Genetic. In addition, Robust Hybrid Genetic Algorithm required less computational time than Hybrid Genetic Algorithm

A Privacy-Preserving Distributed Optimal Scheduling for Interconnected Microgrids

Directory of Open Access Journals (Sweden)

Nian Liu

2016-12-01

Full Text Available With the development of microgrids (MGs, interconnected operation of multiple MGs is becoming a promising strategy for the smart grid. In this paper, a privacy-preserving distributed optimal scheduling method is proposed for the interconnected microgrids (IMG with a battery energy storage system (BESS and renewable energy resources (RESs. The optimal scheduling problem is modeled to minimize the coalitional operation cost of the IMG, including the fuel cost of conventional distributed generators and the life loss cost of BESSs. By using the framework of the alternating direction method of multipliers (ADMM, a distributed optimal scheduling model and an iteration solution algorithm for the IMG is introduced; only the expected exchanging power (EEP of each MG is required during the iterations. Furthermore, a privacy-preserving strategy for the sharing of the EEP among MGs is designed to work with the mechanism of the distributed algorithm. According to the security analysis, the EEP can be delivered in a cooperative and privacy-preserving way. A case study and numerical results are given in terms of the convergence of the algorithm, the comparison of the costs and the implementation efficiency.

Research on Energy-Saving Production Scheduling Based on a Clustering Algorithm for a Forging Enterprise

Directory of Open Access Journals (Sweden)

Yifei Tong

2016-02-01

Full Text Available Energy efficiency is a buzzword of the 21st century. With the ever growing need for energy efficient and low-carbon production, it is a big challenge for high energy-consumption enterprises to reduce their energy consumption. To this aim, a forging enterprise, DVR (the abbreviation of a forging enterprise, is researched. Firstly, an investigation into the production processes of DVR is given as well as an analysis of forging production. Then, the energy-saving forging scheduling is decomposed into two sub-problems. One is for cutting and machining scheduling, which is similar to traditional machining scheduling. The other one is for forging and heat treatment scheduling. Thirdly, former forging production scheduling is presented and solved based on an improved genetic algorithm. Fourthly, the latter is discussed in detail, followed by proposed dynamic clustering and stacking combination optimization. The proposed stacking optimization requires making the gross weight of forgings as close to the maximum batch capacity as possible. The above research can help reduce the heating times, and increase furnace utilization with high energy efficiency and low carbon emissions.

Multi-objective flexible job shop scheduling problem using variable neighborhood evolutionary algorithm

Science.gov (United States)

Wang, Chun; Ji, Zhicheng; Wang, Yan

2017-07-01

In this paper, multi-objective flexible job shop scheduling problem (MOFJSP) was studied with the objects to minimize makespan, total workload and critical workload. A variable neighborhood evolutionary algorithm (VNEA) was proposed to obtain a set of Pareto optimal solutions. First, two novel crowded operators in terms of the decision space and object space were proposed, and they were respectively used in mating selection and environmental selection. Then, two well-designed neighborhood structures were used in local search, which consider the problem characteristics and can hold fast convergence. Finally, extensive comparison was carried out with the state-of-the-art methods specially presented for solving MOFJSP on well-known benchmark instances. The results show that the proposed VNEA is more effective than other algorithms in solving MOFJSP.

Tri-generation based hybrid power plant scheduling for renewable resources rich area with energy storage

International Nuclear Information System (INIS)

Pazheri, F.R.

2015-01-01

Highlights: • Involves scheduling of the tri-generation based hybrid power plant. • Utilization of renewable energy through energy storage is discussed. • Benefits of the proposed model are illustrated. • Energy efficient and environmental friendly dispatch is analyzed. • Modeled scheduling problem is applicable to any fuel enriched area. - Abstract: Solving power system scheduling is crucial to ensure smooth operations of the electric power industry. Effective utilization of available conventional and renewable energy sources (RES) by tri-generation and with the aid of energy storage facilities (ESF) can ensure clean and energy efficient power generation. Such power generation can play an important role in countries, like Saudi Arabia, where abundant fossil fuels (FF) and renewable energy sources (RES) are available. Hence, effective modeling of such hybrid power systems scheduling is essential in such countries based on the available fuel resources. The intent of this paper is to present a simple model for tri-generation based hybrid power system scheduling for energy resources rich area in presence of ESF, to ensure optimum fuel utilization and minimum pollutant emissions while meeting the power demand. This research points an effective operation strategy which ensure a clean and energy efficient power scheduling by exploiting available energy resources effectively. Hence, it has an important role in current and future power generation. In order to illustrate the benefits of the presented approach a clean and energy efficient hybrid power supply scheme for King Saud University (KSU), Saudi Arabia, is proposed and analyzed here. Results show that the proposed approach is very suitable for KSU since adequate solar power is available during its peak demand periods

Resource-constrained project scheduling problem: review of past and recent developments

Directory of Open Access Journals (Sweden)

Farhad Habibi

2018-01-01

Full Text Available The project scheduling problem is both practically and theoretically of paramount importance. From the practical perspective, improvement of project scheduling as a critical part of project management process can lead to successful project completion and significantly decrease of the relevant costs. From the theoretical perspective, project scheduling is regarded as one of the in-teresting optimization issues, which has attracted the attention of many researchers in the area of operations research. Therefore, the project scheduling issue has been significantly evaluated over time and has been developed from various aspects. In this research, the topics related to Re-source-Constrained Project Scheduling Problem (RCPSP are reviewed, recent developments in this field are evaluated, and the results are presented for future studies. In this regard, first, the standard problem of RCPSP is expressed and related developments are presented from four as-pects of resources, characteristics of activities, type of objective functions, and availability level of information. Following that, details about 216 articles conducted on RCPSP during 1980-2017 are expressed. At the end, in line with the statistics obtained from the evaluation of previ-ous articles, suggestions are made for the future studies in order to help the development of new issues in this area.

Project scheduling with resources constraints: application in the equipment production for the oil industry; Scheduling de projetos com restricao de recursos: aplicacao na producao de equipamentos para a industria de petroleo

Energy Technology Data Exchange (ETDEWEB)

Cruz, Carolina A.O.; Ferreira Filho, Virgilio J.M. [Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE)

2008-07-01

One of the essential steps in the main scope Project Management is the Time Management made by the planning and control of the project schedule. In this work is presented the resource constrained scheduling problem beyond its mathematical formulation and a review of papers about this issue. In sequence is presented a practical example of this model considering a simplified model of an engineering project schedule of oil production equipment. The results obtained with the model application are shown and the conclusions about the work with resource constrained scheduling problems. (author)

A framework for resources allocation in virtualised C-RAN

DEFF Research Database (Denmark)

Al-Samman, Imad; Artuso, Matteo; Christiansen, Henrik Lehrmann

2016-01-01

the performance gains of different resource allocation schemes in legacy LTE air interface virtualization. Moreover Cloud — based mobile networks are anticipated to play a significant role for next generation mobile networks. This paper investigates the potential deployment benefits of a novel resource...... virtualization algorithm (Traffic Aware Joint Scheduling) in Cloud-RANs (C-RAN). Air interface resources are coordinated and allocated dynamically by a hypervisor among different virtual operators (VOs). Three distinctive schemes are proposed and evaluated against standard Round Robin (RR) C-RAN scheduling...

An improved scheduling algorithm for linear networks

KAUST Repository

Bader, Ahmed; Alouini, Mohamed-Slim; Ayadi, Yassin

2017-01-01

In accordance with the present disclosure, embodiments of an exemplary scheduling controller module or device implement an improved scheduling process such that the targeted reduction in schedule length can be achieve while incurring minimal energy penalty by allowing for a large rate (or duration) selection alphabet.

An improved scheduling algorithm for linear networks

KAUST Repository

Bader, Ahmed

2017-02-09

In accordance with the present disclosure, embodiments of an exemplary scheduling controller module or device implement an improved scheduling process such that the targeted reduction in schedule length can be achieve while incurring minimal energy penalty by allowing for a large rate (or duration) selection alphabet.

On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling

DEFF Research Database (Denmark)

Neumann, Frank; Witt, Carsten

2015-01-01

combinatorial optimization problem, namely makespan scheduling. We study the model of a strong adversary which is allowed to change one job at regular intervals. Furthermore, we investigate the setting of random changes. Our results show that randomized local search and a simple evolutionary algorithm are very...

Data location-aware job scheduling in the grid. Application to the GridWay metascheduler

International Nuclear Information System (INIS)

Delgado Peris, Antonio; Hernandez, Jose; Huedo, Eduardo; Llorente, Ignacio M

2010-01-01

Grid infrastructures constitute nowadays the core of the computing facilities of the biggest LHC experiments. These experiments produce and manage petabytes of data per year and run thousands of computing jobs every day to process that data. It is the duty of metaschedulers to allocate the tasks to the most appropriate resources at the proper time. Our work reviews the policies that have been proposed for the scheduling of grid jobs in the context of very data-intensive applications. We indicate some of the practical problems that such models will face and describe what we consider essential characteristics of an optimum scheduling system: aim to minimise not only job turnaround time but also data replication, flexibility to support different virtual organisation requirements and capability to coordinate the tasks of data placement and job allocation while keeping their execution decoupled. These ideas have guided the development of an enhanced prototype for GridWay, a general purpose metascheduler, part of the Globus Toolkit and member of the EGEE's RESPECT program. Current GridWay's scheduling algorithm is unaware of data location. Our prototype makes it possible for job requests to set data needs not only as absolute requirements but also as functions for resource ranking. As our tests show, this makes it more flexible than currently used resource brokers to implement different data-aware scheduling algorithms.

Optimal residential smart appliances scheduling considering distribution network constraints

Directory of Open Access Journals (Sweden)

Yu-Ree Kim

2016-01-01

Full Text Available As smart appliances (SAs are more widely adopted within distribution networks, residential consumers can contribute to electricity market operations with demand response resources and reduce their electricity bill. However, if the schedules of demand response resources are determined only by the economic electricity rate signal, the schedule can be unfeasible due to the distribution network constraints. Furthermore, it is impossible for consumers to understand the complex physical characteristics and reflect them in their everyday behaviors. This paper introduces the concept of load coordinating retailer (LCR that deals with demand responsive appliances to reduce electrical consumption for the given distribution network constraints. The LCR can play the role of both conventional retailer and aggregated demand response provider for residential customers. It determines the optimal schedules for the aggregated neighboring SAs according to their types within each distribution feeder. The optimization algorithms are developed using Mixed Integer Linear Programming, and the distribution network is solved by the Newton–Raphson AC power flow.

A Comparison between Fixed Priority and EDF Scheduling accounting for Cache Related Pre-emption Delays

Directory of Open Access Journals (Sweden)

Will Lunniss

2014-04-01

Full Text Available In multitasking real-time systems, the choice of scheduling algorithm is an important factor to ensure that response time requirements are met while maximising limited system resources. Two popular scheduling algorithms include fixed priority (FP and earliest deadline first (EDF. While they have been studied in great detail before, they have not been compared when taking into account cache related pre-emption delays (CRPD. Memory and cache are split into a number of blocks containing instructions and data. During a pre-emption, cache blocks from the pre-empting task can evict those of the pre-empted task. When the pre-empted task is resumed, if it then has to re-load the evicted blocks, CRPD are introduced which then affect the schedulability of the task. In this paper we compare FP and EDF scheduling algorithms in the presence of CRPD using the state-of-the-art CRPD analysis. We find that when CRPD is accounted for, the performance gains offered by EDF over FP, while still notable, are diminished. Furthermore, we find that under scenarios that cause relatively high CRPD, task layout optimisation techniques can be applied to allow FP to schedule tasksets at a similar processor utilisation to EDF. Thus making the choice of the task layout in memory as important as the choice of scheduling algorithm. This is very relevant for industry, as it is much cheaper and simpler to adjust the task layout through the linker than it is to switch the scheduling algorithm.

Handling machine breakdown for dynamic scheduling by a colony of cognitive agents in a holonic manufacturing framework

Directory of Open Access Journals (Sweden)

T. K. Jana

2015-09-01

Full Text Available There is an ever increasing need of providing quick, yet improved solution to dynamic scheduling by better responsiveness following simple coordination mechanism to better adapt to the changing environments. In this endeavor, a cognitive agent based approach is proposed to deal with machine failure. A Multi Agent based Holonic Adaptive Scheduling (MAHoAS architecture is developed to frame the schedule by explicit communication between the product holons and the resource holons in association with the integrated process planning and scheduling (IPPS holon under normal situation. In the event of breakdown of a resource, the cooperation is sought by implicit communication. Inspired by the cognitive behavior of human being, a cognitive decision making scheme is proposed that reallocates the incomplete task to another resource in the most optimized manner and tries to expedite the processing in view of machine failure. A metamorphic algorithm is developed and implemented in Oracle 9i to identify the best candidate resource for task re-allocation. Integrated approach to process planning and scheduling realized under Multi Agent System (MAS framework facilitates dynamic scheduling with improved performance under such situations. The responsiveness of the resources having cognitive capabilities helps to overcome the adverse consequences of resource failure in a better way.

A short-term operating room surgery scheduling problem integrating multiple nurses roster constraints.

Science.gov (United States)

Xiang, Wei; Yin, Jiao; Lim, Gino

2015-02-01

Operating room (OR) surgery scheduling determines the individual surgery's operation start time and assigns the required resources to each surgery over a schedule period, considering several constraints related to a complete surgery flow and the multiple resources involved. This task plays a decisive role in providing timely treatments for the patients while balancing hospital resource utilization. The originality of the present study is to integrate the surgery scheduling problem with real-life nurse roster constraints such as their role, specialty, qualification and availability. This article proposes a mathematical model and an ant colony optimization (ACO) approach to efficiently solve such surgery scheduling problems. A modified ACO algorithm with a two-level ant graph model is developed to solve such combinatorial optimization problems because of its computational complexity. The outer ant graph represents surgeries, while the inner graph is a dynamic resource graph. Three types of pheromones, i.e. sequence-related, surgery-related, and resource-related pheromone, fitting for a two-level model are defined. The iteration-best and feasible update strategy and local pheromone update rules are adopted to emphasize the information related to the good solution in makespan, and the balanced utilization of resources as well. The performance of the proposed ACO algorithm is then evaluated using the test cases from (1) the published literature data with complete nurse roster constraints, and 2) the real data collected from a hospital in China. The scheduling results using the proposed ACO approach are compared with the test case from both the literature and the real life hospital scheduling. Comparison results with the literature shows that the proposed ACO approach has (1) an 1.5-h reduction in end time; (2) a reduction in variation of resources' working time, i.e. 25% for ORs, 50% for nurses in shift 1 and 86% for nurses in shift 2; (3) an 0.25h reduction in

Solving a mixed-integer linear programming model for a multi-skilled project scheduling problem by simulated annealing

Directory of Open Access Journals (Sweden)

H Kazemipoor

2012-04-01

Full Text Available A multi-skilled project scheduling problem (MSPSP has been generally presented to schedule a project with staff members as resources. Each activity in project network requires different skills and also staff members have different skills, too. This causes the MSPSP becomes a special type of a multi-mode resource-constrained project scheduling problem (MM-RCPSP with a huge number of modes. Given the importance of this issue, in this paper, a mixed integer linear programming for the MSPSP is presented. Due to the complexity of the problem, a meta-heuristic algorithm is proposed in order to find near optimal solutions. To validate performance of the algorithm, results are compared against exact solutions solved by the LINGO solver. The results are promising and show that optimal or near-optimal solutions are derived for small instances and good solutions for larger instances in reasonable time.

Optimal Resources Planning of Residential Complex Energy System in a Day-ahead Market Based on Invasive Weed Optimization Algorithm

Directory of Open Access Journals (Sweden)

P. Αhmadi

2017-10-01

Full Text Available This paper deals with optimal resources planning in a residential complex energy system, including FC (fuel cell, PV (Photovoltaic panels and the battery. A day-ahead energy management system (EMS based on invasive weed optimization (IWO algorithm is defined for managing different resources to determine an optimal operation schedule for the energy resources at each time interval to minimize the operation cost of a smart residential complex energy system. Moreover, in this paper the impacts of the sell to grid and purchase from grid are also considered. All practical constraints of the each energy resources and utility policies are taken into account. Moreover, sensitivity analysis are conducted on electricity prices and sell to grid factor (SGF, in order to improve understanding the impact of key parameters on residential CHP systems economy. It is shown that proposed system can meet all electrical and thermal demands with economic point of view. Also enhancement of electricity price leads to substantial growth in utilization of proposed CHP system.

Improved Channel Allocation and RLC block scheduling for Downlink traffic in GPRS

DEFF Research Database (Denmark)

Wang, Haibo; Devendra, Devendra; Zhou, Xin

This paper investigates the Radio Resource Management (RRM) approaches for GSM/GPRS networks. The effort was put on Access Control(AC), Time-Slot (TS) allocation, and Radio Link Control (RLC) packet scheduling for down-link performance enhancement. A new credit-based algorithm was proposed...

Multimode Resource-Constrained Multiple Project Scheduling Problem under Fuzzy Random Environment and Its Application to a Large Scale Hydropower Construction Project

Science.gov (United States)

Xu, Jiuping

2014-01-01

This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform the fuzzy random parameters into fuzzy variables that are subsequently defuzzified using an expected value operator with an optimistic-pessimistic index. Then a combinatorial-priority-based hybrid particle swarm optimization algorithm is developed to solve the proposed model, where the combinatorial particle swarm optimization and priority-based particle swarm optimization are designed to assign modes to activities and to schedule activities, respectively. Finally, the results and analysis of a practical example at a large scale hydropower construction project are presented to demonstrate the practicality and efficiency of the proposed model and optimization method. PMID:24550708

Multimode resource-constrained multiple project scheduling problem under fuzzy random environment and its application to a large scale hydropower construction project.

Science.gov (United States)

Xu, Jiuping; Feng, Cuiying

2014-01-01

This paper presents an extension of the multimode resource-constrained project scheduling problem for a large scale construction project where multiple parallel projects and a fuzzy random environment are considered. By taking into account the most typical goals in project management, a cost/weighted makespan/quality trade-off optimization model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform the fuzzy random parameters into fuzzy variables that are subsequently defuzzified using an expected value operator with an optimistic-pessimistic index. Then a combinatorial-priority-based hybrid particle swarm optimization algorithm is developed to solve the proposed model, where the combinatorial particle swarm optimization and priority-based particle swarm optimization are designed to assign modes to activities and to schedule activities, respectively. Finally, the results and analysis of a practical example at a large scale hydropower construction project are presented to demonstrate the practicality and efficiency of the proposed model and optimization method.

Scheduling with Group Dynamics: a Multi-Robot Task Allocation Algorithm based on Vacancy Chains

National Research Council Canada - National Science Library

Dahl, Torbjorn S; Mataric, Maja J; Sukhatme, Gaurav S

2002-01-01

.... We present a multi-robot task allocation algorithm that is sensitive to group dynamics. Our algorithm is based on vacancy chains, a resource distribution process common in human and animal societies...

Constrained consumption shifting management in the distributed energy resources scheduling considering demand response

International Nuclear Information System (INIS)

Faria, Pedro; Vale, Zita; Baptista, Jose

2015-01-01

Highlights: • Consumption reduction and/or shift to several periods before and after. • Optimization problem for scheduling of demand response and distributed generation. • Minimization of the Virtual Power Player operation (remuneration) costs. • Demand response can be efficient to meet distributed generation shortages. • Consumers benefit with the remuneration of the participation in demand response. - Abstract: Demand response concept has been gaining increasing importance while the success of several recent implementations makes this resource benefits unquestionable. This happens in a power systems operation environment that also considers an intensive use of distributed generation. However, more adequate approaches and models are needed in order to address the small size consumers and producers aggregation, while taking into account these resources goals. The present paper focuses on the demand response programs and distributed generation resources management by a Virtual Power Player that optimally aims to minimize its operation costs taking the consumption shifting constraints into account. The impact of the consumption shifting in the distributed generation resources schedule is also considered. The methodology is applied to three scenarios based on 218 consumers and 4 types of distributed generation, in a time frame of 96 periods

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

Directory of Open Access Journals (Sweden)

Muhammad Kamal Amjad

2018-01-01

Full Text Available Flexible Job Shop Scheduling Problem (FJSSP is an extension of the classical Job Shop Scheduling Problem (JSSP. The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions.

Unit Commitment Towards Decarbonized Network Facing Fixed and Stochastic Resources Applying Water Cycle Optimization

Directory of Open Access Journals (Sweden)

Heba-Allah I. ElAzab

2018-05-01

Full Text Available This paper presents a trustworthy unit commitment study to schedule both Renewable Energy Resources (RERs with conventional power plants to potentially decarbonize the electrical network. The study has employed a system with three IEEE thermal (coal-fired power plants as dispatchable distributed generators, one wind plant, one solar plant as stochastic distributed generators, and Plug-in Electric Vehicles (PEVs which can work either loads or generators based on their charging schedule. This paper investigates the unit commitment scheduling objective to minimize the Combined Economic Emission Dispatch (CEED. To reduce combined emission costs, integrating more renewable energy resources (RER and PEVs, there is an essential need to decarbonize the existing system. Decarbonizing the system means reducing the percentage of CO2 emissions. The uncertain behavior of wind and solar energies causes imbalance penalty costs. PEVs are proposed to overcome the intermittent nature of wind and solar energies. It is important to optimally integrate and schedule stochastic resources including the wind and solar energies, and PEVs charge and discharge processes with dispatched resources; the three IEEE thermal (coal-fired power plants. The Water Cycle Optimization Algorithm (WCOA is an efficient and intelligent meta-heuristic technique employed to solve the economically emission dispatch problem for both scheduling dispatchable and stochastic resources. The goal of this study is to obtain the solution for unit commitment to minimize the combined cost function including CO2 emission costs applying the Water Cycle Optimization Algorithm (WCOA. To validate the WCOA technique, the results are compared with the results obtained from applying the Dynamic Programming (DP algorithm, which is considered as a conventional numerical technique, and with the Genetic Algorithm (GA as a meta-heuristic technique.

CMS readiness for multi-core workload scheduling

Science.gov (United States)

Perez-Calero Yzquierdo, A.; Balcas, J.; Hernandez, J.; Aftab Khan, F.; Letts, J.; Mason, D.; Verguilov, V.

2017-10-01

In the present run of the LHC, CMS data reconstruction and simulation algorithms benefit greatly from being executed as multiple threads running on several processor cores. The complexity of the Run 2 events requires parallelization of the code to reduce the memory-per- core footprint constraining serial execution programs, thus optimizing the exploitation of present multi-core processor architectures. The allocation of computing resources for multi-core tasks, however, becomes a complex problem in itself. The CMS workload submission infrastructure employs multi-slot partitionable pilots, built on HTCondor and GlideinWMS native features, to enable scheduling of single and multi-core jobs simultaneously. This provides a solution for the scheduling problem in a uniform way across grid sites running a diversity of gateways to compute resources and batch system technologies. This paper presents this strategy and the tools on which it has been implemented. The experience of managing multi-core resources at the Tier-0 and Tier-1 sites during 2015, along with the deployment phase to Tier-2 sites during early 2016 is reported. The process of performance monitoring and optimization to achieve efficient and flexible use of the resources is also described.

CMS Readiness for Multi-Core Workload Scheduling

Energy Technology Data Exchange (ETDEWEB)

Perez-Calero Yzquierdo, A. [Madrid, CIEMAT; Balcas, J. [Caltech; Hernandez, J. [Madrid, CIEMAT; Aftab Khan, F. [NCP, Islamabad; Letts, J. [UC, San Diego; Mason, D. [Fermilab; Verguilov, V. [CLMI, Sofia

2017-11-22

In the present run of the LHC, CMS data reconstruction and simulation algorithms benefit greatly from being executed as multiple threads running on several processor cores. The complexity of the Run 2 events requires parallelization of the code to reduce the memory-per- core footprint constraining serial execution programs, thus optimizing the exploitation of present multi-core processor architectures. The allocation of computing resources for multi-core tasks, however, becomes a complex problem in itself. The CMS workload submission infrastructure employs multi-slot partitionable pilots, built on HTCondor and GlideinWMS native features, to enable scheduling of single and multi-core jobs simultaneously. This provides a solution for the scheduling problem in a uniform way across grid sites running a diversity of gateways to compute resources and batch system technologies. This paper presents this strategy and the tools on which it has been implemented. The experience of managing multi-core resources at the Tier-0 and Tier-1 sites during 2015, along with the deployment phase to Tier-2 sites during early 2016 is reported. The process of performance monitoring and optimization to achieve efficient and flexible use of the resources is also described.

Scheduling of Iterative Algorithms with Matrix Operations for Efficient FPGA Design—Implementation of Finite Interval Constant Modulus Algorithm

Czech Academy of Sciences Publication Activity Database

Šůcha, P.; Hanzálek, Z.; Heřmánek, Antonín; Schier, Jan

2007-01-01

Roč. 46, č. 1 (2007), s. 35-53 ISSN 0922-5773 R&D Projects: GA AV ČR(CZ) 1ET300750402; GA MŠk(CZ) 1M0567; GA MPO(CZ) FD-K3/082 Institutional research plan: CEZ:AV0Z10750506 Keywords : high-level synthesis * cyclic scheduling * iterative algorithms * imperfectly nested loops * integer linear programming * FPGA * VLSI design * blind equalization * implementation Subject RIV: BA - General Mathematics Impact factor: 0.449, year: 2007 http://www.springerlink.com/content/t217kg0822538014/fulltext.pdf

New Mathematical Model and Algorithm for Economic Lot Scheduling Problem in Flexible Flow Shop

Directory of Open Access Journals (Sweden)

H. Zohali

2018-03-01

Full Text Available This paper addresses the lot sizing and scheduling problem for a number of products in flexible flow shop with identical parallel machines. The production stages are in series, while separated by finite intermediate buffers. The objective is to minimize the sum of setup and inventory holding costs per unit of time. The available mathematical model of this problem in the literature suffers from huge complexity in terms of size and computation. In this paper, a new mixed integer linear program is developed for delay with the huge dimentions of the problem. Also, a new meta heuristic algorithm is developed for the problem. The results of the numerical experiments represent a significant advantage of the proposed model and algorithm compared with the available models and algorithms in the literature.

Dynamic Hierarchical Sleep Scheduling for Wireless Ad-Hoc Sensor Networks

Directory of Open Access Journals (Sweden)

Chih-Yu Wen

2009-05-01

Full Text Available This paper presents two scheduling management schemes for wireless sensor networks, which manage the sensors by utilizing the hierarchical network structure and allocate network resources efficiently. A local criterion is used to simultaneously establish the sensing coverage and connectivity such that dynamic cluster-based sleep scheduling can be achieved. The proposed schemes are simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show that the proposed algorithms provide efficient network power control and can achieve high scalability in wireless sensor networks.

Dynamic hierarchical sleep scheduling for wireless ad-hoc sensor networks.

Science.gov (United States)

Wen, Chih-Yu; Chen, Ying-Chih

2009-01-01

This paper presents two scheduling management schemes for wireless sensor networks, which manage the sensors by utilizing the hierarchical network structure and allocate network resources efficiently. A local criterion is used to simultaneously establish the sensing coverage and connectivity such that dynamic cluster-based sleep scheduling can be achieved. The proposed schemes are simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show that the proposed algorithms provide efficient network power control and can achieve high scalability in wireless sensor networks.

A lower bound on deterministic online algorithms for scheduling on related machines without preemption

Czech Academy of Sciences Publication Activity Database

Ebenlendr, Tomáš; Sgall, J.

2015-01-01

Roč. 56, č. 1 (2015), s. 73-81 ISSN 1432-4350 R&D Projects: GA ČR GBP202/12/G061; GA AV ČR IAA100190902 Institutional support: RVO:67985840 Keywords : online algorithms * scheduling * makespan Subject RIV: IN - Informatics, Computer Science Impact factor: 0.719, year: 2015 http://link.springer.com/article/10.1007%2Fs00224-013-9451-6

Comparison of different chaotic maps in particle swarm optimization algorithm for long-term cascaded hydroelectric system scheduling

International Nuclear Information System (INIS)

He Yaoyao; Zhou Jianzhong; Xiang Xiuqiao; Chen Heng; Qin Hui

2009-01-01

The goal of this paper is to present a novel chaotic particle swarm optimization (CPSO) algorithm and compares the efficiency of three one-dimensional chaotic maps within symmetrical region for long-term cascaded hydroelectric system scheduling. The introduced chaotic maps improve the global optimal capability of CPSO algorithm. Moreover, a piecewise linear interpolation function is employed to transform all constraints into restrict upriver water level for implementing the maximum of objective function. Numerical results and comparisons demonstrate the effect and speed of different algorithms on a practical hydro-system.

A demand response modeling for residential consumers in smart grid environment using game theory based energy scheduling algorithm

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S. Sofana Reka

2016-06-01

Full Text Available In this paper, demand response modeling scheme is proposed for residential consumers using game theory algorithm as Generalized Tit for Tat (GTFT Dominant Game based Energy Scheduler. The methodology is established as a work flow domain model between the utility and the user considering the smart grid framework. It exhibits an algorithm which schedules load usage by creating several possible tariffs for consumers such that demand is never raised. This can be done both individually and among multiple users of a community. The uniqueness behind the demand response proposed is that, the tariff is calculated for all hours and the load during the peak hours which can be rescheduled is shifted based on the Peak Average Ratio. To enable the vitality of the work simulation results of a general case of three domestic consumers are modeled extended to a comparative performance and evaluation with other algorithms and inference is analyzed.

Using Improved Ant Colony Algorithm to Investigate EMU Circulation Scheduling Problem

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Yu Zhou

2014-01-01

Full Text Available High-speed railway is one of the most important ways to solve the long-standing travel difficulty problem in China. However, due to the high acquisition and maintenance cost, it is impossible for decision-making departments to purchase enough EMUs to satisfy the explosive travel demand. Therefore, there is an urgent need to study how to utilize EMU more efficiently and reduce costs in the case of completing a given task in train diagram. In this paper, an EMU circulation scheduling model is built based on train diagram constraints, maintenance constraints, and so forth; in the model solving process, an improved ACA algorithm has been designed. A case study is conducted to verify the feasibility of the model. Moreover, contrast tests have been carried out to compare the efficiency between the improved ACA and the traditional approaches. The results reveal that improved ACA method can solve the model with less time and the quality of each representative index is much better, which means that efficiency of the improved ACA method is higher and better scheduling scheme can be obtained.

E-Token Energy-Aware Proportionate Sharing Scheduling Algorithm for Multiprocessor Systems

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Pasupuleti Ramesh

2017-01-01

Full Text Available WSN plays vital role from small range healthcare surveillance systems to largescale environmental monitoring. Its design for energy constrained applications is a challenging issue. Sensors in WSNs are projected to run separately for longer periods. It is of excessive cost to substitute exhausted batteries which is not even possible in antagonistic situations. Multiprocessors are used in WSNs for high performance scientific computing, where each processor is assigned the same or different workload. When the computational demands of the system increase then the energy efficient approaches play an important role to increase system lifetime. Energy efficiency is commonly carried out by using proportionate fair scheduler. This introduces abnormal overloading effect. In order to overcome the existing problems E-token Energy-Aware Proportionate Sharing (EEAPS scheduling is proposed here. The power consumption for each thread/task is calculated and the tasks are allotted to the multiple processors through the auctioning mechanism. The algorithm is simulated by using the real-time simulator (RTSIM and the results are tested.

An Efficient Scheduling Scheme to Enhance the Capacity of VoIP Services in Evolved UTRA Uplink

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Kim Yong-Seok

2008-01-01

Full Text Available An efficient scheduling scheme is proposed to increase the available capacity of VoIP services over evolved UTRA uplink. On top of the advantages of persistent scheduling, the proposed scheme adaptively share the resources of two VoIP users to get early-termination gain of dynamic scheduler. Through system-level simulations, the performance of the proposed algorithm is evaluated in terms of the capacity enhancement of VoIP services. Comparisons with the original persistent scheduling and the HSUPA scheduler reveal that the proposed scheme increases the capacity of VoIP services up to 20%.

Parallel genetic algorithms with migration for the hybrid flow shop scheduling problem

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K. Belkadi

2006-01-01

Full Text Available This paper addresses scheduling problems in hybrid flow shop-like systems with a migration parallel genetic algorithm (PGA_MIG. This parallel genetic algorithm model allows genetic diversity by the application of selection and reproduction mechanisms nearer to nature. The space structure of the population is modified by dividing it into disjoined subpopulations. From time to time, individuals are exchanged between the different subpopulations (migration. Influence of parameters and dedicated strategies are studied. These parameters are the number of independent subpopulations, the interconnection topology between subpopulations, the choice/replacement strategy of the migrant individuals, and the migration frequency. A comparison between the sequential and parallel version of genetic algorithm (GA is provided. This comparison relates to the quality of the solution and the execution time of the two versions. The efficiency of the parallel model highly depends on the parameters and especially on the migration frequency. In the same way this parallel model gives a significant improvement of computational time if it is implemented on a parallel architecture which offers an acceptable number of processors (as many processors as subpopulations.

A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem

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Chun Wang

2017-01-01

Full Text Available A novel multiobjective memetic algorithm based on decomposition (MOMAD is proposed to solve multiobjective flexible job shop scheduling problem (MOFJSP, which simultaneously minimizes makespan, total workload, and critical workload. Firstly, a population is initialized by employing an integration of different machine assignment and operation sequencing strategies. Secondly, multiobjective memetic algorithm based on decomposition is presented by introducing a local search to MOEA/D. The Tchebycheff approach of MOEA/D converts the three-objective optimization problem to several single-objective optimization subproblems, and the weight vectors are grouped by K-means clustering. Some good individuals corresponding to different weight vectors are selected by the tournament mechanism of a local search. In the experiments, the influence of three different aggregation functions is first studied. Moreover, the effect of the proposed local search is investigated. Finally, MOMAD is compared with eight state-of-the-art algorithms on a series of well-known benchmark instances and the experimental results show that the proposed algorithm outperforms or at least has comparative performance to the other algorithms.