Two-Machine Job-Shop Scheduling with Equal Processing Times on Each Machine

Gafarov, Evgeny R.; Werner, Frank

doi:10.3390/math7030301

Cited by 8 publications

(8 citation statements)

References 24 publications

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“…Similar to the increase operator, the decrease operator first determines the number of individuals to be deleted using Eq. (20). After that, the population is also divided into n dec classes with Algorithm 1, and the worst individuals in each class are then deleted.…”

Section: Optimization Of Search Capabilitymentioning

confidence: 99%

“…After that, the population is also divided into n dec classes with Algorithm 1, and the worst individuals in each class are then deleted. (20) In this case, the deleted individuals would be evenly distributed in the population, and thus the diversity of the population can be maintained.…”

Section: Optimization Of Search Capabilitymentioning

confidence: 99%

“…The reason is that they are shown to be highly effective and can find approximately optimal solutions in polynomial time rather than exponential time, compared to conventional methods [3], [13]. In fact, various metaheuristics as well as their variations have been used to solve scheduling problems in many fields [14], [15], [16], [17], [18], [19], [20], which also include the cloud computing. As summarized by the latest survey [21], currently metahuristics used in cloud task scheduling mainly include the genetic algorithm (GA) [22] and swarm intelligence algorithms, such as the ant colony optimization (ACO) [23] and the particle swarm optimization (PSO) [24].…”

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confidence: 99%

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A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Chen

Cheng

Liu

et al. 2020

IEEE Systems Journal

214

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Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (the whale optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called IWC (Improved WOA for Cloud task scheduling) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks. Index Terms-Cloud computing; task scheduling; whale optimization algorithm; metaheuristics; multi-objective optimization I. INTRODUCTION W Ith the ubiquitous growth of Internet access and big data, cloud computing becomes more and more popular in today's business world [1]. Compared to other distributed computing techniques (e.g., cluster and grid computing), cloud computing has provided an elastic and scalable way on delivering services to consumers. Namely, consumers do not need to possess the underlying technology and they can make use of computing resources and platforms in a pay-per-use fashion [2], [3]. The basic mechanism of cloud computing is to dispatch computing tasks to a resource pooling constituting of a

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Section: Optimization Of Search Capabilitymentioning

confidence: 99%

Section: Optimization Of Search Capabilitymentioning

confidence: 99%

mentioning

confidence: 99%

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A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Chen

Cheng

Liu

et al. 2020

IEEE Systems Journal

214

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“…In recent years, parallel machine scheduling problems (PMSPs) have attracted significant attention because they are used in different industrial applications and considered to be important key factors for sustainability at the operational level [1][2][3]. This kind of problems aims at assigning a set of jobs to a number of parallel machines with satisfying the requirements of the customers [4].…”

Section: Introductionmentioning

confidence: 99%

Sine-Cosine Algorithm to Enhance Simulated Annealing for Unrelated Parallel Machine Scheduling with Setup Times

et al. 2019

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This paper presents a hybrid method of Simulated Annealing (SA) algorithm and Sine Cosine Algorithm (SCA) to solve unrelated parallel machine scheduling problems (UPMSPs) with sequence-dependent and machine-dependent setup times. The proposed method, called SASCA, aims to improve the SA algorithm using the SCA as a local search method. The SCA provides a good tool for the SA to avoid getting stuck in a focal point and improving the convergence to an efficient solution. SASCA algorithm is used to solve UPMSPs by minimizing makespan. To evaluate the performance of SASCA, a set of experiments were performed using 30 tests for 4 problems. Moreover, the performance of the proposed method was compared with other meta-heuristic algorithms. The comparison results showed the superiority of SASCA over other methods in terms of performance dimensions.

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“…Production scheduling is of great significance in both scientific study and engineering applications [1][2][3][4][5]. Generally, the aim of the job shop scheduling problem (JSS) is to find a schedule that minimizes certain performance objective, given a set of machines and a set of jobs.…”

Section: Introductionmentioning

confidence: 99%

A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling

2019

Mathematics

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In the real-world manufacturing system, various uncertain events can occur and disrupt the normal production activities. This paper addresses the multi-objective job shop scheduling problem with random machine breakdowns. As the key of our approach, the robustness of a schedule is considered jointly with the makespan and is defined as expected makespan delay, for which a meta-model is designed by using a data-driven response surface method. Correspondingly, a multi-objective evolutionary algorithm (MOEA) is proposed based on the meta-model to solve the multi-objective optimization problem. Extensive experiments based on the job shop benchmark problems are conducted. The results demonstrate that the Pareto solution sets of the MOEA are much better in both convergence and diversity than those of the algorithms based on the existing slack-based surrogate measures. The MOEA is also compared with the algorithm based on Monte Carlo approximation, showing that their Pareto solution sets are close to each other while the MOEA is much more computationally efficient.

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Two-Machine Job-Shop Scheduling with Equal Processing Times on Each Machine

Cited by 8 publications

References 24 publications

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Sine-Cosine Algorithm to Enhance Simulated Annealing for Unrelated Parallel Machine Scheduling with Setup Times

A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling

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