Branch-and-bound and PSO algorithms for no-wait job shop scheduling

AitZai, Abdelhakim; Benmedjdoub, Brahim; Boudhar, Mourad

doi:10.1007/s10845-014-0906-7

Cited by 54 publications

(22 citation statements)

References 28 publications

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“…This problem has been widely studied in the literature using various optimization approaches based on greedy algorithms such as particle swarm optimization and genetic algorithms, . However, in the context of resource sharing environments, these general approaches are ill‐equipped to address the variability of resource consumption and interference of jobs.…”

Section: Related Workmentioning

confidence: 99%

Static job scheduling for environments with vertical elasticity

Kloh

Rebello

Boeres

et al. 2020

Concurrency and Computation

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Summary In virtualized environments, such as Clouds, allocating a fixed amount of resources to a job a priori, may result in underutilization of the shared host. Meanwhile, vertical elasticity can be adopted to reduce the impact by resizing virtual machines (VMs) dynamically, in conjunction with suspension and/or migration before the host had been overloaded. In order to reduce the number of such events, but still avoid overloading the host, it is important to find an effective initial VM allocation. To achieve this, the scheduler must work in unison with the local host elasticity controllers to reduce interference. This work proposes and evaluates a framework for job scheduling in elastic memory managed virtualized environments. The memory elasticity management in clouds (MEMiC) framework is a two‐tier VM scheduler for batch jobs which attempts to predict the impact caused by competition for the memory of a host in shared Cloud‐like environments for harnessing VM allocation, suspension, and migration. Evaluations show that the MEMiC achieves a reduction of approximately 19% in the total time for jobs execution in comparison to other approaches, by reducing the degree of interference.

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Section: Related Workmentioning

confidence: 99%

Static job scheduling for environments with vertical elasticity

Kloh

Rebello

Boeres

et al. 2020

Concurrency and Computation

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“…Wang and Li [12] proposed an accelerated tabu search (TS) to minimize the maximum lateness. AitZai et al [27] investigated a branchand-bound algorithm and a particle swarm optimization (PSO) algorithm. Pan and Wang [14] proposed an improved iterated greedy algorithm (IIGA) for F m |prmu, no−wait|C max .…”

Section: Related Workmentioning

confidence: 99%

An Iterated Greedy Heuristic for Mixed No-Wait Flowshop Problems

Wang

Ruíz

et al. 2018

IEEE Trans. Cybern.

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The mixed no-wait flowshop problem with both wait and no-wait constraints has many potential real-life applications. The problem can be regarded as a generalization of the traditional permutation flowshop and the no-wait flowshop. In this paper, we study, for the first time, this scheduling setting with makespan minimization. We first propose a mathematical model and then we design a speed-up makespan calculation procedure. By introducing a varying number of destructed jobs, a modified iterated greedy algorithm is proposed for the considered problem which consists of four components: 1) initialization solution construction; 2) destruction; 3) reconstruction; and 4) local search. To further improve the intensification and efficiency of the proposal, insertion is performed on some neighbor jobs of the best position in a sequence during the initialization, solution construction, and reconstruction phases. After calibrating parameters and components, the proposal is compared with five existing algorithms for similar problems on adapted Taillard benchmark instances. Experimental results show that the proposal always obtains the best performance among the compared methods.

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“…From past to present, a large number of techniques have been proposed for solving the JSSP, which can be classified into two categories: a mathematical approach and an approximation approach. Historically, the use of mathematic methods, such as integer programming [1] or branch and bound algorithm [2,3], for solving the JSSP were popular because they could achieve an optimal solution. However, the mathematics optimization methods have limitations on the large-scale problems due to polynomialtime solutions.…”

Section: Introductionmentioning

confidence: 99%

A Study on Performance Evaluation of Different Parameter Settings of Particle Swarm Optimization in Job Shop Scheduling Problem

Poolsamran

2019

Proceedings of the 2019 2nd International Conference on Information Science and Systems

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This study is to present a particle swarm optimization (PSO) to solve a job shop scheduling problem with the objective of minimizing a makespan. The solution representation of a particle that is encoded with a permutation of all operations on jobs. The operations are ordered by precedence constraints. For the performance evaluation, each particle is evaluated by a quality of the fitness function. In the particle swarm optimization model, the effect of parameters, like the swarm size, on performance are investigated in this paper. In different swarm sizes, the performance of proposed methodology is tested on forty wellknown benchmark problems from the OR-library. The computational results demonstrate that the proposed methodology can effectively solve a job shop scheduling problem and also take less processing time although run on a personal computer. CCS Concepts• Computing methodologies ➝ Planning and scheduling. Keywordsparticle swarm optimization (PSO); job shop scheduling problem (JSSP); parameter setting; permutation encoding schema.

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Branch-and-bound and PSO algorithms for no-wait job shop scheduling

Cited by 54 publications

References 28 publications

Static job scheduling for environments with vertical elasticity

Static job scheduling for environments with vertical elasticity

An Iterated Greedy Heuristic for Mixed No-Wait Flowshop Problems

A Study on Performance Evaluation of Different Parameter Settings of Particle Swarm Optimization in Job Shop Scheduling Problem

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