A hybrid Differential Evolution—Tabu Search algorithm for the solution of Job-Shop Scheduling Problems

Ponsich, Antonin; Coello, Carlos A. Coello

doi:10.1016/j.asoc.2012.07.034

Cited by 84 publications

(29 citation statements)

References 33 publications

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“…The agent-based parallel approach was used to parallelize the genetic algorithm and to accelerate the creation of the initial population of genetic algorithm. Some other recent studies on the problem are: (Gao et al, 2011;Kammer et al, 2011;Lochtefeld & Ciarallo, 2011;Mati et al, 2011;Sels et al, 2011;Ponsich & Coello, 2013;Zhang et al, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

Job shop scheduling with makespan objective: A heuristic approach

Ziaee¹

2014

10.5267/j.ijiec

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Job shop has been considered as one of the most challenging scheduling problems and there are literally tremendous efforts on reducing the complexity of solution procedure for solving job shop problem. This paper presents a heuristic method to minimize makespan for different jobs in a job shop scheduling. The proposed model is based on a constructive procedure to obtain good quality schedules, very quickly. The performance of the proposed model of this paper is examined on standard benchmarks from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic is computationally efficient and practical approach for the problem.

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Section: Literature Reviewmentioning

confidence: 99%

Job shop scheduling with makespan objective: A heuristic approach

Ziaee¹

2014

10.5267/j.ijiec

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“…DE with TS DE-TS(Ponsich & Coello, 2013) GA with TS GATS(Meeran & Morshed, 2014). Asexual GA with TS and GT algorithms TGA(Amirghasemi & Zamani, 2015).…”

mentioning

confidence: 99%

A new hybrid island model genetic algorithm for job shop scheduling problem

Kurdi

2015

Computers & Industrial Engineering

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“…Currently, DE has been successfully applied to solve optimization problems in a considerable number of fields, such as electrical and power systems [24], [25], manufacturing science and operational research [26], [27], automotive design [28], and controller design [13], [14], [15].…”

Section: B Differential Evolution (De)mentioning

confidence: 99%

Composite Differential Evolution for Constrained Evolutionary Optimization

Wang

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

138

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Abstract-When solving constrained optimization problems by evolutionary algorithms, the search algorithm plays a crucial role. In general, we expect that the search algorithm has the capability to balance not only diversity and convergence but also constraints and objective function during the evolution. For this purpose, this paper proposes a composite differential evolution for constrained optimization, which includes three different trial vector generation strategies with distinct advantages. In order to strike a balance between diversity and convergence, one of these three trial vector generation strategies is able to increase diversity, and the other two exhibit the property of convergence. In addition, to accomplish the tradeoff between constraints and objective function, one of the two trial vector generation strategies for convergence is guided by the individual with the least degree of constraint violation in the population, and the other is guided by the individual with the best objective function value in the population. After producing offspring by the proposed composite differential evolution, the feasibility rule and the ε constrained method are combined elaborately for selection in this paper. Moreover, a restart scheme is proposed to help the population jump out of a local optimum in the infeasible region for some extremely complicated constrained optimization problems. By assembling the above techniques together, a constrained composite differential evolution is proposed. The experiments on two sets of benchmark test functions with various features, i.e., 24 test functions from IEEE CEC2006 and 18 test functions with 10 dimensions and 30 dimensions from IEEE CEC2010, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods.

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A hybrid Differential Evolution—Tabu Search algorithm for the solution of Job-Shop Scheduling Problems

Cited by 84 publications

References 33 publications

Job shop scheduling with makespan objective: A heuristic approach

Job shop scheduling with makespan objective: A heuristic approach

A new hybrid island model genetic algorithm for job shop scheduling problem

Composite Differential Evolution for Constrained Evolutionary Optimization

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