A self-tuning PSO for job-shop scheduling problems

Pongchairerks, Pisut

doi:10.1504/ijor.2014.057848

Cited by 8 publications

(8 citation statements)

References 20 publications

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“…Because can be adjusted to be any real number between 0 and 1, this parameter thus controls the algorithm's solution space between nondelay schedules and active schedules. Some variants of the hybrid scheduler of [16], which are modified for the purpose of simplification, can be found in published articles such as [15,31,33,34].…”

Section: Preliminariesmentioning

confidence: 99%

“…Some metaheuristic algorithms such as [16,33] use with fixed values. On the other hand, some other metaheuristic algorithms such as [34,35] adjust the values during their evolutionary processes; thus, their methods of changing the values belong to the classification of selfadaptive parameter control based on the definition given by [10]. In addition, some metaheuristic algorithms adjust the values based on the mathematical functions of another parameter (or other parameters); for example, the local search algorithms of [31] adjust the value based on the iteration's index.…”

Section: Preliminariesmentioning

confidence: 99%

“…Algorithm 1 can be defined as a generalization of the solution-decoding procedures shown in [15,31,33,34] since it can generate a parameterized-active schedule in either the forward direction or backward direction as the options. Algorithm 1 and its abovementioned variants are modified from the hybrid scheduler of [16] for the purpose of simplification (as found in Steps 5 and 6 of Algorithm 1).…”

Section: Decoding Procedures To Generate Parameterized-activementioning

confidence: 99%

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A Two‐Level Metaheuristic Algorithm for the Job‐Shop Scheduling Problem

Pongchairerks

2019

Complexity

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This paper proposes a novel two-level metaheuristic algorithm, consisting of an upper-level algorithm and a lower-level algorithm, for the job-shop scheduling problem (JSP). The upper-level algorithm is a novel population-based algorithm developed to be a parameter controller for the lower-level algorithm, while the lower-level algorithm is a local search algorithm searching for an optimal schedule in the solution space of parameterized-active schedules. The lower-level algorithm’s parameters controlled by the upper-level algorithm consist of the maximum allowed length of idle time, the scheduling direction, the perturbation method to generate an initial solution, and the neighborhood structure. The proposed two-level metaheuristic algorithm, as the combination of the upper-level algorithm and the lower-level algorithm, thus can adapt itself for every single JSP instance.

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Section: Preliminariesmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Section: Decoding Procedures To Generate Parameterized-activementioning

confidence: 99%

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A Two‐Level Metaheuristic Algorithm for the Job‐Shop Scheduling Problem

Pongchairerks

2019

Complexity

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“…JSP is important to industry and attractive to academia, so many algorithms have been developed for solving the problem. These algorithms include tabu search algorithms [4,5], a simulated annealing algorithm (SA) [6], a hybrid algorithm between particle swarm optimization (PSO) and VNS [7], genetic algorithms (GAs) [8][9][10][11][12][13], PSO algorithms [14][15][16], VNS algorithms [17][18][19], a hybrid algorithm between PSO and GA [20], a bee colony algorithm [21], an ant colony optimization algorithm [22], a memetic algorithm [23], and a hybrid algorithm between GA and SA [24]. Based on the literature review, the VNS algorithms are recognized as wellperforming algorithms for JSP, so this paper will research more on the VNS algorithms.…”

Section: Literature Review and Research Contributionmentioning

confidence: 99%

Forward VNS, Reverse VNS, and Multi-VNS Algorithms for Job-Shop Scheduling Problem

Pongchairerks

2016

Modelling and Simulation in Engineering

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This paper proposes a number of forward VNS and reverse VNS algorithms for job-shop scheduling problem. The forward VNS algorithms are the variable neighborhood search algorithms applied to the original problem (i.e., the problem instance with the original precedence constraints). The reverse VNS algorithms are the variable neighborhood search algorithms applied to the reversed problem (i.e., the problem instance with the reversed precedence constraints). This paper also proposes a multi-VNS algorithm which assigns an identical initial solution-representing permutation to the selected VNS algorithms, runs these VNS algorithms, and then uses the best solution among the final solutions of all selected VNS algorithms as its final result. The aim of the multi-VNS algorithm is to utilize each single initial solution-representing permutation most efficiently and thus receive its best result in return.

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“…Solution strategies presented for the JSP range from artificial bee colony optimization and hybrid genetic tabu searches (Banharnsakun et al 2012 ; Meeran and Morshed 2012 ; Zhang et al 2013 ; Zhang et al 2008b ) through dynamic and linear programming (Gromicho et al 2012 ; Bülbül and Kaminsky 2013 ) to path relinking and particle swarm optimization (Pongchairerks 2014 ; Nasiri and Kianfar 2012 ). In an overview of scheduling models presented in (Framinan et al 2014 ) several of these strategies have been examined.…”

Section: Related Workmentioning

confidence: 99%

A synergetic combination of small and large neighborhood schemes in developing an effective procedure for solving the job shop scheduling problem

Amirghasemi

Zamani

2014

SpringerPlus

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This paper presents an effective procedure for solving the job shop problem. Synergistically combining small and large neighborhood schemes, the procedure consists of four components, namely (i) a construction method for generating semi-active schedules by a forward-backward mechanism, (ii) a local search for manipulating a small neighborhood structure guided by a tabu list, (iii) a feedback-based mechanism for perturbing the solutions generated, and (iv) a very large-neighborhood local search guided by a forward-backward shifting bottleneck method. The combination of shifting bottleneck mechanism and tabu list is used as a means of the manipulation of neighborhood structures, and the perturbation mechanism employed diversifies the search. A feedback mechanism, called repeat-check, detects consequent repeats and ignites a perturbation when the total number of consecutive repeats for two identical makespan values reaches a given threshold. The results of extensive computational experiments on the benchmark instances indicate that the combination of these four components is synergetic, in the sense that they collectively make the procedure fast and robust.

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A self-tuning PSO for job-shop scheduling problems

Cited by 8 publications

References 20 publications

A Two‐Level Metaheuristic Algorithm for the Job‐Shop Scheduling Problem

A Two‐Level Metaheuristic Algorithm for the Job‐Shop Scheduling Problem

Forward VNS, Reverse VNS, and Multi-VNS Algorithms for Job-Shop Scheduling Problem

A synergetic combination of small and large neighborhood schemes in developing an effective procedure for solving the job shop scheduling problem

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