An effective genetic algorithm for the flexible job-shop scheduling problem

Zhang, Guo Hui; Gao, Liang; Shi, Yang

doi:10.1016/j.eswa.2010.08.145

Cited by 354 publications

(109 citation statements)

References 20 publications

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“…It was also shown that indirect representations can achieve better results in scenarios with large search spaces. Similarly, [4] described an encoding scheme designed for machine selection and operation sequencing that respects all constraints while implementing different crossovers. The encoding scheme ensured the generation of a high-quality initial population with better performance when compared to previously published algorithms.…”

Section: Related Workmentioning

confidence: 99%

A genetic algorithm for a workforce scheduling and routing problem

Algethami

Pinheiro

Landa-Silva

2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits across various geographical locations. Solving this problem demands tackling scheduling and routing constraints while aiming to minimise the total operational cost. This paper presents a Genetic Algorithm (GA) tailored to tackle a set of real-world instances of this problem. The proposed GA uses a customised chromosome representation to maintain the feasibility of solutions. The performance of several genetic operators is investigated in relation to the tailored chromosome representation. This paper also presents a study of parameter settings for the proposed GA in relation to the various problem instances considered. Results show that the proposed GA, which incorporates tailored components, performs very well and is an effective baseline evolutionary algorithm for this difficult problem.

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Section: Related Workmentioning

confidence: 99%

A genetic algorithm for a workforce scheduling and routing problem

Algethami

Pinheiro

Landa-Silva

2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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“…The existing literatures [2][3][4][5] about solving singleobjective FJSP (SOFJSP) over the past decades mainly concentrated on minimizing one specific objective such as makespan. However, in practical manufacturing process, single-objective optimization cannot fully satisfy the production requirements since many optimized objectives are usually in conflict with each other.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

2017

Mathematical Problems in Engineering

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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.

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“…FJSP is known to be strongly NP-hard. Consequently, most of the literature related to the FJSP is based on metaheuristic methods like genetic algorithms (GAs) (Zhou et al, 2006;Pezzella et al, 2008;Zhang et al, 2011;Zambrano Rey et al, 2014), particle swarm optimization (PSO) (Zhang et al, 2009;Nouiri et al, 2015) simulated annealing (SA) (Najid et al, 2002;Yazdani et al, 2009), tabu search (TS) (Brandimarte, 1993;Fatahi et al, 2007;Vilcot and Billaut, 2011) and beam search (BS) (Wang et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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

Azzouz

Ennigrou

Saïd

2017

Int. J. Prod. Manag. Eng.

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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.

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An effective genetic algorithm for the flexible job-shop scheduling problem

Cited by 354 publications

References 20 publications

A genetic algorithm for a workforce scheduling and routing problem

A genetic algorithm for a workforce scheduling and routing problem

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

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

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