Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic

Kacem, Imed; Hammadi, Slim; Borne, Pierre

doi:10.1016/s0378-4754(02)00019-8

Cited by 462 publications

(256 citation statements)

References 8 publications

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“…This strategy was inspired by both the rule-based algorithms currently used by Optimatix, as well as elements of Kacem, Hammadi, and Borne's (2002b) genetic algorithm-based approach to flexible job shop scheduling. Similar to the RKPSO, the particle representation consists of one n-dimensional vector which represents the sequencing variables.…”

Section: The Rule-based Pso Algorithmmentioning

confidence: 99%

“…By considering the classical FJSP literature a number of potential benchmark algorithms can be identified. These include the work of Kacem et al (2002aKacem et al ( , 2002b, Gao et al (2006), and Xia and Wu (2005). However, these algorithms specialize in solving classical FJSPs and it is the authors' opinion that they cannot be extended easily to address more constrained problems without significantly changing the structure of the algorithms, even when sequencedependent set-up times and resource-dependent processing times are excluded from the problem.…”

Section: An Existing Algorithm Selected From Literaturementioning

confidence: 99%

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Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

Grobler

Engelbrecht²,

Kok³

et al. 2009

Ann Oper Res

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This paper investigates the application of particle swarm optimization (PSO) to the multi-objective flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and machine down time. To achieve this goal, alternative particle representations and problem mapping mechanisms were implemented within the PSO paradigm. This resulted in the development of four PSO-based heuristics. Benchmarking on real customer data indicated that using the priority-based representation resulted in a significant performance improvement over the existing rule-based algorithms commonly used to solve this problem. Additional investigation into algorithm scalability led to the development of a priority-based differential evolution algorithm. Apart from the academic significance of the paper, the benefit of an improved production schedule can be generalized to include cost reduction, customer satisfaction, improved profitability, and overall competitive advantage.

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Section: The Rule-based Pso Algorithmmentioning

confidence: 99%

Section: An Existing Algorithm Selected From Literaturementioning

confidence: 99%

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

Grobler

Engelbrecht²,

Kok³

et al. 2009

Ann Oper Res

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show abstract

“…These benchmark instances are all taken from of Brandimarte [3] and from Kacem [11] as well as those from GENACE [14]. The different results obtained by proposed approach is presented and compared with the other methods in table 9.…”

Section: Validation and Comparisonmentioning

confidence: 99%

Ant systems & Local Search Optimization for flexible Job Shop Scheduling Production

Liouane

Saad

Hammadi

et al. 2007

INT J COMPUT COMMUN

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The problem of efficiently scheduling production jobs on several machines is an important consideration when attempting to make effective use of a multimachines system such as a flexible job shop scheduling production system (FJSP). In most of its practical formulations, the FJSP is known to be NP-hard [8][9], so exact solution methods are unfeasible for most problem instances and heuristic approaches must therefore be employed to find good solutions with reasonable search time. In this paper, two closely related approaches to the resolution of the flexible job shop scheduling production system are described. These approaches combine the Ant system optimisation meta-heuristic (AS) with local search methods, including tabu search. The efficiency of the developed method is compared with others.

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“…Kacem et al embed the local optimization algorithm within evolution algorithm [2]. Later they added fuzzy logic into their scheme and used Pareto method to tradeoff between makespan and machine workload [3]. Gao et al [4] developed an adapted genetic algorithm (GA) hybridized with a shifting bottleneck strategy for MOFJSP.…”

Section: Introductionmentioning

confidence: 99%

Solving the Problem of Multi-objective Flexible Job Shop Based on Hybrid Genetic Algorithm and Particle Swarm Optimization

Huang¹

2018

Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)

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Abstract-A teaching-learning-based hybrid genetic-particle swarm optimization algorithm is proposed for multi-objective flexible job shop scheduling problem. It includes three modules: genetic algorithm (GA), bi-memory learning (BL) and particle swarm optimization (PSO). Firstly, in the BL module, a learning mechanism is introduced into GA to generate chromosomes which have a self-learning characteristic. During the process of evolution, the offspring in GA learn the characteristics of good chromosomes in the BL. Then, a discretization PSO algorithm which iterates the genetic population and particle population simultaneously is proposed. Finally, experiments are conducted to compare the rationality and validity of the proposed algorithm with others.

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Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic

Cited by 462 publications

References 8 publications

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

Ant systems & Local Search Optimization for flexible Job Shop Scheduling Production

Solving the Problem of Multi-objective Flexible Job Shop Based on Hybrid Genetic Algorithm and Particle Swarm Optimization

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