A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times

Zhang, Q.; Manier, Hervé; Manier, Marié-Ange

doi:10.1016/j.cor.2011.10.007

Cited by 142 publications

(73 citation statements)

References 36 publications

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“…The long record of good results obtained with hybrid methods that combine genetic algorithms (GA) and different local search methods, in particular Tabu Search (TS), supports the choice of this kind of metaheuristic [30], [21], [10]. It is wellknown that a key component for the success of a TS algorithm is the neighbourhood structure used in it.…”

Section: The Hybrid Algorithmmentioning

confidence: 99%

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Genetic tabu search for the fuzzy flexible job shop problem

Palacios

González

Vela

et al. 2015

Computers & Operations Research

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This paper tackles the flexible job-shop scheduling problem with uncertain processing times. The uncertainty in processing times is represented by means of fuzzy numbers, hence the name fuzzy flexible job-shop scheduling. We propose an effective genetic algorithm hybridised with tabu search and heuristic seeding to minimise the total time needed to complete all jobs, known as makespan. To build a high-quality and diverse set of initial solutions we introduce a heuristic method which benefits from the flexible nature of the problem. This initial population will be the starting point for the genetic algorithm, which then applies tabu search to every generated chromosome. The tabu search algorithm relies on a neighbourhood structure that is proposed and analysed in this paper; in particular, some interesting properties are proved, such as feasibility and connectivity. Additionally, we incorporate a filtering mechanism to reduce the neighbourhood size and a method that allows to speed-up the evaluation of new chromosomes. To assess the performance of the resulting method and compare it with the state-of-the-art, we present an extensive computational study on a benchmark with 205 instances, considering both deterministic and fuzzy instances to enhance the significance of the study. The results of these experiments clearly show that not only does the hybrid algorithm benefit from the synergy among its components but it is also quite competitive with the state-of-the-art when solving both crisp and fuzzy instances, providing new best-known solutions for a number of these test instances. Genetic Tabu AbstractThis paper tackles the flexible job-shop scheduling problem with uncertain processing times. The uncertainty in processing times is represented by means of fuzzy numbers, hence the name fuzzy flexible job-shop scheduling. We propose an effective genetic algorithm hybridised with tabu search and heuristic seeding to minimise the total time needed to complete all jobs, known as makespan. To build a high-quality and diverse set of initial solutions we introduce a heuristic method which benefits from the flexible nature of the problem. This initial population will be the starting point for the genetic algorithm, which then applies tabu search to every generated chromosome. The tabu search algorithm relies on a neighbourhood structure that is proposed and analysed in this paper; in particular, some interesting properties are proved, such as feasibility and connectivity. Additionally, we incorporate a filtering mechanism to reduce the neighbourhood size and a method that allows to speed-up the evaluation of new chromosomes. To assess the performance of the resulting method and compare it with the state-of-the-art, we present an extensive computational study on a benchmark with 205 instances, considering both deterministic and fuzzy instances to enhance the significance of the study. The results of these experiments clearly show that not only does the hybrid algorithm benefit from the synergy among its component...

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Section: The Hybrid Algorithmmentioning

confidence: 99%

“…Fuzzy processing times and flexibility on the machines can be considered simultaneously, as done for example in [10]. When this is the case, we have the fuzzy flexible job-shop scheduling problem (FfJSP).…”

Section: Introductionmentioning

confidence: 99%

Genetic tabu search for the fuzzy flexible job shop problem

Palacios

González

Vela

et al. 2015

Computers & Operations Research

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“…Although, due to the complexity of the problem, mainly metaheuristics have been investigated. The most used metaheuristics include Simulated Annealing (SA) (Li et al [106], Shao et al [159]), Tabu Search (TS) (Zhang et al [197], Li et al [104], Bożejko et al [18], Li et al [105]), Genetic Algorithm (GA) (Li and Huang [101], Costa [42], Defersha and Chen [52], Zhang et al [200,196], Azzouz et al [8]), Particle Swarm Optimization (PSO) (Huang et al [82], Singh et al…”

Section: Scheduling Problemmentioning

confidence: 99%

Flexible job-shop scheduling problem with resource recovery constraints

Devassia

Salazar-Aguilar

Boyer

2018

International Journal of Production Research

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“…A new chromosome representation and different crossover and mutation strategies are used to minimize the makespan in [11]. A hybrid genetic algorithm with tabu search is proposed to solve FJSP with transportation and bounded processing times constraints in [15]. An improved genetic algorithm for solving the distributed and flexible job shop scheduling problem was introduced in [14].…”

Section: Introductionmentioning

confidence: 99%

A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm

Wang

Luo

Cai

2017

Journal of Advanced Transportation

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In real-world manufacturing systems, production scheduling systems are often implemented under random or dynamic events like machine failure, unexpected processing times, stochastic arrival of the urgent orders, cancellation of the orders, and so on. These dynamic events will lead the initial scheduling scheme to be nonoptimal and/or infeasible. Hence, appropriate dynamic rescheduling approaches are needed to overcome the dynamic events. In this paper, we propose a dynamic rescheduling method based on variable interval rescheduling strategy (VIRS) to deal with the dynamic flexible job shop scheduling problem considering machine failure, urgent job arrival, and job damage as disruptions. On the other hand, an improved genetic algorithm (GA) is proposed for minimizing makespan. In our improved GA, a mix of random initialization population by combining initialization machine and initialization operation with random initialization is designed for generating high-quality initial population. In addition, the elitist strategy (ES) and improved population diversity strategy (IPDS) are used to avoid falling into the local optimal solution. Experimental results for static and several dynamic events in the FJSP show that our method is feasible and effective.

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A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times

Cited by 142 publications

References 36 publications

Genetic tabu search for the fuzzy flexible job shop problem

Genetic tabu search for the fuzzy flexible job shop problem

Flexible job-shop scheduling problem with resource recovery constraints

A Variable Interval Rescheduling Strategy for Dynamic Flexible Job Shop Scheduling Problem by Improved Genetic Algorithm

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