A particle swarm optimization algorithm for flexible job shop scheduling problem

Girish, B S; Jawahar, N.

doi:10.1109/coase.2009.5234153

Cited by 26 publications

(18 citation statements)

References 21 publications

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“…As we can observe, for three in- stances HGTS obtains the optimal solution in every single run, while for the remaining instances it reaches the best-known solution, as it is also the case with the other methods. The results on the Thomalla's benchmark [61] are compared with the results reported for the PSO by Girish and Jawahar in [70] together with those obtained by ILOG OPL Studio, also reported in [70]. In this paper best-known solution (BKS) values are also given.…”

Section: Comparison With the State-of-the-art In The Fjspmentioning

confidence: 87%

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: Comparison With the State-of-the-art In The Fjspmentioning

confidence: 87%

Genetic tabu search for the fuzzy flexible job shop problem

Palacios

González

Vela

et al. 2015

Computers & Operations Research

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“…Particle swarm optimization is a population based stochastic optimization technique for the solution of continuous optimization problems (Kennedy and Eberhart -1995) [15]. In particle swarm optimization (PSO), a set of software agents called particles search for best solutions to a given continuous optimization problem.…”

Section: B Particle Swarm Optimizationmentioning

confidence: 99%

“…A more extensive class of mathematical problems is solved and as a consequence, several problems have been merged. In ACO, search for global solutions to a given optimization problem is transformed for finding the cost path in the weighted graph which is minimal [15]. Solutions built by artificial ants moving on the graph.…”

Section: A Ant Colony Optimizationmentioning

confidence: 99%

Hybridization of Evolutionary and Swarm Intelligence Techniques for Job Scheduling Problem

Rani

2015

Multi-Objective Swarm Intelligence

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Abstract-For more than a decade, scheduling of jobs has been an attractive research subject for researchers. There are several different ways to schedule jobs, and the threads which make them up. As well, the job scheduling is one of the active research fields, where the researchers work to enhance the efficiency of the job scheduling process in a scheduling environment. In existing hybrid techniques, some efficient factors related to jobs like turnaround time, job execution time and more have not been considered in the job scheduling process. The main drawback is lack of factors in the scheduling process which reduces the performance. To overcome such drawback in the existing methods, an adaptive ABC technique is proposed. In this proposed adaptive ABC technique, the term adaptiveness is achieved by using mutation, crossover and velocity in the employed bee phase for finding the new food sources. The adaptive ABC algorithm optimally allocates the jobs to the accurate processors or resources. Moreover, these existing techniques mostly concentrate on two major factors such as the minimization of the makespan and the completion time. The adaptiveness improves the efficiency of scheduling process when compared to the two conventional hybrid job scheduling techniques. The experimental result shows the performance of the proposed job scheduling process.

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“…More recently, with the emergence of new techniques from the field of artificial intelligence, much attention has been devoted to meta-heuristics. The tabu search (TS) has been widely used, such as Brandimart 11 , Mastrolilli and Gambardella 12 , Bozejko et al 13 and Li et al 14 , while the genetic algorithm (GA) has also been examined to be an efficient method such as in Chen et al 15 , Kacem et al 16 , Pezzella et al 17 and Gao et al 18 Besides, some other meta-heuristics have been employed for this problem such as simulated annealing (SA) [19][20][21] , particle swarm optimization (PSO) [22][23][24] , ant colony optimization (ACO) 25 , artificial neural network (ANN) 26 , and artificial immune system (AIS) 27 .…”

Section: Introductionmentioning

confidence: 99%

An Efficient Artificial Fish Swarm Model with Estimation of Distribution for Flexible Job Shop Scheduling

Ge¹,

Sun²,

Chen³

et al. 2016

IJCIS

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The flexible job shop scheduling problem (FJSP) is one of the most important problems in the field of production scheduling, which is the abstract of some practical production processes. It is a complex combinatorial optimization problem due to the consideration of both machine assignment and operation sequence. In this paper, an efficient artificial fish swarm model with estimation of distribution (AFSA-ED) is proposed for the FJSP with the objective of minimizing the makespan. Firstly, a pre-principle and a post-principle arranging mechanism that operate by adjusting machine assignment and operation sequence with different orders are designed to enhance the diversity of population. Following this, the population is divided into two sub-populations and then two arranging mechanisms are applied. In AFSA-ED, a preying behavior based on estimation of distribution is proposed to improve the performance of algorithm. Moreover, an attracting behavior is proposed to improve the global exploration ability and a public factor based critical path search strategy is proposed to enhance the local exploitation ability. Simulated experiments are carried on BRdata, BCdata and HUdata benchmark sets. The computational results validate the performance of the proposed algorithm in solving the FJSP, as compared with some other state of the art algorithms.

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A particle swarm optimization algorithm for flexible job shop scheduling problem

Cited by 26 publications

References 21 publications

Genetic tabu search for the fuzzy flexible job shop problem

Genetic tabu search for the fuzzy flexible job shop problem

Hybridization of Evolutionary and Swarm Intelligence Techniques for Job Scheduling Problem

An Efficient Artificial Fish Swarm Model with Estimation of Distribution for Flexible Job Shop Scheduling

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