A discrete artificial bee colony algorithm with composite mutation strategies for permutation flow shop scheduling problem

Xiao, Li; Yin, Minghao

doi:10.1016/j.scient.2012.10.034

Cited by 44 publications

(26 citation statements)

References 56 publications

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“…In [29][30][31][32][33][34][35][36][37], this local search has shown its high effectiveness in obtaining the better quality solutions than the local search based on swap operators. The curial idea of this method is to compare the previous makespan with the new makespan of the sequence, which has inserted a job into all positions of the sequence.…”

Section: Perturbation and Local Search Methodsmentioning

confidence: 99%

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A Hybrid Local Search Algorithm for the Sequence Dependent Setup Times Flowshop Scheduling Problem with Makespan Criterion

Wang

2017

Sustainability

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This paper focuses on the flowshop scheduling problem with sequence dependent setup times (FSSP-SDST), which has been an investigated object for decades as one of the most popular scheduling problems in manufacturing systems. A novel hybrid local search algorithm called HLS is presented to solve the flowshop scheduling problem with sequence dependent setup times with the criterion of minimizing the makespan. Firstly, the population is initialized by the Nawaz-Enscore-Hoam based problem-specific method (NEHBPS) to generate high quality individuals of the current population. Then, a global search embedded with a light perturbation is designed to produce a new population. After that, to improve the quality of individuals in the current population, a single insertion-based local search is applied. Meanwhile, a further local search strategy based on the insertion-based local search is used to find better solutions for the individuals which are non-improved. Finally, the heavy perturbation is used to explore potential solutions in the neighbor region. To validate the performance of HLS, we compare our proposed algorithm with other competitive algorithms on Taillard benchmark problems. From the experimental results, it can be concluded that the proposed algorithm outperforms the benchmark algorithms.

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Section: Perturbation and Local Search Methodsmentioning

confidence: 99%

“…18: 20: s i = s i ; isReplacing = true; break; 21: else 22: iterNum = iterNum + 1; 23: end if 24: end while 25: end if 26: if isReplacing = true then 27: Update the PotentialInd; 28: else 29: s i = HeavyPerturbation(s i ); 30: end if 31:…”

Section: Inputmentioning

confidence: 99%

A Hybrid Local Search Algorithm for the Sequence Dependent Setup Times Flowshop Scheduling Problem with Makespan Criterion

Wang

2017

Sustainability

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“…There are many metaheuristic algorithms that have been implemented in flow shop scheduling problems with makespan criterion including: Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Greedy approaches, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) algorithm, and Variable Neighborhood Search (VNS). Additionally, hybrid algorithms, combining some of these algorithms, have been developed in the literature [21]. In this paper, we choose some early constructive heuristic algorithms that have been clues for other researchers like Palmer"s [17], CDS [18], Gupta"s [19], and NEH [20] heuristics.…”

Section: Solution Methodsmentioning

confidence: 99%

On the n-job, m-machine permutation flow shop scheduling problems with makespan criterion and rework

Bootaki

Paydar

2017

Scientia Iranica

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This paper addresses an n-job, m-machine permutation flow shop scheduling problem (PFSSP) with unlimited intermediate buffers and rework activities. The concept of rework means that processing of a job on a machine may not meet a predefined quality level through its first process. Thus we have a probabilistic cycle of operations for jobs on different machines which is based on two concepts: (1) a failure probability of a job on a machine and, (2) a descent rate that reduces the processing times for rework phase. In this case, the processing times of jobs on machines become random variables with a known probability distribution. The aim of this paper is to examine possible solution approaches for generating the efficient job sequences with the least potential makespan. A wide range of simulationbased approaches are applied to address the proposed problem. These methods contain mathematical formulation, heuristic algorithms, and metaheuristics. The mechanism of the solution approaches is based on firstly using expected processing times to find a job sequence; then evaluating the obtained job sequences by several simulated trials. Using the one-way ANOVA test, these methods have been compared together, and the results show the superiority of metaheuristics, especially simulated annealing, over the other methods.

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“…The employed bee of an abandoned food source becomes a scout bee, and as soon as it finds a new food source, it becomes an employed bee again. The ABC algorithm is an iterative algorithm [14]. It starts by associating all employed bees with randomly generated food sources (solution).…”

Section: Scout Bee Phasementioning

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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A discrete artificial bee colony algorithm with composite mutation strategies for permutation flow shop scheduling problem

Cited by 44 publications

References 56 publications

A Hybrid Local Search Algorithm for the Sequence Dependent Setup Times Flowshop Scheduling Problem with Makespan Criterion

A Hybrid Local Search Algorithm for the Sequence Dependent Setup Times Flowshop Scheduling Problem with Makespan Criterion

On the n-job, m-machine permutation flow shop scheduling problems with makespan criterion and rework

Hybridization of Evolutionary and Swarm Intelligence Techniques for Job Scheduling Problem

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