Hybrid flow shop scheduling using genetic algorithms
28 Jun 2000-Vol. 1, pp 537-541
TL;DR: In this article, the authors investigated the genetic algorithm approach for scheduling hybrid flow shops with minimum makespan as performance measure, which is characterized as the scheduling of jobs in a flow shop environment where, at any stage, there may exist multiple machines.
Abstract: We investigate the genetic algorithm approach for scheduling hybrid flow shops with minimum makespan as performance measure The hybrid flow shop problem is characterized as the scheduling of jobs in a flow shop environment where, at any stage, there may exist multiple machines The algorithm is based on the list scheduling principle by developing job sequences for the first stage and queuing the remaining stages in a FIFO manner Experiments show that the proposed algorithm outperforms existing heuristic procedures and random search methods
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TL;DR: A literature review on exact, heuristic and metaheuristic methods that have been proposed for the solution of the hybrid flow shop problem is presented.
647 citations
TL;DR: The computational results indicate that the proposed efficient genetic algorithm approach is effective in terms of reduced total completion time or makespan (Cmax) for HFS problems.
Abstract: This paper addresses the Hybrid Flow Shop (HFS) scheduling problems to minimize the makespan value. In recent years, much attention is given to heuristic and search techniques. Genetic algorithms (GAs) are also known as efficient heuristic and search techniques. This paper proposes an efficient genetic algorithm for hybrid flow shop scheduling problems. The proposed algorithm is tested by Carlier and Neron's (2000) benchmark problem from the literature. The computational results indicate that the proposed efficient genetic algorithm approach is effective in terms of reduced total completion time or makespan (Cmax) for HFS problems.
81 citations
TL;DR: A genetic algorithm to solve the hybrid flow shop scheduling problem to minimize the total tardiness is presented, which incorporates a new decoding method developed for total tardy objective that is able to obtain tight schedule meanwhile guarantee the influence of the chromosome on the schedule.
77 citations
TL;DR: A mathematical model, four variants of iterated greedy algorithms and a variable block insertion heuristic for the HFSP with total flow time minimization based on the well-known NEH heuristic, an efficient constructive heuristic is also proposed, and compared with NEH.
54 citations
TL;DR: The results indicate that the proposed PSO algorithm is quite effective in reducing makespan because average PD is observed as 2.961, whereas GA results in average percentage deviation of 3.559.
Abstract: In simple flow shop problems, each machine operation center includes just one machine. If at least one machine center includes more than one machine, the scheduling problem becomes a flexible flow shop problem (FFSP). Flexible flow shops are thus generalization of simple flow shops. Flexible flow shop scheduling problems have a special structure combining some elements of both the flow shop and the parallel machine scheduling problems. FFSP can be stated as finding a schedule for a general task graph to execute on a multiprocessor system so that the schedule length can be minimized. FFSP is known to be NP-hard. In this study, we present a particle swarm optimization (PSO) algorithm to solve FFSP. PSO is an effective algorithm which gives quality solutions in a reasonable computational time and consists of less numbers parameters as compared to the other evolutionary metaheuristics. Mutation, a commonly used operator in genetic algorithm, has been introduced in PSO so that trapping of solutions at local minima or premature convergence can be avoided. Logistic mapping is used to generate chaotic numbers in this paper. Use of chaotic numbers makes the algorithm converge fast towards near-optimal solution and hence reduce computational efforts further. The performance of schedules is evaluated in terms of total completion time or makespan (Cmax). The results are presented in terms of percentage deviation (PD) of the solution from the lower bound. The results are compared with different versions of genetic algorithm (GA) used for the purpose from open literature. The results indicate that the proposed PSO algorithm is quite effective in reducing makespan because average PD is observed as 2.961, whereas GA results in average percentage deviation of 3.559. Finally, influence of various PSO parameters on solution quality has been investigated.
44 citations
References
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TL;DR: A simple algorithm for the solution of very large sequence problems without the use of a computer that produces approximate solutions to the n job, m machine sequencing problem where no passing is considered and the criterion is minimum total elapsed time.
Abstract: This paper describes a simple algorithm for the solution of very large sequence problems without the use of a computer. It produces approximate solutions to the n job, m machine sequencing problem where no passing is considered and the criterion is minimum total elapsed time. Up to m-1 sequences may be found.
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TL;DR: A Genetic Algorithm is developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem and the performance of the algorithm is compared with that of a naive Neighbourhood Search technique and with a proven Simulated Annealing algorithm.
849 citations
TL;DR: In this article, a scheduling algorithm is described which employs discrete simulation in combination with straightforward part dispatching rules in a dynamic fashion, instead of scheduling being planned ahead of time and then being applied to a rapidly changing system, a dispatching rule is determined for each short period just before the implementation time occurs.
Abstract: The on-line control and scheduling of flexible manufacturing systems has been a major interest in the production research area since these systems first appeared. In this paper, a scheduling algorithm is described which employs discrete simulation in combination with straightforward part dispatching rules in a dynamic fashion. The result is that, instead of scheduling being planned ahead of time and then being applied to a rapidly changing system, a dispatching rule is determined for each short period just before the implementation time occurs. In the long run, the algorithm combines various dispatching rules in response to the dynamic status of the system. The algorithm is described in detail. The efficacy of the algorithm is discussed and demonstrated on a prototype system.
245 citations
TL;DR: In this paper, a branch and bound algorithm is presented to solve scheduling problems of a flow shop with multiple processors for optimizing the maximum completion time, where the lower bounds and elimination rules developed in this research are based upon the generalization of the flow shop problem.
236 citations