Abstract
Flexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having ‘g’ operations is performed on ‘g’ operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem becomes a flexible flow shop problem (FFSP). FFSP which contains all the complexities involved in a simple flow shop and parallel machine scheduling problems is a well-known NP-hard (Non-deterministic polynomial time) problem. Owing to high computational complexity involved in solving these problems, it is not always possible to obtain an optimal solution in a reasonable computation time. To obtain near-optimal solutions in a reasonable computation time, a large variety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solving FFSP is rather tricky and time consuming. To address this limitation, teaching–learning-based optimization (TLBO) and JAYA algorithm are chosen for the study because these are not only recent meta-heuristics but they do not require tuning of algorithm-specific parameters. Although these algorithms seem to be elegant, they lose solution diversity after few iterations and get trapped at the local optima. To alleviate such drawback, a new local search procedure is proposed in this paper to improve the solution quality. Further, mutation strategy (inspired from genetic algorithm) is incorporated in the basic algorithm to maintain solution diversity in the population. Computational experiments have been conducted on standard benchmark problems to calculate makespan and computational time. It is found that the rate of convergence of TLBO is superior to JAYA. From the results, it is found that TLBO and JAYA outperform many algorithms reported in the literature and can be treated as efficient methods for solving the FFSP.
Highlights
Problems of scheduling occur in many economic domains, such as airplane scheduling, train scheduling, time table scheduling and especially in the shop scheduling of manufacturing organizations
To evaluate the effectiveness of Teaching–learning-based optimization (TLBO) and JAYA algorithms in solving flexible flow shop scheduling problem (FFSP), experiments have been conducted on 77 benchmark problems taken from Carlier and Neron (2000)
All the problems have been solved and the results are compared with various algorithms, such as BB, particle swarm optimization (PSO), genetic algorithm (GA), Artificial immune system (AIS), improved discrete artificial bee colony (IDABC), immune algorithm (IA), quantum algorithm (QA), and quantuminspired immune algorithm (QIA), from the literature available
Summary
Problems of scheduling occur in many economic domains, such as airplane scheduling, train scheduling, time table scheduling and especially in the shop scheduling of manufacturing organizations. A flexible flow shop scheduling problem (FFSP) has M jobs with ‘g’ number of operations to be carried out in ‘g’ stages. Teaching–learning-based optimization (TLBO) and JAYA are some of the recent optimization techniques that were proposed without any algorithm-specific tuning parameters in comparison with any other meta-heuristics. Choong et al (2011) have proposed hybrid algorithms combining tabu search (TS) and simulated annealing (SA) to particle swarm optimization (PSO) to improve efficiency of PSO in solving FFSP. A new local search technique is proposed in the present work to improve the solution quality of FFSP generated by present algorithms. These algorithms seem to be elegant, they. Mutation strategy from genetic algorithm is incorporated to the algorithm to maintain the diversity in the population
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