A Discrete Invasive Weed Optimization Algorithm for the No-Wait Lot-Streaming Flow Shop Scheduling Problems
The no-wait lot-streaming flow shop scheduling has important applications in modern industry. This paper deals with the makespan for the problems with equal-size sublots. A fast calculation method is designed to reduce the time complexity. A discrete inva
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Abstract. The no-wait lot-streaming flow shop scheduling has important applications in modern industry. This paper deals with the makespan for the problems with equal-size sublots. A fast calculation method is designed to reduce the time complexity. A discrete invasive weed optimization (DIWO) algorithm is proposed. In the proposed DIWO algorithm, job permutation representation is utilized, Nawaz–Enscore–Ham heuristic is used to generate initial solutions with high quality. A reference local search procedure is employed to perform local exploitation. Extensive computational simulations and comparisons are provided, which demonstrate the effectiveness of the proposed DIWO algorithm. Keywords: Invasive weed optimization Scheduling
Lot-streaming
No-wait
1 Introduction The lot-streaming flowshop scheduling problem (LFSP) has important applications in practical situations where a job is divided into many identical items. Since the late 1980s, lot- streaming flowshop scheduling technique has been extensively studied in academic as well as industrial fields [1]. Yoon and Ventura [2] presented sixteen pairwise interchange methods to search for the best sequence for an m-machine lot-streaming flowshop problem, where a linear programming (LP) formulation was designed to obtain optimal sublot completion times. Later, the same authors [3] proposed a hybrid genetic algorithm (HGA) by incorporating the LP and a pairwise interchange method into the traditional genetic algorithm. Some effective methods including: genetic algorithm (GA), hybrid genetic algorithm (HGA), ant colony optimization (ACO) algorithm, tabu search (TS) and threshold accepting (TA) algorithm were proposed to the lot-streaming with the objective of minimizing makespan and total flow time by Marimuthu et al. [4–6]. Then, Tseng and Liao [7] developed a discrete particle swarm optimization (DPSO). In the DPSO, a net benefit of movement (NBM) algorithm instead of the LP method was utilized to produce the optimal allocation of the sublots for a given sequence. © Springer International Publishing Switzerland 2016 D.-S. Huang et al. (Eds.): ICIC 2016, Part I, LNCS 9771, pp. 517–526, 2016. DOI: 10.1007/978-3-319-42291-6_52
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The no-wait lot-streaming flow shop scheduling problems (nwLFSP) can be found in metallurgy, chemical industry, pharmaceutical industry, and so on. In the scheduling, each sublot must be processed continuously from the first machine to the last machine. There is no interruption in this process and the sublot is no-wait. Obviously, the problem is very difficult to solve. Many researchers studied no-wait lot streaming flowshop scheduling problems. Sriskandarajah and Wagneur [8] considered lot-streaming and scheduling multiple products in two-machine no-wait flowshops. They devised an efficient heuristic for the problem of simultaneous lot streaming and scheduling of multiple products. For the m-machine no-wait flowshop scheduling problems, Kumar, Bag and Sriskandarajah [9] obtained optimal continuous-sized sublots in sin
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