Considering stockers in reentrant hybrid flow shop scheduling with limited buffer capacity. (January 2020)

• Record Type: Journal Article
• Authors: Lin, Chun-Cheng; Liu, Wan-Yu; Chen, Yu-Hsiang
• Is Part Of: Computers & industrial engineering. Volume 139(2020)
• Publication Date: 2020-01
• Subjects: Flow shop scheduling -- Stocker -- Reentrant -- Harmony search algorithm -- Genetic algorithm; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.106154
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Automated material handling system has stockers, serving as a centralized buffer. First work for reentrant hybrid flow shop schedule with limited buffers & stockers. NP-hard to minimize makespan & mean flowtime, under limited buffer size & stockers. Propose hybrid harmony search & genetic algorithm; stockers cause complex decoding. Simulation shows outperformance of the setting with limited buffer size & stockers. Abstract: Diversification of products has increased the involvement of reentrant manufacturing processes, in which a job returns multiple times to a machine at the preceding workflow stage to continue the manufacturing process. Reentrant flow shop manufacturing can substantially improve manufacturing efficiency when scheduled properly. In practice, advanced manufacturing companies (e.g., semiconductor foundries) have introduced automated material handling system (AMHS), including stockers that serve as centralized inventory buffer space for temporarily storing the inventories owing to limited buffer capacity of each machine. However, no previous studies on reentrant flow shop scheduling have considered the impact of limited buffer capacity or stockers on scheduling efficiency. Consequently, this study investigated the application of stockers in solving the reentrant hybrid flow shop scheduling problem with limited buffer capacity. With the objective of optimizing the makespan and mean flowtime of a schedule, this problem is NP-hard because it generalizes the flow shop problem. Therefore, this study developed a hybrid harmony search and genetic algorithm (HHSGA) for the problem, in which limited buffer capacity and stockers cause solution decoding to be non-trivial. Experimental comparison on scheduling problems with different numbers of jobs showed that the HHSGA performed better than conventional algorithms. Moreover, among three manufacturing conditions (i.e., with buffers and stockers, with buffers only, and with stockers only), the results indicated that the condition using inventory buffers and stockers was more beneficial.