An efficient approach to cross-fab route planning for wafer manufacturing

Wu, Muh-Cherng; Shih, Chang-Fu; Chen, Chen-Fu

doi:10.1016/j.eswa.2009.02.008

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…The other track focused on the capacity-trading decisions between the two plants. Example studies on the route assignment decisions include Toba et al (2005) and Wu et al (2009aWu et al ( , 2009b. Example studies on the capacity-trading decisions include Wu and Chang (2007), Chen et al (2008), Chiang et al (2010) and Renna and Argoneto (2011).…”

Section: Relevant Literaturementioning

confidence: 99%

“…In the route assignment studies, Wu et al (2009aWu et al ( , 2009b focused on how to plan the production routes of jobs in advance to optimise capacity utilisation. In contrast, Toba et al (2005) addressed the route assignment problem in shop floor control context; that is, how to determine the production route of a job in a real time manner by dynamically considering machine breakdown status.…”

Section: Relevant Literaturementioning

confidence: 99%

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A combined dispatching criteria approach to scheduling dual flow-shops

Chiou

Chen

Wu³

2013

International Journal of Production Research

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This study investigates a dual flow-shops scheduling problem. In the scheduling context, there are two flow shops and each shop involves three processing stages. The two shops are functionally identical but their stage processing times for a job are different. While sequentially going through the three processing stages, each job is allowed to travel between the two shops. That is, for a job, each of its three stages could be processed in any of the two shops. Such a context is called dual flow-shops in the sense that the two flow shops' capacities are completely shared. The scheduling problem involves two decisions: (1) route assignment (i.e. assigning the processing stages of a job to a shop), and (2) job sequencing (i.e. sequencing the jobs waiting before each stage). The scheduling objective is to minimise the coefficient of variation of slack time (CV S ), in which the slack time (also called lateness) denotes the difference between the due date and total completion time of a job. We propose five genetic-algorithm-based (GA-based) solution methods to solve the scheduling problem, which are called GA-EDD, GA-FIFO, GA-SPT, GA-LFO, and GA-COMBO respectively. Numerical experiments indicate that GA-COMBO outperforms the other four methods.

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