Browsing by Author "Chen, Wen-Min"

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A combined dispatching criteria approach to scheduling dual flow-shops
(2013-02-01)
Chiou, Chie-Wun
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Chen, Wen-Min
;
Wu, Muh-Cherng
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
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 (CVS ), 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.
A genetic algorithm for scheduling dual flow shops
(2012-01-01)
Chiou, Chie-Wun
;
Chen, Wen-Min
;
Liu, Chin-Min
;
Wu, Muh-Cherng
;
工業工程與管理學系
This study examines a dual-flow shop-scheduling problem that allows cross-shop processing. The scheduling objective is to minimize the coefficient of variation of slack time (lateness), where the slack time (ST) of a job denotes the difference between its due date and total completion time. This scheduling problem involves two decisions: job route assignment (assigning jobs to shops) and job sequencing. This study develops a genetic algorithm (GA) embedded with the earliest due date (EDD) dispatching rule for making these decisions. Numerical experiments with the GA algorithm indicate that the performance of adopting a cross-shop production policy may significantly outperform that of adopting a single-shop production policy. This is particularly true when the two flow shops are asymmetrically designed. This study develops a grouping heuristic algorithm to reduce setup time and due-date-based demand simultaneously. This study uses the proposed genetic algorithm (GA) to prove that the grouping heuristic algorithm performs well. Obtaining an approximate optimal solution makes it possible to decide the route assignment of jobs and the job sequencing of machines. (C) 2011 Elsevier Ltd. All rights reserved.