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On the Use of Genetic Algorithm for Solving Re-entrant Flowshop Scheduling with Sum-of-processing-times-based Learning Effect to Minimize Total Tardiness

Win-Chin Lina, Chin-Chia Wua, Kejian Yub, Yong-Han Zhuanga, Shang-Chia Liuc

a Department of Statistics, Feng Chia University, Taichung City, Taiwan;
b Management School, Zhejiang Shuren University, Zhejiang, China;
c Business Administration Department, Fu Jen Catholic University, New Taipei City, Taiwan

* Corresponding Author: Shang-Chia Liu,

Intelligent Automation & Soft Computing 2018, 24(4), 671-681.


Most research studies on scheduling problems assume that a job visits certain machines only one time. However, this assumption is invalid in some real-life situations. For example, a job may be processed by the same machine more than once in semiconductor wafer manufacturing or in a printed circuit board manufacturing machine. Such a setting is known as the “re-entrant flowshop”. On the other hand, the importance of learning effect present in many practical situations such as machine shop, in different branches of industry and for a variety of corporate activities, in shortening life cycles, and in an increasing diversity of products in the manufacturing environment. Inspired by these observations, this paper addresses a re-entrant m-machine flowshop scheduling problems with time-dependent learning effect to minimize the total tardiness. The complexity of the proposed problem is very difficult. Therefore, in this paper we first present four heuristic algorithms, which are modified from existing algorithms to solve the problem. Then, we use the solutions as four initials to a genetic algorithm. Finally, we report experimental performances of all the proposed methods for the small and big numbers of jobs, respectively


Cite This Article

. Win-Chin Lin, . Chin-Chia Wu, . Kejian Yu, . Yong-Han Zhuang and . Shang-Chia Liu, "On the use of genetic algorithm for solving re-entrant flowshop scheduling with sum-of-processing-times-based learning effect to minimize total tardiness," Intelligent Automation & Soft Computing, vol. 24, no.4, pp. 671–681, 2018.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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