A Branch-and-Bound Algorithm for Two-Agent Scheduling with Learning Effect and Late Work Criterion

Shang-Chia Liu (), Jiahui Duan (), Win-Chin Lin (), Wen-Hsiang Wu (), Jan-Yee Kung (), Hau Chen () and Chin-Chia Wu
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Shang-Chia Liu: Department of Business Administration, Fu Jen Catholic University, New Taipei City, 24205, Taiwan
Jiahui Duan: Business School, Sichuan University, Chengdu 610064, P. R. China
Win-Chin Lin: Department of Statistics, Feng-Chia University, Taichung 40724, Taiwan
Wen-Hsiang Wu: Department of Healthcare Management, Yuanpei University of Medical Technology, Hsinchu 30015, Taiwan
Jan-Yee Kung: Department of Business Administration, Cheng Shiu University, Kaohsiung 83347, Taiwan
Hau Chen: Department of Statistics, Feng-Chia University, Taichung 40724, Taiwan
Chin-Chia Wu: Department of Statistics, Feng-Chia University, Taichung 40724, Taiwan

Asia-Pacific Journal of Operational Research (APJOR), 2018, vol. 35, issue 05, 1-24

Abstract: This paper studies a two-agent single-machine scheduling problem with sum-of-processing-times-based learning consideration. The goal is to find an optimal schedule to minimize the total late work of the first agent subject to the restriction that the maximum lateness of the second agent has an upper bound. For this problem, a branch-and-bound algorithm along with several dominances and a lower bound is developed to find the optimal solution, and a tabu algorithm with several improvements is proposed to find the near-optimal solution. Computational experiments are provided to further measure the performance of the proposed algorithms.

Keywords: Scheduling; sum-of-processing-times-based learning; two-agent; tabu algorithm (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0217595918500379

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