Genetic Algorithm for a Two-Agent Scheduling Problem with Truncated Learning Consideration

Wen-Hsiang Wu (), Yunqiang Yin (), Shuenn-Ren Cheng (), Peng-Hsiang Hsu () and Chin-Chia Wu ()
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Wen-Hsiang Wu: Department of Healthcare Management, Yuanpei University, Hsinchu, Taiwan
Yunqiang Yin: Faculty of Science, Kunming University of Science and Technology, Kunming 650093, China
Shuenn-Ren Cheng: Graduate Institute of Business Administration, Cheng Shiu University, Kaohsiung County, Taiwan
Peng-Hsiang Hsu: Department of Business Administration, Kang-Ning Junior College of Medical Care and Management, Taipei, Taiwan
Chin-Chia Wu: Department of Statistics, Feng Chia University, Taichung, Taiwan

Asia-Pacific Journal of Operational Research (APJOR), 2014, vol. 31, issue 06, 1-23

Abstract: Scheduling with learning effects has received lots of research attention lately. However, the multiple-agent setting with learning consideration is relatively limited. On the other hand, the actual processing time of a job under an uncontrolled learning effect will drop to zero precipitously as the number of the jobs already processed increases. This is rather absurd in reality. Based on these observations, this paper considers a single-machine two-agent scheduling problem in which the actual processing time of a job depends not only on the job's scheduled position, but also on a control parameter. The objective is to minimize the total weighted completion time of jobs from the first agent with the restriction that no tardy job is allowed for the second agent. A branch-and-bound algorithm incorporated with several dominance properties and lower bounds is proposed to derive the optimal solution for the problem. In addition, genetic algorithms (GAs) are also provided to obtain the near-optimal solution. Finally, a computational experiment is conducted to evaluate the performance of the proposed algorithms.

Keywords: Scheduling; genetic algorithm; truncated learning function (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0217595914500468

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