A Group-Dependent Due Window Assignment Scheduling Problem with Controllable Learning Effect

Ke Chen, Songqiao Han, Hailiang Huang () and Min Ji ()
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Ke Chen: School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, P. R. China2Faculty of Business Information, Shanghai, Business School, Shanghai 200235, P. R. China
Songqiao Han: School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, P. R. China
Hailiang Huang: School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, P. R. China
Min Ji: School of Management and E-Business, Contemporary Business and Trade Research Center, Zhejiang Gongshang University, Hangzhou 310018, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2023, vol. 40, issue 03, 1-25

Abstract: In this paper, we study a single machine scheduling problem with group-dependent due window assignment and further incorporate autonomous and induced learning effects. Here, autonomous learning refers to learning by doing, while induced learning denotes that proactive investments can promote the learning effect, i.e., the learning effect is controllable. The proactive spending could include any management efforts like professional training programs, among others. The objective is to find optimal strategies of due window assignments, sequence of groups and jobs, and level of induced learning that optimize the total cost comprising the due window-related penalty costs and the investment cost. We present a polynomial-time algorithm capable of solving this problem and an improved idea to further reduce the time complexity. In addition, a detailed numerical example is conducted. Our study shows that the learning effect can be tuned to fit the demand of the manufacturing system better and lead to a more flexible operating system.

Keywords: Group scheduling; due window assignment; induced learning effect; position-dependent learning effect (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0217595922500257

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