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Parallel machine selection and job scheduling to minimize machine cost and job tardiness
Affiliation:1. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, Jiangxi 310013, People’s Republic of China;2. School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, Henan 450001, People’s Republic of China;1. Department of Operations Research, Loránd Eötvös University, H1117 Budapest, Pázmány Péter sétány 1/C, Hungary;2. Institute for Computer Science and Control, H1111 Budapest, Kende str. 13–17, Hungary;1. Department of Business Administration and Economics, Bielefeld University, Universitaetsstrasse 25, Bielefeld 33501, Germany;2. College of Business Administration, The University of Alabama in Huntsville, Huntsville, Al 35899, USA;3. 40476 Duesseldorf, Germany;1. AR Sanchez School of Business, Texas A&M International University, Laredo, TX 78045, USA;2. School of Business Administration, The University of Mississippi, University, MS 38677, USA;1. Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China;2. Department of Artificial Intelligence, Zhejiang Cainiao Supply Chain Management Co. Ltd., Hangzhou 311100, China;3. Institute for Data and Decision Analytics, The Chinese University of Hong Kong, Shenzhen 518172, China;4. Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN 55455, United States
Abstract:This paper discusses the problem of simultaneously selecting and scheduling parallel machines to minimize the sum of machine holding cost and job tardiness cost. A combinatorial optimization model is developed for this purpose. Solving the developed model is NP-hard. A heuristic algorithm is developed to locate the optimal or near optimal solutions based on a Tabu search mechanism specially designed to control the search process in the solution neighborhood for jobs scheduled on specific machines. Numerical examples show that the solutions of the model lead to compromises between the system cost related to machine selection and the operational cost related to job tardiness penalties. The examples also show that the developed algorithm is effective and computationally efficient.
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