A clustering algorithm for item assignment in a synchronized zone order picking system |
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| Institution: | 1. Department of Information Management, Ling Tung College, 1 Ling Tung Road, Taichung 40816, Taiwan, ROC;2. Department of Business Administration, Ling Tung College, 1 Ling Tung Road, Taichung 40816, Taiwan, ROC;1. Department of Industrial Engineering, Pusan National University, Busan 609-735, Republic of Korea;2. Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX 77843, USA;3. Department of Industrial and Manufacturing Engineering, University of Wisconsin – Milwaukee, Milwaukee, WI 53201, USA;1. Department of Information Management, Fu Jen Catholic University, New Taipei City 24205, Taiwan, ROC;2. Department of Industrial Engineering and Enterprise information, Tunghai University, Taichung 40704, Taiwan, ROC;3. Department of Industrial Management, National Formosa University, No. 64, Wunhua Road, Huwei Township, Yunlin County 632, Taiwan, ROC;1. Institute of Industrial Management, Technische Universität Darmstadt, Hochschulstr. 1, 64289 Darmstadt, Germany;2. Department of Mechanical and Industrial Engineering, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada;1. Department of Logistics Management, Takming University of Science and Technology, 56 Huanshan Road, Section 1, Taipei 11451, Taiwan, ROC;2. Department of Information Management, Vanung University, 1 Van-Nung Road, Chung-Li, Tao-Yuan 32061, Taiwan, ROC |
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| Abstract: | In a synchronized zone order picking system, all the zones process the same order simultaneously. There may be some idle time when the zone pickers wait until all the pickers complete the current order. This paper develops a heuristic algorithm to balance the workload among all pickers so that the utilization of the order picking system is improved and to reduce the time needed for fulfilling each requested order. A similarity measurement, using customer orders, of any two items is first presented for measuring the co-appearance of both items in the same order. With this similarity measurement, a natural cluster model, which is a relaxation of the well-studied NP-hard homogeneous cluster model, is constructed. The heuristic algorithm is then proposed to solve the model for locating all the items into distinct zones. Finally, empirical data and simulation experiments verify that the objectives of the item cluster model are achieved. |
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