A simulation-based decision support system for a multi-echelon inventory problem with service level constraints |
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| Affiliation: | 1. School of Mechanical Engineering, The University of Adelaide, South Australia 5005, Australia;2. School of Mechanical and Manufacturing Engineering, University of New South Wales, New South Wales 2052, Australia;1. Department of Business, School of Business and Leadership, Our Lady of the Lake University, Houston, USA;2. Center for Sustainable Supply Chain Engineering, Department of Technology and Innovation, University of Southern Denmark, Campusvej 55, Odense, Denmark;3. Department of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran;1. School of Mathematical Sciences, University of Adelaide, SA 5005, Australia;2. CH2MHILL, Burderop Park, Swindon, Wiltshire SN4 0QD, United Kingdom;1. Department of Industrial Engineering, Kocaeli University, 41380 Kocaeli, Turkey;2. Department of Computer Engineering, Kocaeli University, 41380 Kocaeli, Turkey;3. Faculty of Engineering and Natural Sciences, Sabancı University, 34956 Istanbul, Turkey;1. School of Management, University of Science & Technology of China, Hefei, Anhui 230026, PR China;2. School of Management, Zhejiang University, Hangzhou, Zhejiang 310058, PR China;3. SHU-UTS SILC Business School, Shanghai University, Shanghai 201800, PR China |
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| Abstract: | In this paper, we present a simulation-based decision support system for solving the multi-echelon constrained inventory problem. The goal is to determine the optimal setting of stocking levels to minimize the total inventory investment costs while satisfying the expected response time targets for each field depot. We derive new decision support algorithms to be applied in different scenarios, including small-sample and large-sample cases. The first case requires that the set of alternative solutions is known at the beginning of the experiment, and the number of evaluated solutions may depend on the simulation budget (i.e., the time available to solve the problem). In the second case, the alternative solutions are generated sequentially during the searching process, and we may terminate the algorithm when the specified sampling budget is exhausted. Empirical studies are conducted to compare the performance of the proposed algorithms with other conventional optimization approaches. |
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| Keywords: | Decision support system Multi-echelon inventory problem Service level Computer simulation |
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