A simulation-based multi-objective optimization framework: A case study on inventory management |
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| Affiliation: | 1. College of Business and Public Administration, University of North Dakota, Grand Forks, ND 58202, United States;2. College of Business Administration, Georgia Southern University, Statesboro, GA 30458, United States;1. W.P. Carey School of Business, Arizona State University, Tempe, USA;2. Indian Institute of Management Indore, India;3. Independent Consultant, India;1. Montpellier Business School/Montpellier Research in Management, France;2. Laboratoire Génie Industriel, Ecole Centrale Paris, France;3. TUM School of Management, Technische Universität München, Germany;1. Department of Industrial Engineering, PUC-RIO, Rua Marquês de São Vicente, 225 - Gávea, Rio de Janeiro – RJ, 22451-900, Brazil;2. CITTA, Department of Civil Engineering, University of Coimbra, Portugal;1. School of Industrial Engineering, LIUC-Cattaneo University, Italy;2. Past: Operations Research Center, Massachusetts Institute of Technology (MA). Present: Boston Consulting Group, UK |
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| Abstract: | We propose a simulation-based solution framework for tackling the multi-objective inventory optimization problem. The goal is to find appropriate settings of reorder point and order quantity to minimize three objective functions simultaneously, which are the expected values of the total inventory cost, the average inventory level, and the frequency of inventory shortage. We develop new algorithms that can exploit statistically valid ranking and selection (R&S) procedures and the desirable mechanics of conventional multi-objective optimization techniques. Two simulation algorithms are proposed to be applied in different scenarios depending on the preference information that is revealed either during or after the actual optimization process. Experimental results are provided to evaluate the efficiency of the developed algorithms and other existing solution frameworks. |
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| Keywords: | Multi-objective inventory problem Feasibility check Ranking and selection Optimization Simulation |
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