Fuzzy Economic Order Quantity Inventory Models Without Backordering |
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| Authors: | WANG Xiaobin TANG Wansheng ZHAO Ruiqing |
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| Affiliation: | 1. Institute of Systems Engineering, Tianjin University, Tianjin 300072, China;;2. School of Computer and Information Engineering, Shandong University of Finance, Ji’nan 250014, China;1. Welding and Joining Research Center, School of Industrial Engineering, Iran University of Science and Technology (IUST), Narmak 16846-13114, Tehran, Iran;2. Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU), Richard Birkelands vei 2b, 7491 Trondheim, Norway;1. Guangdong Provincial Engineering Research Center for Modernization of TCM, College of Pharmacy, Jinan University, Guangzhou 510632, PR China;2. State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao 999078, PR China;1. SIMUMECAMAT Research Group, University of Oviedo, Campus universitario, 33203 Gijón, Spain;2. University of Oviedo, Dept. of Construction and Manufacturing Engineering, Campus Universitario, 33203 Gijón, Spain |
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| Abstract: | In economic order quantity models without backordering, both the stock cost of each unit quantity and the order cost of each cycle are characterized as independent fuzzy variables rather than fuzzy numbers as in previous studies. Based on an expected value criterion or a credibility criterion, a fuzzy expected value model and a fuzzy dependent chance programming (DCP) model are constructed. The purpose of the fuzzy expected value model is to find the optimal order quantity such that the fuzzy expected value of the total cost is minimal. The fuzzy DCP model is used to find the optimal order quantity for maximizing the credibility of an event such that the total cost in the planning periods does not exceed a certain budget level. Fuzzy simulations are designed to calculate the expected value of the fuzzy objective function and the credibility of each fuzzy event. A particle swarm optimization (PSO) algorithm based on a fuzzy simulation is designed, by integrating the fuzzy simulation and the PSO algorithm. Finally, a numerical example is given to illustrate the feasibility and validity of the proposed algorithm. |
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| Keywords: | inventory fuzzy variable dependent chance programming fuzzy simulation particle swarm optimization |
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