Optimization of variable demand fuzzy economic order quantity inventory models without and with backordering |
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| Affiliation: | 1. Center for Product Design and Manufacturing (CPDM), Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Wilayah Persekutuan, Malaysia;2. School of Engineering and Sciences, Tecnológico de Monterrey, E. Garza Sada 2501 Sur, C.P. 64849, Monterrey, N.L., Mexico;1. School of Industrial Engineering, College of Engineering, University of Tehran, Iran;2. School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran;3. Center for Sustainable Supply Chain Engineering, Department of Technology and Innovation, University of Southern Denmark, Odense M, 5230, Denmark |
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| Abstract: | A variable demand inventory model was developed for minimizing inventory cost, treating the holding and ordering costs and demand as independent fuzzy variables. Thereafter, backordering cost was also considered as an independent fuzzy variable. Fuzzy expected value model and fuzzy dependent chance programming model were constructed to find the optimal economic order quantity, which would minimize the fuzzy expected value of the total cost, so that the credibility of the total cost not exceeding a certain budget level was maximized. Optimization was carried out using genetic algorithms and particle swarm optimization algorithm, and their performances were compared. The developed model was found to be efficient not only in one artificial case study but also in two data sets collected from the industries. Therefore, this model could solve real-world problems, too. |
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| Keywords: | Inventory model Variable demand Fuzzy economic order quantity Optimization Genetic algorithm Particle swarm optimization |
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