Solving an Integrated Sales-Leasing Problem With Remanufacturing and Inventory Shortage Using Differential Evolution

Masoud Rabbani, Sina Keyhanian, Mojtaba Aryaee and Esmat Sangari
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Masoud Rabbani: School of Industrial and Systems Engineering, University of Tehran, Tehran, Iran
Sina Keyhanian: Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
Mojtaba Aryaee: École Nationale Supérieure d'Arts et Métiers (ENSAM), Paris Tech University, Paris, France
Esmat Sangari: Department of Industrial Engineering and Management Sciences, McCormick School of Engineering, Northwestern University, Evanston, USA

International Journal of Operations Research and Information Systems (IJORIS), 2018, vol. 9, issue 3, 1-26

Abstract: In this article, an integrated sales and leasing company is considered. This company remanufactures leased products at the end of operating lease contracts to make them as good as new ones and sell them to the customers. In order to satisfy customers' demand, required products are provided from a third-party when the company meets inventory shortage. Non-linear competitive demand functions are used which are sensitive to manufacturer suggested retail price (MSRP) and inflation rate. A mixed integer non-linear mathematical model (MINLP) is developed to determine optimal price of selling products, optimal amount of monthly payments in leasing contracts, and optimal inventory control planning, i.e. the optimal amount of manufacturing and remanufacturing products and optimal inventory levels. The main objective is to maximize net profit of the company. Small, medium and large-scale sizes of the model are solved to show the applicability of the model. To solve the large-scale problem, differential evolution (DE) algorithm is applied as a meta-heuristic solution approach. Numerical results show high sensitivity of model to demands. Also, optimal trend behaviors of some main variables of the problem seem similar to the competitive behavior of demands.

Date: 2018
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