Article ID: | iaor20171120 |
Volume: | 29 |
Issue: | 1 |
Start Page Number: | 34 |
End Page Number: | 66 |
Publication Date: | Mar 2017 |
Journal: | International Journal of Operational Research |
Authors: | Shahanaghi Kamran, Yazdian Seyed Ahmad, Naini Seyed Gholamreza Jalali |
Keywords: | management, manufacturing industries, economics, programming: nonlinear, programming: convex, programming: quadratic, heuristics: genetic algorithms |
Remanufacturing is the ultimate form of product recovery with substantial economic and environmental advantages. However, despite growing popularity of remanufacturing, marketing issues such as pricing and warranty as well as accounting for the multi‐component nature of remanufactured products are not adequately addressed in the literature. In this paper, we consider an independent remanufacturer (IR) who acquires multi‐component end‐of‐life (EOL) products in different quality classes, remanufactures them and sells the recovered products bundled with warranty. The remanufacturer is interested in optimal acquisition prices of return products (known as cores) in different quality groups, the remanufacturing plan for each component, the selling price of the final product and the length of the warranty period to maximise its net profits. A mixed‐integer nonlinear program is developed to address the problem where reliability and warranty issues are statistically incorporated into the model and PSO and GA algorithms hybridised with convex quadratic programming (CQP) are proposed for its solution.