Designing a multi-objective closed-loop supply chain for multi-period multi-generational products with social impacts considerations - Keshavarz-Ghorbani, Fatemeh; Hamid Reza Pasandideh, Seyed (Computers & industrial engineering, 2023)
[Display omitted] •Joint optimization of production-sale strategy and CLSC network configuration.•Evidence of computation efficiency of the OA to solve large-sized problems.•Stimulating diffusion of a new product by enhancing technology innovation level.•An inverse association between price drop offer and total profit.•Social responsibility optimization against its negative economic perception. This paper assesses a multi-period closed-loop supply chain (CLSC) for making strategic and operational decisions in multi-generation applications. For this purpose, joint decisions on network design, inventory control, production planning, pricing, supplier selection, and service level requirements are determined along with the forward flows of the supply chain. Besides, a segmentation policy is proposed for disassembling and recovering the different quality grades of returned products. All the raised issues have become more challengeable when an inner competition on coexistence goes up between the incumbent and new generations. Accordingly, this paper attempts to bridge the gaps in the literature by proposing a bi-objective mathematical model that involves a demand function with the integration of price drop effects, marketing efforts, and technology desirability to tackle trade-offs decisions between multiple generations. This research aims to simultaneously optimize the total profit and social benefits. To solve the problem, first, the ε-constraint method has been applied to transform the bi-objective problem into a single objective. Then, as a great deal to solve the complex mixed-integer non-linear program model, an outer approximation algorithm is applied based on approximation, relaxation, and decomposition. Finally, a case example in a mobile phone CLSC problem has been investigated to validate the proposed model and provide managerial insights.