Economic pricing of complex products in a competitive closed-loop supply chain network under uncertainty: A case study of CoPS industry

Abstract

The development of technology, globalization of the economy and the unpredictable behavior of customers have eventuated in a dynamic and competitive environment in the complex product systems (CoPS) market. Besides, CoPS economic pricing is one of the key factors that dramatically reduces production costs and increases competitiveness. In this regard, this paper unveils a hybrid data envelopment analysis (DEA)-fuzzy mathematical model for economic pricing of CoPS in a competitive closed-loop supply chain network under uncertainty. In the first stage, different CoPS suppliers are evaluated exploiting a DEA model based on a set of economic, technical, and geographical criteria. The advantage of this evaluation is choosing appropriate suppliers, and reducing the complexity of the original model. Next, using a robust optimization model, the strategic and tactical decisions are simultaneously determined, providing a fully optimal solution to the model. In the concerned model, the costs and capacities of facilities are considered to be hemmed in by uncertainty. Eventually, to evaluate the proposed approach, a case study is conducted to derive the important managerial results. The numerical results corroborate that the presented robust model is capable of providing a stable structure under different realizations.