The development of technology, the globalization of the economy, and the unpredictable behavior of customers have led to a dynamic and competitive environment in the Complex Product Systems (CoPS) market. Besides, CoPS economic pricing is one of the key factors that significantly reduces production costs of Complex products and systems and increases competitiveness .
In this regard, this paper develops a hybrid data envelopment analysis (DEA) fuzzy mathematical model for economic pricing of CoPS in a competitive closed-loop supply chain network under uncertainty, which leads to productivity and reducing the costs. To achieve the aim of this study, at first, different CoPS providers were evaluated using DEA based on a set of economic, technical, and geographical criteria . The advantage of this evaluation was choosing the right providers, eliminating inappropriate providers, and reducing complexity as one of the fundamental problems in mathematical models. Next, we maximize the benefit of the supply chain using the mathematical model. The objective of the proposed model is to identify strategic and tactical decisions at the same time to provide a fully optimal solution to the model. Furthermore, the presented robust model is capable of providing a stable structure under different uncertainties. This leads to minimizing the purchasing cost of CoPS manufacturers. Eventually, to evaluate the effectiveness and usefulness of the proposed approach, a case study was used to derive important managerial results .
Designing a Closed-loop Supply Chain Network Considering Social Factors; A Case Study on Avocado Industry