Seyed Mohammad javad mirzapoor al-hashem - Amirkabir University Of Technology, Tehran, Iran
Mahtab Asoodeh - Amirkabir University Of Technology, Tehran, Iran
Seyed mohammad moatar hosseini - Amirkabir University Of Technology, Tehran, Iran

The design of closed-loop supply chain networks has attracted more attention in recent decades as a result of environmental concerns and commercial factors. Due to the rapid growth of knowledge and technology, the complexity of the supply chain operations is increasing daily and organizations will be faced with numerous challenges and risks in its management. Most organizations outsource their logistics services with limited resources, capabilities and knowledge, and with the goal of cost savings and increased customer satisfaction. The increasing trend of focusing on structures to reduce inventories or distribution centers that can save a large geographic area hascreated proper opportunities for outsourcing logistics services. The Third Party Logistics Services Providers (3PLs) have been set up to outsource various supply chain activities to specialized companies. This paper proposes a biobjective possibilistic mixed integer nonlinear programming model for designing a closed-loop supply chain network from the perspective of third party logistics service providers. To solve the proposed multi-objective model, a twostage solving approach was applied to convert the possibilistic model into its equivalent crisp counterpart in the first stage, and then transfer the crisp multi-objective model into a single-objective model in the second stage. Using thisapproach, a single-objective equivalent auxiliary crisp model is obtained and solved optimally by IBM ILOG CPLEX software. Solving numerical examples has proved the effectiveness of the proposed bi-objective and possibilistic framework. Several sensitivity analyses are performed to drive managerial insights

Network design, Closed-loop supply chain; Third-party logistics providers (3PLs); Possibilistic programming; Fuzzy multi-objective optimization