Abstract
In this paper, a general closed-loop supply chain (CLSC) network is configured which consists of multiple customers, parts, products, suppliers, remanufacturing subcontractors, and refurbishing sites. We propose a three-stage model including evaluation, network configuration, and selection and order allocation. In the first stage, suppliers, remanufacturing subcontractors, and refurbishing sites are evaluated based on a new quality function deployment (QFD) model. The proposed QFD model determines the relationship between customer requirements, part requirements, and process requirements. In addition, the fuzzy sets theory is utilised to overcome the uncertainty in the decision-making process. In the second stage, the closed-loop supply chain network is configured by a stochastic mixed-integer nonlinear programming model. It is supposed that demand is an uncertain parameter. Finally in the third stage, suppliers, remanufacturing subcontractors, and refurbishing sites are selected and order allocation is determined. To this end, a multi-objective mixed-integer linear programming model is presented. An illustrative example is conducted to show the process. The main novel innovation of the proposed model is to consider the CLSC network configuration and selection process simultaneously, under uncertain demand and in an uncertain decision-making environment.
Highlights
The products may be returned by customers after use
Suppliers, remanufacturing subcontractors, and refurbishing sites are evaluated based on the proposed fuzzy quality function deployment (QFD) model
The uncertainty in selection process and demand are taken into account
Summary
The products may be returned by customers after use. Reverse logistics is defined as the activities of the collection and recovery of product returns in supply chain management (SCM). Government directions, and customer pressure are three aspects of reverse logistics (Melo et al, 2009). There are more supply points than demand points in reverse logistics networks when they are compared with forward networks (Snyder, 2006). In the majority of them, the parameters are deterministic (such as Krikke et al, 2003; Kannan et al, 2009; Amin and Zhang, 2012a). It is noticeable that a few of them have taken into account two or more sources of uncertainties (Snyder, 2006; Peidro et al, 2009; Amin and Zhang, 2012b)
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