A proposed method for third-party reverse logistics partner selection and order allocation in the cellphone industry

There are growing social and government pressures that encourage end of life (EOL) electronic products companies to focus on reverse logistics (RL) and recovery options. The complexity of RL elements for EOL electronic products is a challenge in this field. Recently, original equipment manufacturers (OEMs) have focused on RL activities. To this aim, third-party reverse logistics partners (3PRLPs) play an important role. This paper simultaneously draws attention to the selection of 3PRLPs and determine how orders are assigned to every 3PRLP using a new framework. There are two phases in this framework. The first phase includes an approach that integrates data envelopment analysis (DEA) with a differential evolution (DE) algorithm to increase the discriminatory power and distinguish the 3PRLPs based on their efficiencies. DEA is considered in this study because it is an effective method to determine the efficiency based on the simultaneous analysis of inputs and outputs. In Phase 2, those efficiency scores are utilized to allocate orders to 3PRLPs using a multi-objective model. Two solution approaches are employed for solving the proposed multi-objective model and to find Pareto-optimal solutions. The application of the models is shown in Canada's cellphone industry. The results reveal that the efficiency of 3PRLPs may have an effect on both third-party reverse logistics partner selection and the orders allocated to them.

Automated summary

This research focuses on reverse logistics in the field of EOL electronic products, with a particular emphasis on how to select third-party reverse logistics partners and allocate orders to them. By combining data envelopment analysis (DEA) with a differential evolution (DE) algorithm, the discriminatory power of 3PRLPs' efficiency is enhanced, and a multi-objective model is proposed to allocate orders to these partners.