Modeling and multi-objective optimization of closed loop supply chains: A case study

• The optimization of the asset management process in a closed-loop supply chain is investigated.
• A simulation model, based on an adapted EOQ policy, is developed to reproduce the assets flow.
• A multi-objective optimization, including both cost and strategic KPIs, is carried out on the closed-loop supply chain.
• The optimal configuration of the asset management process is identified for each scenario simulated.

This study investigates the issue of optimizing the asset management process in a real closed-loop supply chain (CLSC), consisting of a pallet provider, a manufacturer and 7 retailers. A detailed simulation model, based on an adapted economic order quantity (EOQ) policy is developed under Microsoft Excel™ to reproduce the reorder process of assets by the manufacturer and the corresponding flow of returnable transport items (RTIs) in the CLSC. A multi-objective optimization, including both economic and strategic key performance indicators of the system, is then carried out exploiting the commercial software ModeFRONTIER™. The optimization investigates three scenarios, which refer to as many operating conditions of the manufacturer. Scenario 1 basically reproduces the current operating conditions of the manufacturer, while scenarios 2 and 3 are both hypothetical, and describe situations where the manufacturer would like to minimize the purchase of new assets and the pick-up of assets from its customers, respectively. For each scenario, the optimal configuration (i.e., the setting of the asset management process that performs best in the multi-objective optimization) is identified. Scenarios 1 and 3 are found to generate the most interesting performance of the assets management process, from both the economic and strategic perspectives. Because the present paper is grounded on a real CLSC, the results are expected to be useful to logistics and supply chain managers, to support the evaluation of the performance of CLSCs.