A stochastic multi-objective model for a closed-loop supply chain with environmental considerations

In today's world, environmental impacts usually motivate the governments and international comities to control special properties of the designing supply chain networks at the early steps. Closed-loop Supply Chain (CLSC) is one way to consider the recycling and the remanufacturing of used products to control environmental considerations more efficiently. Besides, uncertainty management is one of significant tools for controlling and predicting the CLSC's behavior for managers. The literature shows that using the stochastic models to design a CLSC is still scare and needed. Hence, this paper is among the first studies to develop a two-stage stochastic multi-objective model for a CLSC by considering the environmental aspects and downside risk, simultaneously. To solve the model, a number of memetic metaheuristics has been considered. Furthermore, ε-constraint method is used to validate the metaheuristic results in small sizes. The parameters of the proposed algorithms are tuned by Response Surface Method (RSM) via an MODM approach. A comparative study confirms the efficiency and effectiveness of Virus Colony Search (VCS) as a recent nature-inspired algorithm in comparison with other well-known and recent metaheuristics. Results show the importance of controlling the uncertainty to improve the environmental and economical aspects of CLSC through an industrial example.