Sustainable multi-period reverse logistics network design and planning under uncertainty utilizing conditional value at risk (CVaR) for recycling construction and demolition waste

Abstract

Decreasing the environmental effect and increasing the social effect as well as the profit of organizations has gained a considerable attention in supply chain network problems. Construction and demolition wastes (C&D wastes) management poses serious challenges to the government and private sector in terms of the environmental damages and potential gains due to recycling these types of wastes. On the other hand, considering the triple bottom line in reverse logistics network design problems simultaneously is an issue that has gained little attention in previous studies. Therefore in this research we propose a multi-period multi-objective mixed integer linear programming to design and plan a network of reverse logistics under uncertainty for recycling C&D wastes in which the objectives are represented as profit and social impact maximization and environmental effect minimization. In this paper the network design problem with stochastic demand of recycled products and rate on investment is regarded. In order to cope with the uncertainty in the model, we consider a risk averse two-stage stochastic programming, where we specify the conditional value at risk (CVaR) as the risk measure. We also apply off-site and on-site separation as two common method for segregating C&D wastes. Appropriate numerical is conducted to learn the effects of crucial parameters on the model and gain managerial insights as well.