Integration of logistics outsourcing decisions in a green supply chain design: A stochastic multi-objective multi-period multi-product programming model

This paper develops a programming model, which combines logistics outsourcing decisions with some strategic Supply Chains' planning issues, such as the Security of supplies, the customer Segmentation, and the Extended Producer Responsibility. The purpose is to minimize both the expected logistics cost and the Green House Gas (GHG) emissions of the Supply Chain (SC) network, in the context of business environment uncertainty. First, we define a general structure of the closed-loop SC. Second, we provide constructive models to roughly estimate the insourcing and outsourcing logistics costs, and their corresponding GHG emissions. Third, we establish a stochastic plan based on a scenarios approach to capture the uncertainty od demand, capacity of facilities, quantity and quality of returns of used products, and the transportation, warehousing, and reprocessing costs. Fourth, we suggest a programming model, and an algorithm based on the Epsilon-constraint method to solve it. The result is a set of optimal non-dominant green SC configurations, which provide the decision' makers with optimal levels of logistics outsourcing integration within a decarbonized Supply Chain, before any further low-carbon investment.