A global optimization strategy for the environmentally conscious design of chemical supply chains under uncertainty in the damage assessment model

This paper addresses the optimal design and planning of sustainable chemical supply chains (SCs) in the presence of uncertainty in the damage model used to evaluate their environmental performance. The environmental damage is assessed through the Eco-indicator 99, which includes the recent advances made in Life Cycle Assessment (LCA). The overall problem is formulated as a bi-criterion stochastic nonconvex MINLP. The deterministic equivalent of such a model is obtained by reformulating the joint chance constraint employed to calculate the environmental performance of the SC in the space of uncertain parameters. The resulting bi-criterion non-convex MINLP is solved by applying the epsilon constraint method. To guarantee the global optimality of the Pareto solutions found, we propose a novel spatial branch and bound method that exploits the specific structure of the problem. The capabilities of our modeling framework and the performance of the proposed solution strategy are illustrated through a case study.