Abstract
In this work, we analyze the effect of demand uncertainty on the multi-objective optimization of chemical supply chain (SC) networks considering simultaneously their economic and environmental performance. We present a novel multi-scenario mixed-integer stochastic linear programming (MILP) model with the unique feature of accounting for the effects of demand uncertainty on the life cycle environmental performance of the network. The uncertain parameter is modeled by a set of scenarios with given probability of occurrence. The environmental performance is quantified following life cycle assessment (LCA) principles, which are explicitly incorporated in the model formulation through standard algebraic equations. The capabilities of the approach presented are illustrated through a case study.
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