Dimensionality reduction applied to the simultaneous optimization of the economic and life cycle environmental performance of supply chains

The design and planning of more sustainable supply chains should take into account several impacts for a proper assessment of the environmental performance of the logistic activities. Unfortunately, minimizing several environmental objectives simultaneously leads to hard optimization problems. This paper presents a rigorous computational framework for solving complex multi-objective optimization (MOO) problems encountered in the optimization of logistic tasks under economic and environmental indicators. The key ingredient of our method is the use of an objective reduction algorithm that allows identifying redundant objectives that can be omitted while still preserving the problem structure to the extent possible. The advantages of our method are illustrated by means of two case studies that address the multi-objective optimization of supply chains that produce bioethanol and hydrogen for vehicle use.