A Simulated Annealing Algorithm for the Design and Planning of Supply Chains with Economic and Environmental Objectives

Nowadays it is becoming increasingly more important to incorporate environmental and social perspectives into problems that in the past were subject to analysis focused only on economic aspects. Specifically, the emerging sustainability concerns on Supply Chain Networks (SCN) optimization contemplate environmental impacts associated with their activities, without ignoring the economic perspective. The combination of these different and frequently antagonistic aspects leads to a complex problem formulation that can be modeled with a bi-objective mixed linear programming. Unfortunately, the nature and dimension of these problems usually imply large formulations that come associated with a high computational burden. In order to overcome this difficulty, a meta-heuristic approach, based on the Simulated Annealing (SA) methodology is developed and a sensitivity analysis is made on the main parameters. The performance of the proposed methodology is compared with an exact approach with the support of a Pareto frontier.