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In recent years, consumers and legislation have been pushing companies to optimize their activities in such a way as to reduce negative environmental and social impacts more and more. In the other side, companies must keep their total supply chain costs as low as possible to remain competitive. This work aims to develop a model to traveling salesman problem including environmental impacts and to identify, as far as possible, the contribution of genetic operator’s tuning and setting in the success and efficiency of genetic algorithms for solving this problem with consideration of CO2 emission due to transport. This efficiency is calculated in terms of CPU time consumption and convergence of the solution. The best transportation policy is determined by finding a balance between financial and environmental criteria. Empirically, we have demonstrated that the performance of the genetic algorithm undergo relevant improvements during some combinations of parameters and operators which we present in our results part.

Multi-Objective Optimization using Genetic Algorithms in MOTSP (Co2 Emissions)