Um modelo multiobjetivo para otimização da cadeia de suprimentos sustentável aplicado à indústria siderúrgica brasileira

Abstract

Recently, companies have been pressured to consider not only the financial aspects of their activities, but also their impacts on the ecosystem and on society. In that sense, interest in the sustainable supply chain management (SSCM) has increased, as it takes into account economic, environmental and social impacts when analyzing production, transportation and facility location decisions. This work proposes a multi-objective mixed integer linear programming model for the optimization of a sustainable steel supply chain network design problem, addressing the three pillars of sustainability. The economic bottom line is measured in terms of total network costs, while the environmental bottom line takes into account CO2 emissions due to production and transportation. The social aspect considers the cities’ social vulnerability indexes. To solve the problem, two methods have been compared, a multi-objective genetic algorithm and the epsilon-constrained method. Results show that, although the metaheuristic was not able to find the optimum for some of the objective functions, it offered a greater option of viable solutions at the Pareto frontier in less computational time, when compared to the exact method. The environmental performance of the network can be improved by prioritizing the use of railroads as the main method of transportation, as well as by the establishment of production plants with the planted charcoal route and the electric arc furnace route. Social performance can be improved by establishing facilities in cities with a higher social vulnerability index, although that will result in higher environmental and economic costs if these cities do not have the necessary infrastructure.