Minimizing environmental impacts for hub and spoke distribution network problems through the use of multiobjective evolutionary algorithms

While transportation is crucial to our economy and our personal lives, as a sector it is also a significant source of greenhouse gas (GHG) emissions. The AASHTO report states, “America’s transportation system has served us well, but now faces the challenges of congestion, energy supply, environmental impacts, climate change, and threatens the economic, social, and environmental future of the nation”. With respect to the environment, transportation is the most visible aspect of supply chains. Transportation emissions can be reduced by implementing approaches that contribute to GHG emissions reduction, for example, the use of low-carbon fuels, new and improved vehicle technologies, improved transportation system efficiency, strategies to reduce the number of vehicle miles traveled and operating vehicles more efficiently. This work is motivated by the urgent need of advancing knowledge and understanding of transportation’s current global challenges by contributing to the transition of the current transportation sector into a more sustainable one by implementing a more holistic analysis considering the three approaches stated by the U.S. Department of Transportation, which are, the use of low-carbon fuels, strategies to reduce the number of vehicle miles traveled, and optimizing the design of transportation networks to reduce trip frequencies. This work provides a design alternative for the centralized carrier collaboration problem and to the biomass-to-biofuels supply chain by simultaneously addressing economic and environmental impacts. A hybrid hub-and-spoke system is used as a set for collaborative consolidation transshipments hubs increasing the efficiency of the operations. The present research proposes the development of Single and Multi-Objective Evolutionary Algorithms to solve the Centralized Carrier Collaboration Multi-hub Location Problem evaluating transportation cost and global warming potential separately and advancing it further into a simultaneous optimization. The second scenario focuses in the design of the supply chain for densified biomass by developing a Multi-Objective Evolutionary Algorithm to determine the Pareto optimal solutions for the operations configuration of a hub and spoke biofuels logistics network. Providing Pareto optimal yearly configurations for the system’s planning operations as well as their corresponding optimal transportation design. In contrast to previous research, this work not only takes into consideration the GHG emissions, but evaluates the global warming impact category; likewise, it aggregates two other objectives by considering as well acidification and eutrophication potentials creating a more extensive and robust analysis of environmental sustainability of a hub and spoke network. Furthermore, data analytics methodologies were implemented to examine raw data on coordinates searching for optimal locations for facilities in the biofuel supply chain. The approaches used are the K-means and the Silhouette value analyses to select the optimal number of clusters for our data. In addition, a Life Cycle Assessment was conducted to evaluate the environmental potential impacts associated with the problems analyzed providing an adequate instrument for environmental decision support