Resumen de Quantitative models of location, inventory and transportation decisions for sustainable supply chain management
Pablo Andrés Becerra Muñoz
Sustainable supply chain management has become a topic of great interest among practitioners and researchers in the field of industrial engineering. The impact generated by economic activities on the environment and the communities where they are located has generated an increase in the development of tools that allow the incorporation of these impacts in the decisions taken at the strategic level of companies and industries. This is how new paradigms emerge regarding the production model, such as the Circular Economy, which seeks to move from a linear production economy to a circular one by minimising the generation of waste, both material and energetic. In this context, this PhD thesis, supported by a state-of-the-art study and the analysis of benchmark mathematical optimisation models, presents a conceptual framework to provide the key elements that act as a valuable tool to further develop quantitative models of location, inventory and transport (LIT) problems in sustainable supply chains, and a novel mixed integer non-linear multi-objective mixed integer multi-objective optimisation model (MOMINLP) for designing a closed-loop sustainable supply chain considering location, inventory and transportation decisions, where economic, environmental and social sustainability aspects are incorporated in each of the above mentioned decisions. The proposed model, called 3S-LIT, considers four objective functions that aim to: (1) minimise the total cost of the supply chain, (2) minimise the costs associated with CO2 equivalent emissions, (3) minimise the social cost related to occupational accidents, and (4) maximise the social impact, measured as a higher generation of direct and indirect jobs and a lower number of people affected by transport risks within the chain. The 3S-LIT model has been applied in a company in the copper mining sector, confirming a better performance in the values of the objective functions compared to those obtained in the current situation. In addition, the mathematical optimisation model is replicated in a simulation model with which possible supply chain disruption scenarios were studied to analyse the resilience of the supply chain.
