Traceability optimization in the meat supply chain with economic and environmental considerations

Abstract

This work aims to develop a decision support tool for optimizing the traceability in a supply chain (SC) of meat products considering economic and environmental concerns. The problem is formulated as a multi-objective mixed-integer linear programming (MOMILP) model that seeks to minimize three objectives simultaneously: total batch dispersion, operating costs, and carbon emissions of the network. This model is solved by applying a Goal Programming approach that minimizes the undesirable deviations from the goals. The advantages of the posed formulation are illustrated through its application to a case study in which trade-offs exist among the performance indicators of the meat SC. Results show the formulation constitutes a valuable instrument for selecting the involved actors in the SC, planning the production and distribution of produced batches, and identifying the batches of animals that form each finished product batch delivered to every retailer, thus allowing their traceability.