A hybrid algorithm for supply chain optimization of assembly companies

A fundamental goal of any system is to get an optimal state. These optimal states can be found in different areas, such as medicine, engineering, or architecture. In the field of industrial engineering, one of its objectives is improving or optimizing company processes in order to increase benefits while reducing costs. In this context, an essential component is the supply chain, which is a network in that different entities, such as manufacturers, suppliers, distributors, retailers, transporters, and customers or end-users, are associated. Several optimization algorithms with different approaches have been developed to optimize the supply chain. Nevertheless, they still have problems to fulfill some requirements at once. This research aims to develop a hybrid optimization algorithm that leverages the capabilities of different approaches. This algorithm, which presents a multi-objective optimization schema, meets a tradeoff between the optimization results quality and the runtime. To this end, a manufacturing and assembly company is used as case study to prove the algorithm. The results are also compared with other state-of-the-art algorithms using the same execution environment and general settings. Findings indicate that the hybrid algorithm converges in less time and in most cases, it could reach the global optimal.