Design of Multi-Stage Manufacturing Networks for Personalized Products Using Metaheuristics

Manufacturers are nowadays highly affected by the ever-increasing number of product variants, under the product personalization trend. The large number of cooperating manufacturing network partners leads to enormous search spaces of alternative manufacturing network configurations. This obstructs effective decision-making towards configuring efficient network structures, a nonetheless crucial decision for a company. Exact methods guarantee that the identified solution is the optimum, with regards to the objectives set in the specified problem. However, in real life cases the magnitude of the solution space is such that these methods cannot be utilized due to computational constraints. For tackling such NP-hard problems, meta-heuristics can be utilized that provide a trade-off between the quality of solution and the computation time. This research work describes the modeling and solving of a manufacturing network design problem using the meta-heuristic methods of simulated annealing and tabu search. The quality of the results identified by these methods is compared with the results obtained from an intelligent search algorithm and an exhaustive enumerative method, which are implemented into a web-based platform for the design and planning of manufacturing networks. The approach is validated through its application to a real life case study with data acquired from the automotive industry.