Streamlined Supply Chain Operations: Leveraging Permutation-Based Genetic Algorithms for Production and Distribution

Abstract

Minimizing production and distribution costs by using resources in the most efficient way in supply chain management is among the most fundamental objectives. In increasingly competitive conditions, companies can act more strongly in market share with improvements in cost and efficiency factors. With the proposed Permutation Based Genetic Algorithm (PBGA) approach, the problem of optimizing the production and distribution line in the supply chain is addressed. The algorithm uses the processes of selection, crossover, and mutation to evolve the population in a permuted manner, taking into account multiple iterations, i.e. generation states. The results from the case studies also showed that resource utilization was realized efficiently with cost reductions and improvements in lead times. In this study, cost savings were achieved by applying the PBGA method, especially in information flow and process optimization between distribution and production. This can provide an advantage in a competitive environment.