Optimization mixed procurement model for MRP and JIT with hybrid particle Swarm algorithm and genetic algorithm

This paper examines the use of hybrid metaheuristic algorithms to optimize order quantity in a single manufacturer-multi-supplier two-level JIT Supply Chain (SC) in the production system. Over the years, production systems have largely been controlled by either Material Requirement Planning (MRP), Just in Time (JIT), or Optimized Production Technology (OPT) paradigm. In the SC environment, traditional material demand planning does not consider the supplier's supply capacity and economic benefits, which is not conducive to the long-term cooperation of upstream and downstream enterprises in the SC. The main goal of this paper is to optimize ordering batches based on MRP and JIT in the SC. There is limited research in designing and optimizing the SC/procurement network. This study is among the first to integrate supplier selection to optimize performance indicators in SC network design, considering the minimization of the total cost of the JIT SC order batch coordination adjustment model. The Bill of Materials (BOM) constraints and MRP formulation principles of product production are followed to minimize the total cost of downstream companies' inventory, transportation, out-of-stock, and SC crashes. The MRP-led SC ordering batch collaborative optimization model is constructed; the manufacturer's main production plan is adjusted to change the procurement plan to obtain supplier supplies according to the scheme, an improved discrete Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) is designed to solve the model; an example verifies the feasibility of the model. The algorithm's effectiveness is proved by analyzing and comparing the algorithm results.

Keywords:

Material requirement planning , Just-in-time manufacturing , Particle Swarm Optimization , Genetic Algorithm