Research On Order-oriented Supplier Selection Problem

With the continuous development of intelligent manufacturing technology,the manufacturing industry is undergoing a revolution in production techniques.Some production enterprises are transitioning from the traditional model of mass production with few product varieties to a model of small-batch production with multiple product varieties to meet the increasing demand for customization.This paper focuses on a three-tier supply chain that consists of multiple suppliers and a single manufacturer.The multi-supplier,single-manufacturer configuration is a typical form of supply chain.The study investigates the selection of multiple component suppliers in the supply chain under a production mode of small-batch,low-inventory,and multiple product varieties.A solution method for optimizing the supply chain based on the objective of minimizing the number of supplier selections is proposed.This method significantly reduces the complexity of supply chain management by reducing the number of supplier selections in each production batch during the manufacturing process.To address the problem of multi-supplier selection in the supply chain,the paper analyzes the structure of the supply chain system of a case company.The company belongs to the configuration of a multi-supplier,single-manufacturer supply chain.To solve the problem,the paper first describes the context of the original problem and simplifies the research problem through appropriate problem assumptions,and then establishes a mixed integer linear programming optimization model.In addition,a heuristic algorithm is designed to generate a near-optimal initial feasible solution.This algorithm alternately selects suppliers and orders based on certain priority rules.Through problem analysis,it is demonstrated that the problem belongs to an NP-hard combinatorial optimization problem with a large solution space,and the initial feasible solution obtained may not be the optimal result.To solve the problem,an improved genetic algorithm using natural number encoding is designed.This algorithm first generates an initial population using the neighborhood search mechanism of simulated annealing algorithm and then applies a grasshopper algorithm to perform adaptive large neighborhood search on each generation individual,thus improving the stability and efficiency of the algorithm.This method optimizes the initial feasible solution sufficiently.Furthermore,the paper organizes the recent procurement and production data of the company and,after data analysis and organization,applies the method described in the paper to solve the case.Through the study,a result close to the theoretical optimum is obtained.Compared with the initial feasible solution,the number of supplier selections in the entire production process is reduced by 16.5% through algorithm optimization,approaching the theoretical optimum value for the case.Lastly,the relationship between the number of supplier selections and orders and the final assembly plant is explored through numerical analysis.The application of the set cover problem reduces the number of supplier selections.Through a series of numerical studies on small-scale cases,it is demonstrated that the described method can reduce the number of component suppliers required for each batch of orders in the production process.By reducing the number of suppliers required for each batch of orders,the supply chain management process,as well as the tracking and traceability of defective products,is simplified.The relationship between the number of supplier selections and the various entities in the supply chain is explored using the method of controlling variables.This paper provides decision support for supply chain managers and introduces new methods and ideas for supplier selection.