Research On Multi-Objective Optimization Of Integrated Production And Transportation Scheduling For Make-to-Order Manufacturing Enterprises

The 14 th Five-Year Plan states that we should encourage the deep interconnection and cooperation of the supply chain,promote the deep integration of manufacturing and service industries,and provide strong support for the establishment of a strong manufacturing country and high-quality economic development.Production and transportation,two essential supply chain elements,can be optimized together to boost the supply chain’s competitiveness and better respond to the shifting demands of customers and the increasingly competitive market.Multi-objective optimization is a challenge when collaborative optimization is used because there are frequently many competing goals.The results of multi-objective optimization will be more realistic and more in line with the true circumstances of the enterprise operation.The following are the main conclusions of this thesis,which examines the multi-objective optimization of integrated production and transportation for a make-to-order manufacturing business.(1)First,this paper examines the MTO model’s components for the integrated production and transport scheduling issue.In the production phase,the supply chain perspective includes the major production units and distribution entities,while the analysis from the perspective of the production enterprise includes three aspects,namely input elements,intermediate elements,and output elements.The transportation main body,transportation tools,operation mode,transportation facilities,and transportation network are the primary problem components in the transportation stage.The following section of the essay describes the traits of MTO businesses,including their extremely low inventory levels,dependence on customer orders for production,emphasis on customer satisfaction,and flexibility in production.The paper also identifies the crucial elements in designing a scheduling solution,including the first necessity of creating a scientific scheduling model,the second necessity of choosing and developing suitable optimization techniques,and the third necessity of testing the developed optimization algorithms.(2)For the integrated production and transportation scheduling multi-objective optimization problem for make-to-order manufacturing enterprises,where the first objective is to minimize the transportation cost and the second objective is to minimize the overall order delivery time,a mathematical model is created.In order to optimize both the operating cost and the level of customer service provided by the enterprise through the efficient synergy of production and transportation links,the production order of orders must be ranked in the production phase and the path must be optimized in the transportation phase.The nature of the problem’s ideal answer is then determined,and a method for carrying out batch loading of orders is then suggested.(3)The proven multi-objective optimization model is solved by a non-dominated sorting genetic algorithm with an elite strategy.The integrated scheduling of production and transportation for make-to-order manufacturing enterprises is a multi-objective optimization problem.This paper introduces the multi-objective optimization theory associated with the algorithm and describes in detail the basic process,key steps,and how to apply the algorithm.Also displayed is the algorithm’s complexity analysis.Additionally presented and contrasted with the NSGA-II algorithm is a multi-objective optimization algorithm based on decomposition.(4)To perform experiments,company A of Linyi City,Shandong Province,is used as an example in this paper.The fundamental situation of Company A is presented first,followed by an analysis of the company’s current production and distribution situation.Finally,experiments are carried out using MATLAB simulation software.Each group of solutions on the pareto frontier can yield both the production order of orders and the transportation of vehicles.The experiments can yield the problem’s pareto frontier.By contrasting the outputs of the NSGA-II algorithm solution with those of the original solution and the MOEA/D algorithm solution,the superiority and applicability of the non-dominated sorting genetic algorithm with the elite strategy to this issue are demonstrated.