Research On Customized Production And Transportation Scheduling Of Perishable Goods Based On Learning Effect

With the society advancing and standard of living improving ceaselessly,people who have met their basic needs have a sharp increase in the need for personalized customized products.How to expand production safely and improve consumer satisfaction is the major problem we are facing in the moment.Customized products are time sensitive,that is,the effectiveness and value of goods will gradually decrease over time.These products are collectively referred to as perishable products.Make-To-Order(MTO)is a popular production strategy commonly used by manufacturers customized products.Due to the limited production capacity and high demand with uncertainty over time,the production and operation management of traditional production and processing enterprises has also faced great challenges.For the company’s internal production equipment and production environment,making reasonable adjustment plans is of key significance to effectively respond to market demand and enhance competitiveness.Based on the practice of customized production and transportation scheduling and operation management of perishable products in online environment,this paper deeply studies a class of production and transportation scheduling problems with(truncated)learning effect.It is expected to provide safe and reliable theoretical support and decisionmaking guidance for the production scheduling optimization method of customized products in production and operation management.Due to the negative time sensitivity of perishable products,the purpose is to shorten the customized production cycle and delivery time of perishable products as much as possible.For example,due to the influence of learning effect,apprentices can be added to the production queue,apprentices can get further learning in production,and enterprises can also save resources and improve performance.The outline of the main research contents is as follows:(1)Research on online production scheduling model and online algorithm considering total completion time as optimization objective.Firstly,for the online processing environment with one production equipment and uncertain release time,a customized production scheduling optimization model with the goal of minimizing the sum of total completion time is established.Using the adversary method,the lower bound of the online scheduling problem is 2,and a deterministic online algorithm that can be solved in polynomial time is designed according to the lower bound.Then the analysis technique of "Peeling Onion" is popularized and used to prove that the competition ratio of the problem is 2,which shows that the online algorithm is the best possible.Finally,an example is given to illustrate the implementation of the online algorithm and compared with the offline optimal solution.(2)Scheduling model and online algorithm with the optimization objective of maximum(weighted)completion time is studied.First of all,in consideration of the complexity of the problem,the model with the goal of maximum weighted completion time is further constrained,and its offline optimal production scheduling scheme is discussed.Then an online scheduling model is established to minimize the maximum completion time of orders and the weighted maximum completion time with model constraints.The lower bound of the problem is analyzed and the corresponding online algorithm is designed.The general technique of "Reverse Search Law" is used to analyze the competition ratio of the algorithm,which proves that the online algorithm is the best possible.(3)Considering the time sensitivity of perishable goods,this paper also considers the supply chain scheduling model with distribution time.In view of the characteristics of perishable products,two kinds of transportation time are adopted,which are fixed value and degradation function about completion time.And an online production transportation scheduling model with the maximum delivery time as the optimization objective is established.Solve the lower bound of the problem,design a deterministic online algorithm that can be solved in polynomial time,and analyze the competition ratio of the algorithm by using "Reverse Search Law",which proves that the online algorithm proposed in this chapter is the best possible.(4)Finally,an effective numerical simulation experiment is designed to verify the feasibility and effectiveness of the online algorithm.For the model proposed in this paper,real problems,aiming at the two factors of uncertain order release time and truncated learning effect of production equipment in the process of enterprise production and operation management,extensive data are randomly generated for targeted testing.For small-scale instances,the B&B algorithm is used to solve the optimal scheduling plan.The B&B algorithm is improved by the nature of the problem and the online algorithm proposed in this paper.The improved B&B algorithm can solve up to 50 orders,and the operation speed is faster.For large-scale examples,the lower bound of the optimal solution generated by algorithm analysis is used to verify.These two experiments verify the effectiveness of the model and calculation method.Furthermore,this paper derives several managerial insights through computational experiments.The relevant scheduling models and algorithms studied in this paper are very useful for enriching the theoretical knowledge of production and transportation scheduling with(truncation)learning effect.This provides theoretical basis and method support for enterprise managers to make reasonable production scheduling optimization schemes in the face of multi factor environment.Meanwhile,it can also provide some references for manufacturing enterprises to implement digital telephone manufacturing mode,and has high application reference value for enhancing the effective integration and full utilization of production and manufacturing resources.