Prefabricated Parts Production Scheduling With Uncertain Processing Times In Civil Engineering Using Neutrosophic Number Based Optimization Method

The production scheduling of prefabricate parts will affect the production efficiency of the entire factory and the construction progress of the entire project;the production process of a prefabricated part contains several procedures or operations;each procedure needs a certain processing time in a specific workshop or equipment to be completed.Due to many uncertain factors in the actual production environment,the processing time of an operation is usually notified.This article mainly discusses the production scheduling optimization problem of prefabricate part production with uncertain processing time in civil engineering.The first chapter of this article introduces the background of the problem and expounds the problem itself.Afterwards,the scope and objectives of the research are introduced.The second chapter mainly reviews the related literature of scheduling optimization problems;it mainly includes the development of scheduling methods,the related literature of scheduling problems with uncertain factors,and the methods of handling constraints in scheduling problems.Then the related literature on flow shop scheduling and meta-heuristic optimization methods is reviewed.Chapter3 introduces the modeling and optimization methods of the problem in detail.In this research,the production scheduling problem of prefabricate part with uncertain processing time is simplified into a flow shop problem;neutrosophic numbers are introduced and applied in the mathematical model for the first time,and a neutrosophic number based mixed integer linear programming(MILP)model is established.Due to the difficulty in solving the problem as well as the complexity of the problem,it is impossible to obtain the optimal solution by commercial software with affordable computational time,the genetic algorithm is adopted in this article.Therefore,this chapter also elaborates on the encoding and decoding of the algorithm,the corresponding genetic operators and the workflow of the algorithm.The computational results with corresponding analysis or discussion are presented in the fourth chapter.Results show that the ideal solution can be obtained in a short time by using the proposed algorithm.Chapter5 summarizes the full research.