Research On Remanufacturing Production Scheduling Optimization With Uncertain Factors

With the advances of industrialization,manufacturing has brought huge benefits to human society while consuming a large number of non-renewable resources on earth and causing problems such as ecological degradation and environmental pollution.In this context,remanufacturing,as a resource-saving and environment-friendly manufacturing paradigm,has gradually attracted the attention of academia and industry.Remanufacturing is the process of restoring end-of-life products to a new condition through a series of operations such as disassembly,reprocessing and reassembly.Production scheduling plays an important role in the organization of rational remanufacturing production activities.Scientific and rational remanufacturing production scheduling not only improves the economic efficiency of remanufacturing enterprises,but also contributes to the sustainable development of human society.However,different from traditional manufacturing,remanufacturing employs end-of-life products as work blank.These products differ in terms of condition,form and degree of damage,and this variation in quality makes remanufacturing inherently uncertain and makes remanufacturing production scheduling more difficult.How to effectively deal with the uncertainties in remanufacturing production and to obtain a scientific and reasonable remanufacturing scheduling solution to achieve maximum benefits is a challenge that academia and industry have to face.In recent years,the issue of remanufacturing production scheduling has attracted increasing attention from scholars.However,most of the existing studies have focused on a deterministic remanufacturing environment,ignoring the inherent uncertainties in remanufacturing brought by end-of-life products,and the remanufacturing scheduling solutions obtained rarely accurately represent the actual remanufacturing production situation.Although some scholars have studied the uncertainties in remanufacturing production,these studies have not accurately expressed the complexity of uncertainties and are unable to meet the requirements of actual remanufacturing production scheduling.Therefore,this study addresses the problems of remanufacturing production scheduling and the complex uncertainties that exist in the actual remanufacturing production process,establishing an optimization model for remanufacturing production scheduling through a reasonable optimization method.Firstly,for the situation of having historical remanufacturing information of end-of-life products,this study adopts fuzzy optimization methods to model the re-entrant flexible remanufacturing scheduling problem,and adopts bi-fuzzy theory to describe the uncertainties in remanufacturing production;for the situation of not having historical remanufacturing information of endof-life products,this study adopts interval grey number theory to describe the uncertainties,and considers the rework problem in remanufacturing production.The interval grey number scheduling optimization model for remanufacturing production is developed.The study also proposes two different hybrid optimization algorithms to solve the above problem efficiently.The main innovations of this study are as follows.(1)For the situation of having historical remanufacturing information of end-of-life products,a fuzzy optimal scheduling model for remanufacturing production is proposed.This model considers the inherent uncertainty in remanufacturing caused by uncertainty in the quality of the end-of-life product and the uncertainty in processing time and cost caused by uncertainty in the remanufacturing environment,and describes them using bifuzzy theory to obtain a more realistic remanufacturing production scheduling solution.In addition,the re-entrant flexible shop is considered,considering the fact that some of the remanufacturing processes are re-entrant in order to bring the remanufactured product to a new state.This model aims to minimize the maximum completion time,the total cost and the total machine load in order to improve remanufacturing efficiency.To address the above model,this study proposes an improved fast non-dominated sorting genetic algorithm that integrates reinforcement learning with the basic fast non-dominated sorting genetic algorithm,and designs new population initialization strategies as well as local search strategies to efficiently advance the solution and improve the search speed and search capability of the algorithm to obtain a better scheduling solution.(2)A remanufacturing production interval grey number scheduling optimization model is proposed for the situation of not having historical remanufacturing information of end-of-life products.Although bi-fuzzy theory can deal with the uncertainties in remanufacturing production effectively,it is based on the premise that historical remanufacturing information of end-of-life products is available.In contrast,interval grey number theory can deal with uncertainty in the absence of specific information on endof-life products.Thus,this study applies interval grey number theory to model the remanufacturing scheduling optimization problem.It considers the processing time uncertainty,processing route uncertainty,rework risk and rework frequency caused by quality uncertainty,and uses multiple non-equivalent parallel processing lines for remanufacturing to improve the practicality of the model.In this study,an improved hybrid differential evolution and particle swarm optimization algorithm is proposed to solve the model,in which the scaling factor and crossover probability of the differential evolution algorithm are adaptively parameterized to speed up convergence,a position update mechanism is designed to enable the particle swarm optimization algorithm to solve the proposed problem effectively,a two-way bootstrap strategy is used to improve the performance of the particle swarm optimization algorithm,and a local search strategy is designed to obtain a better scheduling solution more quickly.Hence this study concerning the research on the optimization of remanufacturing production scheduling with uncertain factors have significant practical value in promoting the development of China’s circular economy and realizing sustainable development and resource recycling in the manufacturing industry.