Research On Modeling And Optimization Algorithms Of Hybrid Flow Shop Scheduling With Deterioration And Learning Effect

China has just completed the first centenary goal,is in a critical period of climbing over the hill,transformation and upgrading.Manufacturing as a pillar industry of the national economy,is an important embodiment of national creativity,competitiveness and comprehensive national strength.The party and the government pay special attention to the high-quality development of manufacturing.Up to now,China has ranked as the world’s largest manufacturing country for twelve consecutive years,but it is still a long way to become the world’s largest manufacturing powerhouse.Improving production efficiency and quality is the focus of the manufacturing industry to achieve leadership in the Red Sea of competition,and it is also the only way for China to become a manufacturing powerhouse.In the traditional shop scheduling research,the vast majority of the study is the deterministic problem.The processing time of the workpiece is fixed,but this is not suitable for intelligent production environments.In the intelligent manufacturing plant,in order to meet the customized production needs of customers,the intelligent machine can collect information in real time during the processing process to optimize and train itself to make itself more proficient in the product,so that when the intelligent machine processes the subsequent workpiece,the processing time of the workpiece is shortened with the increase of the number of processing,such a phenomenon is called the learning effect;in addition,as a centralized machining center,the processed parts of the smart machine will also have different degrees of wear over time,resulting in an extension of the processing time of the workpiece,which is called the deterioration effect.In this paper,under the premise of considering the characteristics of deterioration and learning effect,the shop scheduling in different scheduling environments is studied.A mixed integer model is constructed for the problems,and an efficient heuristic algorithm is designed.Firstly,the problem of unrelated parallel machine scheduling with deterioration and learning effects aimed at minimizing both makespan and total weighted tardiness is studied.Aiming at such NP-hard problems,an improved simulated annealing algorithm based on two-segment encoding is designed,and the workpiece machining sequence code of the first segment and the machining information code of the second segment are generated by combining the random program and the uniform distribution strategy to obtain the initial scheduling solution of the problem.Then the segmented exchange and variation perturbation operations are proposed to obtain the updated solution.Through the simulation experiment test,the improved simulated annealing algorithm is compared with some heuristic algorithms,and the experimental results show that the proposed algorithm can obtain a better near-optimal solution.Secondly,in the further expansion of the unrelated parallel machine scheduling environment,the hybrid flow shop scheduling problem with deterioration and learning effect is studied with the goal of minimizing makespan.A matrix form of coding scheme is designed.In the first stage of the hybrid flow shop,the first column of the matrix is encoded in decimals,according to which the assignment of the workpiece on the machine and the ordering of the workpiece on the machine are determined.The integer code is designed in the subsequent stages to determine the machine allocation of the workpiece,and the processing sequence of the workpiece on the machine is determined according to the time order of arrival of the workpiece.Finally compared with some other heuristic algorithms,the experimental results show that the proposed algorithm can obtain a better near-optimal solution.This paper discusses the shop scheduling problems with both deterioration and learning effects with the goal of minimizing the sum of makespan and total weighted tardiness and minimizing makespan.Designed efficient heuristic algorithms for the characteristics of the problems,and expanded the research on the shop scheduling problems with both deterioration and learning effects.And also provides a certain reference for actual industrial production.