The Intelligent Optimization Algorithm Based On The Probability Model Solves The Multi-objective Shop Scheduling Problem

The multi-objective workshop scheduling problem refers to the production target of the company's production while ensuring economic benefits,but also to complete on-time delivery,low-carbon emission reduction,and reduced consumption.The Intelligent Optimal Algorithm Based on Probability Model(IOA PM)is a new class of intelligent algorithms,which is different from traditional intelligent algorithms.IOA PM can count the order relationship information in the good solution and infect it to the new individual in the process of algorithm search,so that the algorithm can quickly converge and also have certain local search ability.Therefore,IOA PM is an ideal carrier for learning order relations.This paper studies IOA PM based on two important multi-objective models in shop scheduling.main tasks as follows:(1)A Hybrid Quantum Evolution Algorithm is designed for the problem of blocking flow shop scheduling for makespan and carbon emissions(Total Carbon Emissions).The algorithm combines quantum evolution with neighborhood search,and uses the global search ability of quantum optimization algorithm to explore the high-quality solution region of the problem,and uses the neighborhood search mechanism to conduct more detailed surveys.The effectiveness of the proposed algorithm for solving such problems is verified by simulation experiments and results.(2)A Hybrid Estimation of Distribution Algorithm(HEDA)is proposed for no-wait job shop scheduling problem with maximum completion time(Makespan)and maximum delay time.In the population initialization stage,heuristic operation is added to improve the quality of the initial solution,and the global exploration direction of the algorithm is guided.The variable neighborhood-based search mechanism enhances the local survey efficiency of the algorithm.Finally,the effectiveness and robustness of HEDA are verified by comparison.(3)For the green no-wait job shop scheduling problem with the optimization goal of total flow time and total energy consumption,an improved Estimation of Distribution Algorithm based on front end omission(Improved Estimation of Distribution Algorithm,IEDA)is proposed.Solve.Combined with the problem characteristics of NWJSSP,a local search strategy based on the combination of front-end omission and variable neighborhood is designed into EDA.The EDA sampling mechanism and the probabilistic model updating mechanism are improved to form an improved distribution estimation algorithm.Finally,the effectiveness of the proposed algorithm in solving such problems is verified by experiments and comparisons.