Research On Flexible Job Shop Green Scheduling Problem Considering Different Energy Consumption During Machine Processing Stage

Since the concept of "Industry 4.0" was put forward,the manufacturing industry has ushered in a new round of changes.The "Made in China 2025" strategy has pointed out the development direction of my country’s manufacturing industry,not only to promote intelligent manufacturing,but also to promote green manufacturing.As an important part of intelligent manufacturing,production scheduling is the key to determining whether intelligent manufacturing can proceed smoothly.At present,mainstream production scheduling technologies use scheduling rules(such as the shortest processing time first),and it is often difficult to obtain an efficient scheduling plan.Intelligent optimization algorithm has become an common method for solving production scheduling problems because of its high solving performance and strong robustness.Therefore,this paper takes the important branch flexible job shop green scheduling problem in the field of shop scheduling as the research object,and at the same time studies efficient intelligent optimization algorithms to solve the problem.The research content of this article mainly includes the following three parts:(1)Aiming at the single-objective flexible job shop green scheduling problem(SO-FJGSP),an improved genetic algorithm(IGA)is designed to solve it.First,describe the problem of SO-FJGSP and establish a mathematical model.The objective function is to minimize the weighted sum of the maximum completion time and total energy consumption.Combining the characteristics of SO-FJGSP itself and the defects of genetic algorithm itself,a kind of IGA is proposed.The improvements of IGA are as follows: in the encoding and decoding stage,in order to avoid infeasible solutions,the encoding method of three-dimensional real numbers is adopted;the balanced dispersion principle of orthogonal experiment is introduced to improve the quality of the initial population and enhance the global search performance of the algorithm;in the mutation operation stage,Using dynamic step size for mutation can enhance the local search performance of the algorithm.Brandimarte benchmark examples are used to test IGA,and the test results are compared with several improved algorithms proposed in recent years,which verifies the superiority and effectiveness of IGA in solving scheduling problems.(2)Aiming at the multi-objective flexible job shop green scheduling problem(MO-FJGSP),an adaptive multi-objective Jaya algorithm(SAMO-Jaya)is designed to solve it.First,describe the problem of MO-FJGSP and establish a mathematical model.The objective function includes minimizing the maximum completion time,minimizing the total load of the machine and minimizing the total energy consumption.Based on the characteristics of the multi-objective problem,a SAMOThe main improvements of Jaya and SAMO-Jaya are as follows: A conversion mechanism is designed to establish the mapping between the continuous solution space of the Jaya algorithm and the discrete scheduling space of MO-FJGSP,which is convenient for solving the discrete problem of the SAMO-Jaya algorithm;a uniform distribution is designed The hybrid strategy combined with the chaotic sequence is used as a population initialization method to effectively improve the quality of the initial population;a method of adaptively adjusting the population size is embedded in the algorithm to improve the algorithm’s solution speed.Three simulation experiments of Brandimarte example single-objective simulation,Kacem example multi-objective simulation,and the effect of improvement on SAMO-Jaya are used to verify the SAMO-Jaya algorithm.The results show that the SAMO-Jaya algorithm can effectively solve MO-FJGSP.(3)Taking part of the production data of Yangzhou AS company as a practical case,using the IGA and SAMO-Jaya algorithms to solve the examples respectively.The experimental results verify the effectiveness of the two algorithms in solving practical problems.At the same time,the comparison and analysis between the two algorithms Performance difference.The above-mentioned research has further improved the research content of the FJGSP field,enriched the theoretical system of the corresponding algorithm,and provided certain guiding significance for the actual production of the enterprise.