Research On Green Scheduling Optimization Of Unrelated Parallel Machines Based On Jaya Algorithm

In 2013,the German government proposed the "industry 4.0" strategy,which was officially launched at the Hannover Industrial Expo,marking the beginning of the latest round of industrial revolution.The strategy aims to use information technology to improve the intelligent level of manufacturing industry.In this regard,China put forward the "made in China 2025" strategy in2015,in which two main strategic tasks are: promoting the deep integration of industrialization and informatization(informatization and industrialization)and comprehensively promoting green manufacturing.Scheduling is an important link in manufacturing industry.Optimizing the level of scheduling energy consumption is an important way to respond to green scheduling.In addition,combined with the actual production situation that different products of enterprises have different processing efficiency in different machines,this paper selects irrelevant parallel machine scheduling as the research object.For the two problems with different constraints and different scheduling objectives,it is solved based on the Jaya algorithm framework and the problem constraint design strategy.Finally,the two algorithms are applied to the actual problems of company A.The main work of this paper is as follows:(1)Aiming at the unrelated parallel machine system in which the workpiece has release time and delivery time constraints and the machine can be shut down / restarted,the weighted sum minimum scheduling optimization problem of total workpiece delay time and total energy consumption of the machines is studied.Firstly,the mixed integer programming model of the problem is established,and then the two-stage coding scheme is used to effectively represent the solution of the problem.Secondly,an improved strategy is proposed to form an adaptive elite Jaya algorithm(AE-Jaya): the principle of opposing learning enhances the individual diversity in the algorithm population;Adaptive population elitism promotion algorithm and local search strategy;The crossover strategy based on the idea of Jaya algorithm can ensure the optimization performance of the algorithm while ensuring the internal solution satisfying the constraints in the iterative process.Finally,the effectiveness of the algorithm strategy is verified by randomly generated examples,and AE Jaya algorithm is compared with other algorithms for solving unrelated parallel scheduling problems to verify the superiority of AE Jaya algorithm in solving this problem.(2)Aiming at the uncorrelated parallel machine system with sequence dependent set-up time,the scheduling optimization problem to obtain the Pareto optimal solution of minimizing the maximum completion time and the total energy consumption of the machines is studied.Firstly,the mathematical model of the problem is established,and then the two-dimensional real number coding scheme is used to map the solution space.Secondly,for the multi-objective Jaya algorithm,an improved self-adaptive multi population Jaya algorithm(SAMP-Jaya)is proposed:using two rules to initialize the population and improve the quality of the initial population is helpful to the optimization of the algorithm;Adopt multiple group strategies to improve the global search ability of the algorithm;The position vector sorting method is proposed to update the individual,so as to ensure the effective combination of the update of Jaya algorithm for continuous variables and the iteration of discrete solution of the problem.Finally,four indexes are used to verify the superiority of the algorithm.(3)Aiming at two unrelated parallel machine scheduling problems with different constraints and objectives,this paper proposes AE-Jaya algorithm and SAMP-Jaya algorithm to solve them effectively.After applying the two algorithms to the company example,it is found that the optimal solutions of the two algorithms are similar,the convergence speed of AE Jaya algorithm is faster,and the global search ability of Samp Jaya algorithm is stronger.Compared with the existing scheduling schemes of the company,the optimal solutions of the two algorithms can not only reduce the total weighted tardiness of workpieces,but also significantly reduce the total energy consumption of machines,reduce the scheduling target value and respond to green scheduling.