Research On Scheduling Optimization Of Dual-resource Flexible Job-shop Considering Human Factor

The Intelligent Manufacturing Development Plan of the 14 th five-year Plan puts forward that it is necessary to speed up the construction of intelligent workshops and factories with flexible production capacity and vigorously develop advanced manufacturing modes such as personalized customization.Under this situation,many enterprises are exploring and practicing the discrete flexible production mode of small batch and multi variety.Flexible job shop is a typical multi-product flexible manufacturing mode.Because this manufacturing mode is more in line with the actual production process,it has become a research hotspot in the field of job shop scheduling.At this stage,workers are still an important part of the production system,so it is necessary to take workers’ fatigue level,moving time and other human factors into FJSP.At the same time,the resources of workers and machines in the workshop are limited,and it is very important to use the resources of workers and machines efficiently.Based on the above background,this paper explores FJSP under the resource constraints of two machines and workers.For single-objective scheduling problem of dual-resource flexible job shop(So-DRCFJSP)with human factors,the corresponding scheduling model is constructed,and the human factor of worker movement time is considered in the model,which makes the model closer to the production reality.In order to minimize the completion time,a hybrid genetic algorithm(HGA)is developed.Compared with GA and DPSO,it is found that the proposed algorithm can solve So-DRCFJSP more effectively.In complex situations,it is difficult to meet the actual production needs only to optimize the completion time.For this reason,this paper further explores the dual-resource flexible job shop multi-objective scheduling problem(Ma F-DRCFJSP)considering human factors.In the scheduling model,two human factors,workers’ moving time and workers’ fatigue level,are considered.The model takes completion time,workers’ fatigue level,key machine load and total delay as optimization objectives,for the purpose of optimizing resource allocation throughout the workshop,improving production efficiency and customer satisfaction,and reducing workers’ fatigue levels at the same time.Then,an improved third generation nondominated sorting genetic algorithm(INSGA-III)is designed to solve the problem.In this algorithm,in order to ensure the quality and diversity of the initial population,four initialization strategies are adopted;in order to avoid the algorithm converging too fast,the consanguinity mutation mechanism is introduced;in order to improve the optimization ability,three neighborhood structures are designed.Simulated annealing algorithm is used for neighborhood search.Finally,the effectiveness of INSGA-III is verified by several numerical examples,and the algorithm is used to solve the actual scheduling problem in a foundry workshop.The results show that INSGA-III can effectively solve the Ma F-DRCFJSP and solve the actual scheduling problem in the workshop.Thus,in theory,this study can improve the production efficiency of manufacturing enterprises,balance machine load and worker load in the workshop,it’s very meaningful to ensure the delivery on time and enhance customer satisfaction.In addition,this paper also have practical application value to a certain extent.Because it’s results can provide some decision-making reference for dispatching managers.