Method And Application Of Flexible Job Shop Scheduling With Dual-Resource Constrained Considering Behavioral Effects

With the rapid development of manufacturing information,enterprises have made great achievements in manufacturing execution system.Job shop scheduling problem is in the central position of manufacturing execution system.It is highly valued by researchers.Job shop scheduling problem is one of the hot topics in the research of the job shop scheduling problem,and it is also the theoretical focus of scholars.This paper focuses on the flexible job shop scheduling problem with dual resource constraints,which includes both human resource constraints and equipment resource constraints.On this basis,considering the impact of employee behavioral effect on processing time and NC equipment participating in processing and scheduling results,it has important research value and practical significance to be close to the real production environment.Firstly,considering the objective human factor on processing time in actual production including learning effect,forgetting effect and the effect of initial skill level,a dual resource constrained flexible job shop scheduling problem with learning forgetting effect is constructed with the objectives of maximum completion time,critical equipment load and total equipment load.An improved cultural genetic algorithm based on NSGA-II framework is designed.In order to improve the convergence speed of the algorithm,a number of effective cross-mutation operators are randomly selected during the execution of the algorithm,and a critical path-based neighborhood search strategy is implemented for excellent individuals.Secondly,in view of the complex manufacturing environment of the production workshop,considering the extensive application of numerical control equipment,the interpolation of machine-added/non-machine-processed sequence in the process route,an extended dual resource constrained flexible job shop scheduling mathematical model considering learning forgetting effect is constructed,and multi-objective optimization is carried out with the objectives of maximum completion time,total tardiness,total energy consumption and total cost.Aiming at the characteristics and complexity of the problem,a novel multi-group cooperative teaching and learning optimization algorithm is proposed.Aiming at the characteristic that NC equipment does not need human assistance during its operation,three-layer coding and new decoding methods are designed to avoid the conflict of equipment personnel.In addition,a variety of teaching/self-learning factors and self-learning/communication strategies are designed to effectively solve discrete problems and balance the global and local search ability of the algorithm.Finally,the practical application of the dual-resource-constrained production workshop is implemented.Two scheduling models constructed in this paper are applied and solved by using the corresponding algorithms designed.The validity and effectiveness of the model are verified by comparing and analyzing with other algorithms.