Research On Intelligent Algorithm For Multi-Objective Job Shop Scheduling Problem With Double Flexibility

Flexible job shop scheduling problem(FJSP)has been most widely studied as a type of combinatorial optimization problem.It has important significance for improving work shop efficiency and reducing processing costs.However,with the changes in the shop's processing mode and the company's pursuit of goals,the FJSP issue also needs to incorporate new processing elements to meet the processing requirements in the new situation.The traditional FJSP problem usually considers the machine as the only resource constraint on the shop floor,and studies on other resource constraints such as manual and tooling constraints a re rare,which makes the scheduling model difficult to match the actual operating conditions.Meanwhile,under the background of the country's strong promotion of green manufacturing,how to model the green indicators in the manufacturing system to achieve energy control and environmental protection in the process is an urgent problem to be solved.In addition,how to design an efficient algorithm to solve the new model is also a key and difficult point.The content of this paper is as follows:1)A double flexible multi-objective scheduling shop model considering the constraints of machine resources and manpower resources was proposed,in which manpower as the limited resources and machine in the workshop to determine the processing time of the process.A mathematical model based on process constraints,machine constraints,and artificial constraints was established,whose optimization goal was to minimize the maximum processing time and minimize the maximum machine load and minimize the total machine load.A dual flexible multi-objective shop scheduling model considering the machine,manual constraints and green indicators was established.The effects of different machines and manual selections on energy consumption,noise,chip recycling and safety indicato rs were studied.The optimization objectives were to minimize processing costs and process cycle and optimize green performance.2)A memetic algorithm(MA)was put forward for the double flexible multi-objective scheduling shop model.According to the cha racteristics of the problem,the corresponding coding and decoding methods,crossover and mutation operators were designed,and an elimination operator was raised to guarantee individual diversity in population.In addition,this paper proposed a neighborhood-based search operator based on the critical path,which integrated with non-dominated sorting algorithm(NSGA-II)to achieve accurate search within the problem domain.A set of examples for verifying the effectiveness of the algorithm was designed.Comparison experiments with two other mature multi-objective algorithms showed that the proposed MA algorithm has good performance.3)A non-dominated ensemble fitness ranking algorithm(NEFR)was designed to solve the double FJSP integrating green factors for its multi-goal characteristic.The ensemble fitness ranking algorithm was introduced and improved.The non-dominated ranking and improved ensemble fitness ranking were adopted to select individuals.Two kinds of neighborhood search operators based on the critical path that could take three goals into account were devised and embedded into the NEFR framework to enhance the local search performance of the algorithm.31 verification examples were designed based on the traditional FJSP problem examples,the horizontal and vertical multi-group comparison experiments were performed on NEFR.The results showed that NEFR has better performance in solving multi-objective scheduling problems,and it validated the effectiveness of the proposed neighborhood search str ategy.