Modeling And Optimization Of Energy-saving Flexible Job Shop Scheduling Considering Transportation Constraints

Job shop scheduling is an important means to make rational use of producing materials,improving processing efficiency and reducing production costs during production and processing.In recent years,with the diversification of customer needs,the orders for multivariety and small-batch production in job shops have increased significantly.In order to adapt to this production mode,the manufacturing industry urgently needs new production scheduling methods.In order to simplify the workshop scheduling model,the existing workshop scheduling research often has assumptions that are not in line with the actual workshop processing environment:(?)All workpieces are ready on the machine at time 0and can be processed directly;(?)The workpieces are not considered during the processing transportation constraints between machines.The above two assumptions lead to huge deviations between the shop scheduling solution plan and the actual shop processing plan.The traditional flexible job shop scheduling problem usually ignores the constraints on the transportation of workpieces between machines.In response to this problem,this paper proposes a flexible job shop scheduling model that considers transportation constraints and energy saving.The main tasks are as follows:(1)Establish a mathematical model of flexible job shop scheduling that considers transportation constraints and energy saving.The model focuses on the four goals of maximum completion time,total energy consumption?equipment load and delay of the workshop process.Three transportation constraints are considered in the model namely:(?)The initial position of the workpiece;(?)There is transportation time in adjacent processes that are not processed by the same machine;(?)There are symmetric and asymmetric transportation workshops.(2)A multi-objective solution algorithm for the flexible job shop scheduling problem considering transportation constraints is designed.The algorithm uses a four-layer matrix coded chromosome including: process,machine,candidate machine and transportation time code.Among them,the process coding layer and the machine coding layer are dominant genes,while the alternative machine coding layer and transportation time coding layer are recessive genes,as well as corresponding chromosome decoding?crossover? mutation and other operations are designed.(3)Based on the improved NSGA-? algorithm to solve the workshop scheduling problem,the learning mechanism is introduced into the algorithm,that is,the learning global optimal solution generation individual is included into the descendants.Kacem and Brandimarte standard examples are used to respectively apply to the asymmetric and symmetric transportation workshops.The superiority of the improved NSGA-? algorithm and the feasibility of the mathematical model of flexible job shop scheduling considering transportation constraints are verified.(4)Based on the above research,according to the multi-objective flexible job shop scheduling problem in a heavy-duty gear processing workshop,the research results of this article are applied to the heavy-duty gear processing workshop to optimize the processing efficiency of the heavy-duty gear in the job shop and provide intelligent scheduling plan to further reduce the total energy consumption of the job shop.