Research On Scheduling Problem And Algorithm For Flexible Assembly Job Shop With Tight Job Constraints

With the rapid development of economy and technology,customers' demand for products has become more personalized,and production mode is shifting to mass customization.In the meantime,under the premise of meeting the strict quality requirements and delivery time requirements of each order,how to rationally allocate available resources to maximize production efficiency is an urgent problem for manufacturing enterprises.Production scheduling technology,as one of the core technologies in manufacturing systems,plays an important role in the process of enterprise resource allocation.In the customized production mode,it is necessary to consider the processing and assembly for scheduling.However,most of the current research on assembly scheduling strictly distinguishes the processing and assembly.This method can not be well applied to the production scheduling scheme formulation under the mass customization production mode with complex assembly relations.In addition,in the case of increasingly serious environmental problems and high resource costs,how to reduce energy consumption and environmental pollution in the production process is also an urgent problem for manufacturing enterprises.Therefore,it is of great theoretical significance and application value to study scheduling problems and algorithms by considering processing and assembly and the impa ct of resources and environment.The main content of this paper is as follows:(1)Considering processing and assembly comprehensively,a flexible assembly job shop scheduling model with tight job constraints is constructed to adapt to mass customization production.Aiming at the characteristics of flexible job shop assembly scheduling problem with job constraints,an algorithm with novel genetic operator(JCGA)is proposed.In JCGA,a two-dimensional coding method that can satisfy sequence constraints and job constraints,a compilation rule that guarantees the solution space integrity,a decoding method to reduce constraint judgment,and a reasonable mutation and crossover operator are designed.Based on the comparison with other examples,10 benchmarks are constructed in order to further verify the effectiveness of the algorithm.The experimental results pr ove that JCGA has good performance.(2)Considering the influence of energy efficiency on the flexible job shop assembly scheduling model with job constraints,how to reduce the additional energy consumption caused by the no-load operation of the machine are studied.A flexible job shop assembly scheduling model considering energy efficiency impact is constructed,whose optimization goal was to minimize the latest completion time,total machining energy consumption and the number of machine turn-off/on.According to the characteristics of flexible job shop assembly scheduling problem considering energy efficiency,3 heuristic rules are proposed and proved in detail.On the basis,combined with heuristic rules and multi-objective genetic algorithm NSGA-II,a two-stage optimization algorithm optimization framework is designed,and a multi-objective algorithm NSJCGA-H for solving this problem is proposed.Experimental verification shows that NSJCGA-H can effectively solve the proposed problem,and the proposed heuristic rules have significant effects in reducing the extra energy consumption of the machine and the number of machine turn-off/on.