Application Of Improved Artificial Bee Colony Algorithm In Flexible Job Shop Scheduling Problem

What industry particularly appreciates is research that how to improve the production efficiency and competitiveness of manufacturing enterprises under the background of ‘Made in China 2025' and ‘Industry 4.0'.Scheduling plays an important role in both manufacturing and service industries for improving organizational effectiveness and customer satisfaction.Flexible job shop scheduling problem(FJSP)is always a research focus in the area of production scheduling research.On the one hand,due to machine constraint,FJSP is much more suitable to actual production process.On the other hand,FJSP is also a key factor for improving production efficiency.Considering the above-mentioned,this work is mainly focus on the research of flexible job shop scheduling problem.And it will research on how to apply artificial bee colony algorithm in both single-objective FJSP optimization and multi-objective FJSP optimization.According to the nature and extent of flexible job shop scheduling problem,there are two points in modeling of this work: one is mathematical modeling,another is disjunctive graph.In addition,in order to satisfy the requirement of practical production,this work determines decision variables,constrain condition and optimization criterion.After introducing the FJSP model,an artificial bee colony algorithm is applied to solve this problem.Some test functions are used to test the performance of ABC algorithm,and case study of FJSP is also presented to demonstrate the feasibility and effectiveness of ABC algorithm.An improved artificial bee colony algorithm is proposed to solve the flexible job shop scheduling problem with makespan optimization.Unlike the original ABC algorithm,there are three improvements: hybrid strategies are utilized to generate the initial food sources with high level of quality and diversity;an improved RPOX crossing is incorporated into employed bees to generate more feasible solutions;local search based on SA is designed for onlooker bees to improve searching capability for solutions.Two sets of well-known benchmark instances are tested and the caparisons with some existing algorithms verify the effectiveness of the proposed IABC algorithm.And IABC algorithm could get optimal scheduling schemes.Finally,the proposed IABC algorithm is applied to the actual production data of the aircraft assembly line,and the optimal scheduling scheme is obtained,which proves the application value of the algorithm in actual production.By some extension,an artificial bee colony algorithm is applied to solve the three-objective FJSP with makespan,maximum machine loading and total machine loading optimization.The following three aspects is done on this algorithm.First,a neighborhood search is incorporated into employed.Second,onlooker bees use tournament selection to select food source.Third,an improved update mechanism is used in external Pareto archive set(AS).Finally,experiment results and comparisons with some existing algorithms demonstrate the effectiveness and superiority of ABC in obtaining optimal solution aggregate.