Flexible Job Shop Scheduling Based On Hybrid Frog Leaping Algorith

In the context of economic and information globalization,the development of a strong manufacturing industry is the key to a strong nation.Therefore,it has become imperative to change the traditional manufacturing mode and actively transform to intelligent manufacturing.In this process,the flexible job shop scheduling problem,which is more suitable for the actual production,has received wide attention,and how to coordinate production efficiency and energy consumption has undoubtedly become a hot issue.In this paper,we design an improved shuffled frog leaping algorithm to solve different types of flexible job shop scheduling problems with the following main works.Firstly,a flexible job shop scheduling model with maximum completion time as the optimization objective is established,and a shuffled frog leaping algorithm based on greedy decoding is designed.In the initialization stage,a mixture of multiple heuristic initialization rules is proposed to generate a high-quality initial population,which provide an excellent starting point for algorithm evolution.In the decoding stage,the greedy insertion method is used to improve the decoding timeliness.A local search method with coordinated machine load balancing is proposed to enhance the local search capability of the algorithm.The effectiveness of the improved strategy is verified by testing with Brandimarte standard arithmetic cases;experimental comparisons with three algorithms are conducted,and the results show that the proposed algorithm has outstanding search performance.Secondly,considering the diversified needs of actual scheduling objectives,a multi-objective flexible job shop scheduling model with maximum completion time,bottleneck machine load and total machine load as objectives is established.Considering the disadvantages of the frog leaping algorithm in terms of insufficient merit-seeking ability and poor distribution,a shuffled frog leaping algorithm based on collaborative search is proposed to solve the multi-objective flexible job shop scheduling problem.A mixture of multiple heuristic initialization rules is used to generate high-quality populations to speed up the convergence of the algorithm.An individual evaluation mechanism is proposed and the quality is assigned to the modal groups,and different evolutionary strategies are used for modal groups with different quality characteristics and collaborative search is performed to give full play to their search ability.A variety of local search strategies are proposed to strengthen the algorithm’s search ability.The effectiveness and superiority of the improved strategies and the proposed algorithms are verified by the Brandimarte standard algorithm.Finally,considering the dynamic disturbance of the scheduling process caused by unexpected events in actual production,a dynamic scheduling model of flexible job shop with the objectives of maximum completion time,energy consumption and stability is established.An adaptive shuffled frog leaping algorithm is proposed to solve the dynamic scheduling problem for three common dynamic events: machine failure,rush order and order cancellation.Adaptive crossover and variation probabilities are introduced to ensure the algorithm’s optimization and fast convergence.An adaptive strategy for the number of modal groups is proposed to enhance the ability of mining the global solution space.Different heuristic repair schemes are designed for each dynamic event at the rescheduling moment.A multi-objective decision method is used to assist decision makers in selecting the most reasonable scheduling scheme,which enables the shop to respond to dynamic events quickly.The experimental comparison by Brandimarte standard arithmetic proves that the improved algorithm has good merit seeking and fast response capability.