Hybrid Sparrow Search Algorithm For Optimizing Flexible Job Shop Scheduling Problem

The flexible job shop scheduling problem(FJSP)is a widely studied scheduling problem in processing environments with characteristics such as multiple varieties,small batches,and high variability.Due to its high complexity,FJSP has been an active research area and is considered more flexible and practical compared to traditional job shop scheduling.This thesis focus on FJSP as the research object and investigates both single-objective and multi-objective optimization strategies.The single-objective approach aimes to minimize the maximum makespan,while the multi-objective approach aimes to minimize total energy consumption,total processing cost,and maximum makespan.The main research contents are as follows.Moving to the single objective flexible job shop scheduling problem,a hybrid sparrow search algorithm was designed in this paper to solve the single-objective flexible job shop scheduling problem,with the maximum completion time as the optimization objective.A GLR machine selection strategy was utilized in this algorithm from a global perspective to generate the initial population,thus enhancing the optimization efficiency.Additionally,a local search method based on the critical path was integrated into the algorithm from a local perspective to enhance the global search ability of the algorithm.By combining these two approaches,the algorithm is able to provide an effective solution for the FJSP problem.Moving to the multi-objective flexible job shop scheduling problem(MOFJSP),this thesis proposed a non-dominated sorting-based sparrow search algorithm.This algorithm incorporated two perspectives for global and local search abilities.The population individuals were ranked and sorted by non-dominated ranks and crowding distance entropy for global search.Additionally,three local search methods were adopted to optimize the multiple objectives and enhance the search ability of the algorithm.To verify the effectiveness of the proposed algorithm,a series of experiments were conducted,and its performance was compared with other algorithms using FJSP test cases of different scales.The experimental results demonstrate that the proposed algorithm outperforms other algorithms in solving FJSP problems.Finally,the multi-objective sparrow search algorithm was employed to develop a versatile job shop scheduling system that enables decision-makers to select scheduling strategies tailored to the unique demands of the production workshop.This system was crafted using a mixed programming methodology that incorporates Py Qt,Python,and MATLAB.A case study of a manufacturing company was conducted to validate the feasibility of the system,and the results demonstrate its effectiveness in a practical setting.In conclusion,the study outlined in this thesis presented a novel methodology to address the flexible job shop scheduling problem.Additionally,the resultant flexible job shop scheduling system offered robust assistance for scheduling determinations within real-life production settings.