Job-shop Scheduling Problems Considering Similar Learning Effect in One-worker and Multiple-machine Partterns

A multi-objective job shop scheduling model was established considering the effects of job similarity and personnel learning under the one-worker multiple-machine production mode, and a grid filtering external archive genetic algorithm(GFEAGA) was designed to solve the scheduling problems. In order to improve solution efficiency, a two-stage coding and decoding approach was adopted, and an improved N6 neighbor structure search method was applied. The personnel selection method was designed to balance personnel workload. Non-dominant individuals were filtered based on grid sorting to enhance the diversity of the solution sets. The experiments verified the high efficiency and superiority of GFEAGA solution, and the sensitivity of the similarity and learning rate in the model were analyzed. © 2023 China Mechanical Engineering Magazine Office. All rights reserved.