Abstract
This paper presents an effective learning interactive genetic algorithm based on edge selection encoding (LIGA-ESE) for solving the assembly job shop scheduling problem (AJSSP), with the aim of minimizing the maximum completion time (makespan). In the LIGA-ESE, a novel ESE based on virtual components for producing every feasible solution generated with acceptable probability is proposed to improve the convergence efficiency. Based on the proposed competition and learning interactive mechanisms, two genetic algorithm populations from obverse and reverse schedules, respectively, compete to improve the utilization of computation resources and the quality of populations. In addition, the influences of parameters are considered, and a learning parameter combination selection mechanism based on historical optimization performance is proposed for further improving the convergence efficiency of the LIGA-ESE. Finally, a numerical simulation is conducted based on widely used AJSSP instances. Comparisons between the LIGA-ESE and existing algorithms and other LIGA-ESE versions demonstrate its effectiveness and superiority in solving the AJSSP.
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