A Data-Driven Robust Scheduling Method Integrating Particle Swarm Optimization Algorithm with Kernel-Based Estimation

The assembly job shop scheduling problem (AJSSP) widely exists in the production process of many complex products. Robust scheduling methods aim to optimize the given criteria for improving the robustness of the schedule by organizing the assembly processes under uncertainty. In this work, the uncertainty of process setup time and processing time is considered, and a framework for the robust scheduling of AJSSP using data-driven methodologies is proposed. The framework consists of obtaining the distribution information of uncertain parameters based on historical data and using a particle swarm optimization (PSO) algorithm to optimize the production schedule. Firstly, the kernel density estimation method is used to estimate the probability density function of uncertain parameters. To control the robustness of the schedule, the concept of confidence level is introduced when determining the range of uncertain parameters. Secondly, an interval scheduling method constructed using interval theory and a customized discrete PSO algorithm are used to optimize the AJSSP with assembly constraints. Several computational experiments are introduced to illustrate the proposed method, and these were proven effective in improving the performance and robustness of the schedule.

Automated summary

This study proposed a framework using data-driven methodologies to optimize the robustness of assembly job shop scheduling problem (AJSSP). Considering the uncertainty of process setup time and processing time, the study utilized kernel density estimation method and particle swarm optimization (PSO) algorithm for optimizing production schedule, introducing concepts of confidence level and interval theory.