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

Abstract

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.

Textile Flexible Job-Shop Scheduling Based on a Modified Ant Colony Optimization Algorithm

“…We then compared the average value of T * for each algorithm with the average value of T * obtained by the IPACO algorithm, and the optimal value of T * for each algorithm with the optimal value of T * obtained by the IPACO algorithm. We refer to this comparison as the relative percentage error (RPE) [32]. The RPE is defined by Equation (26).…”

A robust optimal scheduling system based on multi-performance driving for complex manufacturing systems

“…Ba et al 13 measured robustness by comparing the actual scheduling goal with the initial scheduling goal, and provided a method to measure initial scheduling robustness. For scheduling problems with interval processing times, Zheng et al 14 used the maximum regret value of the actual scheduling target for initial scheduling under the disturbance of uncertain factors to express robustness. A decomposition method based on graph theory was proposed by Kutanoglu et al 15 to achieve scheduling robustness using expected average weighted tardiness as the robustness index.…”

A robust optimal scheduling system based on multi-performance driving for complex manufacturing systems

“…They measured robustness by comparing the actual scheduling goal with the initial scheduling goal, and they provided a method to measure initial scheduling robustness. For scheduling problems with interval processing times, Zheng et al [14] used the maximum regret value of the actual scheduling target for initial scheduling under the disturbance of uncertain factors to express robustness, which essentially transformed the robust scheduling problem into a decision-making problem. A decomposition method based on graph theory was proposed by Kutanoglu et al [15] to achieve scheduling robustness using expected average weighted tardiness as the robustness index.…”