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 uncer...
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doaj-c7a38c5bd05640078fb768bb9aa5b8bd2021-06-30T23:38:17ZengMDPI AGApplied Sciences2076-34172021-06-01115333533310.3390/app11125333A Data-Driven Robust Scheduling Method Integrating Particle Swarm Optimization Algorithm with Kernel-Based EstimationPeng Zheng0Peng Zhang1Ming Wang2Jie Zhang3School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaInstitute of Artificial Intelligence, Donghua University, Shanghai 201620, ChinaCollege of Mechanical Engineering, Donghua University, Shanghai 201620, ChinaInstitute of Artificial Intelligence, Donghua University, Shanghai 201620, ChinaThe 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.https://www.mdpi.com/2076-3417/11/12/5333robust schedulingparticle swarm optimization algorithmkernel density estimationinterval theory |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Peng Zheng Peng Zhang Ming Wang Jie Zhang |
spellingShingle |
Peng Zheng Peng Zhang Ming Wang Jie Zhang A Data-Driven Robust Scheduling Method Integrating Particle Swarm Optimization Algorithm with Kernel-Based Estimation Applied Sciences robust scheduling particle swarm optimization algorithm kernel density estimation interval theory |
author_facet |
Peng Zheng Peng Zhang Ming Wang Jie Zhang |
author_sort |
Peng Zheng |
title |
A Data-Driven Robust Scheduling Method Integrating Particle Swarm Optimization Algorithm with Kernel-Based Estimation |
title_short |
A Data-Driven Robust Scheduling Method Integrating Particle Swarm Optimization Algorithm with Kernel-Based Estimation |
title_full |
A Data-Driven Robust Scheduling Method Integrating Particle Swarm Optimization Algorithm with Kernel-Based Estimation |
title_fullStr |
A Data-Driven Robust Scheduling Method Integrating Particle Swarm Optimization Algorithm with Kernel-Based Estimation |
title_full_unstemmed |
A Data-Driven Robust Scheduling Method Integrating Particle Swarm Optimization Algorithm with Kernel-Based Estimation |
title_sort |
data-driven robust scheduling method integrating particle swarm optimization algorithm with kernel-based estimation |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-06-01 |
description |
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. |
topic |
robust scheduling particle swarm optimization algorithm kernel density estimation interval theory |
url |
https://www.mdpi.com/2076-3417/11/12/5333 |
work_keys_str_mv |
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