Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems

Computers & Electrical Engineering, Год журнала: 2024, Номер 120, С. 109780 - 109780

Опубликована: Окт. 18, 2024

Язык: Английский

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A Q-learning based multi-strategy integrated artificial bee colony algorithm with application in unmanned vehicle path planning

Expert Systems with Applications, Год журнала: 2023, Номер 236, С. 121303 - 121303

Опубликована: Авг. 24, 2023

Язык: Английский

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Modelling and optimization of distributed assembly hybrid flowshop scheduling problem with transportation resource scheduling

Computers & Industrial Engineering, Год журнала: 2023, Номер 186, С. 109717 - 109717

Опубликована: Окт. 31, 2023

Язык: Английский

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Intelligent Scheduling Methods for Optimisation of Job Shop Scheduling Problems in the Manufacturing Sector: A Systematic Review

Electronics, Год журнала: 2025, Номер 14(8), С. 1663 - 1663

Опубликована: Апрель 19, 2025

This article aims to review the industrial applications of AI-based intelligent system algorithms in manufacturing sector find latest methods used for sustainability and optimisation. In contrast previous articles that broadly summarised existing methods, this paper specifically emphasises most recent techniques, providing a systematic structured evaluation their practical within sector. The primary objective study is algorithms, including metaheuristics, evolutionary learning-based sector, particularly through lens optimisation workflow production lines, Job Shop Scheduling Problems (JSSPs). It critically evaluates various solving JSSPs, with particular focus on Flexible (FJSPs), more advanced form JSSPs. process consists several intricate operations must be meticulously planned scheduled executed effectively. regard, Production scheduling best possible schedule maximise one or performance parameters. An integral part JSSP both traditional smart manufacturing; however, research focuses concept general, which pertains concerns aim maximising operational efficiency by reducing time costs. A common feature among studies lack consistent effective solution minimise energy consumption, thus accelerating minimal resources.

Язык: Английский

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N-list-enhanced heuristic for distributed three-stage assembly permutation flow shop scheduling

Annals of Operations Research, Год журнала: 2023, Номер unknown

Опубликована: Июль 4, 2023

Abstract System-wide optimization of distributed manufacturing operations enables process improvement beyond the standalone and individual optimality norms. This study addresses production planning a system consisting three stages: parts (subcomponents), assembly components in Original Equipment Manufacturer (OEM) factories, final products at product manufacturer’s factory. Distributed Three Stage Assembly Permutation Flowshop Scheduling Problems (DTrSAPFSP) models this operational situation; it is most recent development literature scheduling problems, which has seen very limited for possible industrial applications. research introduces highly efficient constructive heuristic to contribute on DTrSAPFSP. Numerical experiments considering comprehensive set parameters are undertaken evaluate performance benchmark algorithms. It shown that N-list-enhanced Constructive Heuristic algorithm performs significantly better than current best-performing new metaheuristics terms both solution quality computational time. can, therefore, be considered competitive future studies computing.

Язык: Английский

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Correction: N-list-enhanced heuristic for distributed three-stage assembly permutation flow shop scheduling

Annals of Operations Research, Год журнала: 2023, Номер unknown

Опубликована: Июль 24, 2023

Язык: Английский

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