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Proposta de classificação hierarquizada dos modelos de solução para o problema de job shop scheduling A proposition of hierarchical classification for solution models in the job shop scheduling problem [PDF]

open access: goldGestão & Produção, 1999
Este artigo propõe uma classificação hierarquizada dos modelos utilizados na solução do problema de programação da produção intermitente do tipo job shop, incluindo tanto os que fornecem solução ótima, quanto os modelos heurísticos mais recentes baseados
Ricardo Ferrari Pacheco   +1 more
doaj   +2 more sources

A product-driven system with an evolutionary algorithm to increase flexibility in planning a job shop. [PDF]

open access: yesPLoS ONE, 2023
The scheduling of a job shop production system occurs using models to plan operations for a given period while minimizing the makespan. However, since the resulting mathematical models are computationally demanding, their implementation in the work ...
Patricio Sáez   +4 more
doaj   +3 more sources

A benchmark dataset for multi-objective flexible job shop cell scheduling [PDF]

open access: yesData in Brief
This data article presents a description of a benchmark dataset for the multi-objective flexible job shop scheduling problem in a cellular manufacturing environment. This problem considers intercellular moves, exceptional parts, sequence-dependent family
Derya Deliktaş   +3 more
doaj   +2 more sources

Research on Priority-based Flexible Job Shop Scheduling Algorithm [PDF]

open access: yesE3S Web of Conferences, 2021
Research on the Flexible Job Shop Scheduling (FJSP) problem in the manufacturing process of aircraft engines has been carried out. It is found that there are real requirements such as order mandatory nodes and equipment selection in the current job ...
Liu Manxing, Xia Xiufeng
doaj   +1 more source

A Research Review on Job Shop Scheduling Problem [PDF]

open access: yesE3S Web of Conferences, 2021
In recent years, the manufacturing industry has developed rapidly with fierce competition. Manufacturing enterprises are faced with the challenge of on-time delivery, multiple product choices and quick response to order modification.
Yu Yingchen
doaj   +1 more source

Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning [PDF]

open access: yesInternational Journal of Production Research, 2021
We propose a framework to learn to schedule a job-shop problem (JSSP) using a graph neural network (GNN) and reinforcement learning (RL). We formulate the scheduling process of JSSP as a sequential decision-making problem with graph representation of the
Junyoung Park   +4 more
semanticscholar   +1 more source

Flexible Job-Shop Scheduling via Graph Neural Network and Deep Reinforcement Learning

open access: yesIEEE Transactions on Industrial Informatics, 2023
Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching rules (PDRs) for solving complex scheduling problems. However, the existing works face challenges in dealing with flexibility, which allows an operation to be ...
Wen Song   +3 more
semanticscholar   +1 more source

Multi-Objective Flexible Job Shop Scheduling Using Genetic Algorithms

open access: yesTehnički Vjesnik, 2022
Flexible Job Shop Scheduling is an important problem in the fields of combinatorial optimization and production management. This research addresses multi-objective flexible job shop scheduling problem with the objective of simultaneous minimization of ...
Attia Boudjemline   +5 more
doaj   +1 more source

Deep Reinforcement Learning for Dynamic Flexible Job Shop Scheduling with Random Job Arrival

open access: yesProcesses, 2022
The production process of a smart factory is complex and dynamic. As the core of manufacturing management, the research into the flexible job shop scheduling problem (FJSP) focuses on optimizing scheduling decisions in real time, according to the changes
Jingru Chang   +4 more
semanticscholar   +1 more source

Solving flexible job shop scheduling problems in manufacturing with Quantum Annealing

open access: yesProduction Engineering, 2022
Quantum Annealing (QA) is a metaheuristic for solving optimization problems in a time-efficient manner. Therefore, quantum mechanical effects are used to compute and evaluate many possible solutions of an optimization problem simultaneously.
Philipp Schworm   +3 more
semanticscholar   +1 more source

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