Multi-objective energy-efficient hybrid flow shop scheduling using Q-learning and GVNS driven NSGA-II
Author:
Publisher
Elsevier BV
Subject
Management Science and Operations Research,Modeling and Simulation,General Computer Science
Reference82 articles.
1. The non-permutation flow-shop scheduling problem: a literature review;Alejandro Rossit;Omega,2018
2. An energy-efficient collaborative strategy of maintenance planning and production scheduling for serial-parallel systems under time-of-use tariffs;An;Appl. Energy,2023
3. A hybrid NSGA-II and VNS for solving a Bi-objective no-wait flexible flowshop scheduling problem;Asefi;Int. J. Adv. Manuf. Technol.,2014
4. Machine learning for combinatorial optimization: a methodological tour D’horizon;Bengio;Eur. J. Oper. Res.,2021
5. A shuffled frog-leaping algorithm with memeplex quality for bi-objective distributed scheduling in hybrid flow shop;Cai;Int. J. Prod. Res.,2021
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2. A feedback-based artificial bee colony algorithm for energy-efficient flexible flow shop scheduling problem with batch processing machines;Applied Soft Computing;2024-03
3. Reinforcement Learning-Based Multi-Objective of Two-Stage Blocking Hybrid Flow Shop Scheduling Problem;Processes;2023-12-25
4. Effective Two-Phase Heuristic and Lower Bounds for Multi-Stage Flexible Flow Shop Scheduling Problem with Unloading Times;Symmetry;2023-10-31
5. A Population Diversity-Based Artificial Bee Colony Algorithm for Assembly Hybrid Flow Shop Scheduling with Energy Consumption;Applied Sciences;2023-09-30
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