Hazardous material transportation problems: A comprehensive overview of models and solution approaches
This paper provides a comprehensive review in the domain of hazardous material
transportation from an Operational Research point of view. The paper's focus lies on hazmat …
transportation from an Operational Research point of view. The paper's focus lies on hazmat …
Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem
This paper aims at integrating machine learning techniques into meta-heuristics for solving
combinatorial optimization problems. Specifically, our study develops a novel efficient …
combinatorial optimization problems. Specifically, our study develops a novel efficient …
Nondominated sorting genetic algorithm-II with Q-learning for the distributed permutation flowshop rescheduling problem
The distributed permutation flowshop problem (DPFSP) has been extensively studied in
recent years. However, most of the research has overlooked the disturbance factors in the …
recent years. However, most of the research has overlooked the disturbance factors in the …
A comparative study on genetic algorithm and reinforcement learning to solve the traveling salesman problem
A Uthayasuriyan, H Chandran, UV Kavvin… - Research Reports on …, 2023 - ojs.wiserpub.com
Abstract Machine Learning (ML) and Evolutionary Computing (EC) are the two most popular
computational methodologies in computer science to solve learning and optimization …
computational methodologies in computer science to solve learning and optimization …
A learning-based iterated local search algorithm for order batching and sequencing problems
L Zhou, C Lin, Q Ma, Z Cao - 2022 IEEE 18th International …, 2022 - ieeexplore.ieee.org
An order batching and sequencing problem in a warehouse is studied in this work. The
problem is proved to be an NP-hard problem. A mathematical programming model is …
problem is proved to be an NP-hard problem. A mathematical programming model is …
Hybridizing metaheuristics with machine learning for combinatorial optimization: a taxonomy and learning to select operators
MK Mamaghan - 2022 - theses.hal.science
This thesis integrates machine learning techniques into meta-heuristics for solving
combinatorial optimization problems. This integration aims to guide the meta-heuristics …
combinatorial optimization problems. This integration aims to guide the meta-heuristics …
A Fitness Approximation Assisted Hyper-heuristic for the Permutation Flowshop Problem
Hyper-heuristics can be applied to solve complex optimization problems. Recently, an
efficient hyper-heuristic (HHGA) was proposed for solving the permutation flowshop problem …
efficient hyper-heuristic (HHGA) was proposed for solving the permutation flowshop problem …
[PDF][PDF] Optimizing Optimization: Hyper-Heuristic Approaches for Generating Perturbative Operators through Operator Chaining
C Blom - 2024 - studenttheses.uu.nl
This thesis investigates the development of hyper-heuristics designed to generate
perturbative operators through operator chaining, with the objective of enhancing the …
perturbative operators through operator chaining, with the objective of enhancing the …