Machine learning for combinatorial optimization: a methodological tour d'horizon
This paper surveys the recent attempts, both from the machine learning and operations
research communities, at leveraging machine learning to solve combinatorial optimization …
research communities, at leveraging machine learning to solve combinatorial optimization …
Applications of machine learning methods in port operations–A systematic literature review
Ports are pivotal nodes in supply chain and transportation networks, in which most of the
existing data remain underutilized. Machine learning methods are versatile tools to utilize …
existing data remain underutilized. Machine learning methods are versatile tools to utilize …
Simulation-guided beam search for neural combinatorial optimization
Neural approaches for combinatorial optimization (CO) equip a learning mechanism to
discover powerful heuristics for solving complex real-world problems. While neural …
discover powerful heuristics for solving complex real-world problems. While neural …
Neural large neighborhood search for the capacitated vehicle routing problem
Learning how to automatically solve optimization problems has the potential to provide the
next big leap in optimization technology. The performance of automatically learned …
next big leap in optimization technology. The performance of automatically learned …
Efficient active search for combinatorial optimization problems
Recently numerous machine learning based methods for combinatorial optimization
problems have been proposed that learn to construct solutions in a sequential decision …
problems have been proposed that learn to construct solutions in a sequential decision …
A comprehensive review of quay crane scheduling, yard operations and integrations thereof in container terminals
Over the past decades, container transportation has achieved considerable growth, and
maritime trade now constitutes 80% of the global trade. The vessel sizes increased in …
maritime trade now constitutes 80% of the global trade. The vessel sizes increased in …
[HTML][HTML] The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions
Abstract Container Terminals (CTs) are continuously presented with highly interrelated,
complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in …
complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in …
[HTML][HTML] Optimization with constraint learning: A framework and survey
Many real-life optimization problems frequently contain one or more constraints or objectives
for which there are no explicit formulae. If however data on feasible and/or infeasible states …
for which there are no explicit formulae. If however data on feasible and/or infeasible states …
Machine learning for data-driven last-mile delivery optimization
In the context of the Amazon Last-Mile Routing Research Challenge, this paper presents a
machine-learning framework for optimizing last-mile delivery routes. Contrary to most routing …
machine-learning framework for optimizing last-mile delivery routes. Contrary to most routing …
Great partners: How deep learning and blockchain help improve business operations together
S Luo, TM Choi - Annals of Operations Research, 2024 - Springer
Business operations have entered the digital era in which artificial intelligence (AI), machine
learning (ML) and blockchain (BKC) have emerged as major disruptive forces. In AI and ML …
learning (ML) and blockchain (BKC) have emerged as major disruptive forces. In AI and ML …