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neural combinatorial optimization with reinforcement learning in industrial engineering: a survey

ARTIFICIAL INTELLIGENCE REVIEW 2025年第5期58卷 1-37页

作者： Chung, K. T. Lee, C. K. M. Tsang, Y. P. Hong Kong Polytech Univ Dept Ind & Syst Engn Hung Hom Hong Kong Peoples R China

In recent trends, machine learning is widely used to support decision-making in various domains and industrial operations. Because of the increasing complexity of modern industries, industrial engineering aims not only to increase cost-effectiveness and productivity but also to consider sustainability, resilience, and human centricity, resulting in many-objective, constrained, and stochastic operations research. Based on the above stringent requirements, combinatorial optimization (CO) problems are thus developed to support the complicated decision-making process in operations research. Due to the computational complexity of exact algorithms and the uncertain solution quality of heuristic methods, there is a growing trend to leverage the power of machine learning in solving CO problems, known as neural combinatorial optimization (NCO), where reinforcement learning (RL) is the core to achieve the sequential decision support. This survey study provides a comprehensive investigation of the theories and recent advancements in applying RL to solve hard CO problems, such as vehicle routing, bin packing, assignment, scheduling, and planning problems, and, in addition, summarizes the applications of neural combinatorial optimization with reinforcement learning (NCO-RL). The detailed review found that although the research domain of NCO-RL is still under-explored, its research potential has been proven to address environmental sustainability, adaptability, and human factors. Last but not least, the technical challenges and opportunities of the NCO-RL to embrace the industry 5.0 paradigm are discussed.

关键词： neural combinatorial optimization Reinforcement learning Industrial engineering Operations research Review

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neural combinatorial optimization: A tutorial

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Computers & Operations Research 2025年 182卷

作者： Davide Angioni Claudia Archetti M. Grazia Speranza Department of Economics and Management University of Brescia Italy Orobix Srl Bergamo Italy

Recent advancements in deep reinforcement learning have sparked a growing interest in the application of this approach to solve combinatorial optimization (CO) problems. This paper presents neural combinatorial optimization (NCO) as a framework for constructing functions that work as heuristics for CO problems. Given the rapid expansion of the field and the increasing interest in the topic, this tutorial introduces the main techniques utilized in NCO and explores the current open issues in the field. We define key terms and concepts related to NCO and present the latest developments, using the Knapsack Problem as a running example to complement theoretical explanations. Finally, we analyze prominent works in the field of NCO, with a focus on their application to the Traveling Salesman Problem, which serves as the most extensively studied problem in this domain.

关键词： neural combinatorial optimization Reinforcement learning Deep neural networks Traveling salesman problem

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neural combinatorial optimization for Production Scheduling with Sequence-Dependent Setup Waste

Neural Combinatorial Optimization for Production Scheduling ...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Gannouni, Aymen Samsonov, Vladimir Behery, Mohamed Meisen, Tobias Lakemeyer, Gerhard Rhein Westfal TH Aachen Inst Informat Management Mech Engn Aachen Germany Rhein Westfal TH Aachen Knowledge Based Syst Grp Aachen Germany Univ Wuppertal Chair Technol & Management Digital Transformat Wuppertal Germany

ISBN: (纸本)9781728185262

One of the main objectives of production planning is to minimize the usage of resources and manufacturing-related costs while meeting the customer's requirements, such as delivery dates and quality. Production planners deal with various scheduling problems that are often NP-hard and can not be optimally solved by humans. Solving such problems often relies on methods from the Operations Research (OR) field. Recently, neural combinatorial optimization (NCO) has emerged as a promising field of research that aims at tackling different optimization tasks using the latest advancements in machine learning, including deep reinforcement learning. These methods can be successfully used for short-term production planning because of their flexibility and speed. In this paper, we examine the applicability and scalability of neural combinatorial optimization methods in the context of production planning. We define an evaluation metric to investigate the stability and quality of the solutions. Furthermore, we develop an experimental setup allowing to compare various approaches for production scheduling with sequence-dependent setup costs under real-world production conditions. Although an optimality gap is observed when compared to established OR methods, our experiments demonstrate the superiority of NCO in terms of scheduling time.

关键词： neural combinatorial optimization Supervised Machine Learning Production Scheduling Deep Reinforcement Learning

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IS TRANSFER LEARNING HELPFUL FOR neural combinatorial optimization APPLIED TO VEHICLE ROUTING PROBLEMS?

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COMPUTING AND INFORMATICS 2022年第1期41卷 172-190页

作者： Yaddaden, Ali Harispe, Sebastien Vasquez, Michel Univ Montpellier IMT Mines Ales EaroMov Digital Hlth Mot Ales France

Recently, combinatorial optimization problems have aroused a great deal of interest in Machine Learning, leading to interesting advances in neural combinatorial optimization (NCO): the study of data-driven solvers for NP-Hard problems based on neural networks. This paper studies the benefit of Transfer Learning for NCO by evaluating how model training can be improved taking advantage of knowledge learned while solving similar tasks. We focus, in particular, on two famous routing problems: the Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP). The latter being a generalization of the former, we study the effect of applying Transfer Learning from a model trained to solve TSP while training a model learning to solve the Capacitated VRP (CVRP). We present adaptations of a state-of-the-art NCO model for implementing Transfer Learning. Our results based on extensive empirical experiments in different settings show that Transfer Learning may help to speed up the training process while being more sample efficient.

关键词： neural combinatorial optimization transfer learning vehicle routing problem traveling salesman problem

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Online Vehicle Routing With neural combinatorial optimization and Deep Reinforcement Learning

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第10期20卷 3806-3817页

作者： Yu, James J. Q. Yu, Wen Gu, Jiatao Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Natl Polytech Inst CINVESTAV IPN Dept Automat Control Mexico City 07360 DF Mexico Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

Online vehicle routing is an important task of the modern transportation service provider. Contributed by the ever-increasing real-time demand on the transportation system, especially small-parcel last-mile delivery requests, vehicle route generation is becoming more computationally complex than before. The existing routing algorithms are mostly based on mathematical programming, which requires huge computation time in city-size transportation networks. To develop routes with minimal time, in this paper, we propose a novel deep reinforcement learning-based neural combinatorial optimization strategy. Specifically, we transform the online routing problem to a vehicle tour generation problem, and propose a structural graph embedded pointer network to develop these tours iteratively. Furthermore, since constructing supervised training data for the neural network is impractical due to the high computation complexity, we propose a deep reinforcement learning mechanism with an unsupervised auxiliary network to train the model parameters. A multisampling scheme is also devised to further improve the system performance. Since the parameter training process is offline, the proposed strategy can achieve a superior online route generation speed. To assess the proposed strategy, we conduct comprehensive case studies with a real-world transportation network. The simulation results show that the proposed strategy can significantly outperform conventional strategies with limited computation time in both static and dynamic logistic systems. In addition, the influence of control parameters on the system performance is investigated.

关键词： Online vehicle routing logistic system neural combinatorial optimization deep reinforcement learning intelligent transportation

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A hybrid neural combinatorial optimization framework assisted by automated algorithm design

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COMPLEX & INTELLIGENT SYSTEMS 2024年第6期10卷 8233页

作者： Ma, Liang Hao, Xingxing Zhou, Wei He, Qianbao Zhang, Ruibang Chen, Li Northwest Univ Xian 710127 Peoples R China

In recent years, the application of neural combinatorial optimization (NCO) techniques in combinatorial optimization (CO) has emerged as a popular and promising research direction. Currently, there are mainly two types of NCO, namely, the Constructive neural combinatorial optimization (CNCO) and the Perturbative neural combinatorial optimization (PNCO). The CNCO generally trains an encoder-decoder model via supervised learning to construct solutions from scratch. It exhibits high speed in construction process, however, it lacks the ability for sustained optimization due to the one-shot mapping, which bounds its potential for application. Instead, the PNCO generally trains neural network models via deep reinforcement learning (DRL) to intelligently select appropriate human-designed heuristics to improve existing solutions. It can achieve high-quality solutions but at the cost of high computational demand. To leverage the strengths of both approaches, we propose to hybrid the CNCO and PNCO by designing a hybrid framework comprising two stages, in which the CNCO is the first stage and the PNCO is the second. Specifically, in the first stage, we utilize the attention model to generate preliminary solutions for given CO instances. In the second stage, we employ DRL to intelligently select and combine appropriate algorithmic components from improvement pool, perturbation pool, and prediction pool to continuously optimize the obtained solutions. Experimental results on synthetic and real Capacitated Vehicle Routing Problems (CVRPs) and Traveling Salesman Problems(TSPs) demonstrate the effectiveness of the proposed hybrid framework with the assistance of automated algorithm design.

关键词： neural combinatorial optimization Transformer Reinforcement learning Automated algorithm design

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Hamiltonian-based Quantum Reinforcement Learning for neural combinatorial optimization 5

Hamiltonian-based Quantum Reinforcement Learning for Neural ...

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2024 International Conference on Quantum Computing and Engineering

作者： Kruse, Georg Coelho, Rodrigo Rosskopf, Andreas Wille, Robert Lorenz, Jeanette Miriam Fraunhofer IISB Erlangen Germany Tech Univ Munich Munich Germany Fraunhofer IKS Munich Germany Ludwig Maximilians Univ Munchen Munich Germany

ISBN: (纸本)9798331541378

Advancements in Quantum Computing (QC) and neural combinatorial optimization (NCO) represent promising steps in tackling complex computational challenges. On the one hand, Variational Quantum Algorithms such as QAOA can be used to solve a wide range of combinatorial optimization problems. On the other hand, the same class of problems can be solved by NCO, a method that has shown promising results, particularly since the introduction of Graph neural Networks. Given recent advances in both research areas, we introduce Hamiltonian-based Quantum Reinforcement Learning (QRL), an approach at the intersection of QC and NCO. We model our ansatzes directly on the combinatorial optimization problem's Hamiltonian formulation, which allows us to apply our approach to a broad class of problems. Our ansatzes show favourable trainability properties when compared to the hardware efficient ansatzes, while also not being limited to graph-based problems, unlike previous approaches. In this work, we evaluate the performance of Hamiltonian-based QRL on a diverse set of combinatorial optimization problems to demonstrate the broad applicability of our approach and compare it to QAOA.

关键词： Quantum Reinforcement Learning combinatorial optimization neural combinatorial optimization

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sfc2cpu: Operating a Service Function Chain Platform with neural combinatorial optimization 17

sfc2cpu: Operating a Service Function Chain Platform with Ne...

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IFIP/IEEE International Symposium on Integrated Network Management (IM)

作者： Kraemer, Patrick Diederich, Philip Kraemer, Corinna Pries, Rastin Kellerer, Wolfgang Blenk, Andreas Tech Univ Munich Munich Germany Papierfabrik Louisenthal GmbH Schwabisch Gmund Germany Nokia Bell Labs Munich Germany

ISBN: (纸本)9783903176324

Service Function Chaining realized with a microservice based architecture results in an increased number of computationally cheap Virtual Network Functions (VNFs). Pinning cheap VNFs to dedicated CPU cores can waste resources since not every VNF fully utilizes its core. Thus, cheap VNFs should share CPU cores to improve resource utilization. sfc2cpu learns efficient VNF to core assignments that increase throughput and reduce latency compared to three baseline algorithms. To optimize VNF assignments, sfc2cpu uses game theory combined with neural combinatorial optimization in a novel way. Measurements in a real hardware testbed show that sfc2cpu increases throughput by up to 36% and reduces latency by up to 59% compared to Round Robin. We show that sfc2cpu can be incrementally deployed and easily integrated into existing infrastructures.

关键词： Reinforcement Learning Network Virtualization Game Theory neural combinatorial optimization Digital Twin

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A Novel Graph Representation Learning Approach for Visual Modeling Using neural combinatorial optimization 10th

A Novel Graph Representation Learning Approach for Visual Mo...

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10th Biennial International Conference on Pattern Recognition and Machine Intelligence (PReMI)

作者： Chatterjee, Subhrasankar Pain, Subrata Samanta, Debasis Indian Inst Technol Kharagpur 721302 India

ISBN: (纸本)9783031451690;9783031451706

The human visual system is a complex network of neurons that employs robust mechanisms to perceive and interpret the environment. Despite significant advancements in computer vision technologies in recent years, they still need to improve compared to human abilities, particularly in recognizing faces and interpreting scenes. As a result, there is a growing interest in understanding the underlying mechanisms of human vision. Artificial Intelligence (AI) systems, specifically computer vision models, represent the assigned task using a learned or systematically generated vector space known as the Latent Space. However, the field of brain representation space remains relatively less explored. Despite significant progress, a research gap exists in generating an optimal representation space for human visual processing. While graph-based representations have been proposed to better capture inter-region relationships in visual processing, learning an optimal graph representation from limited data remains a challenge, especially when there is no ground truth. Due to the lack of labeled data, supervised learning approaches are less preferred. The present study introduces a novel method for graph-based representation of the human visual processing system, utilizing neural combinatorial optimization(NCO). We have obtained an accuracy of 60% from our proposed framework, which is comparable to other methods for eight class classification in Visual Modelling.

关键词： Visual Modeling neural combinatorial optimization AutoEncoder

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Navigating Activity Cliffs with neural combinatorial optimization: A Graph-Based Roadmap for Drug Discovery 2

Navigating Activity Cliffs with Neural Combinatorial Optimiz...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Sorathiya, Vatsal Kumar, Mohit Saxena, Jai Garai, Baishali Saha, Jayita Dayananda Sagar University Department of CSE Bengaluru India Dayananda Sagar Universtiy Department of CSE Bengaluru India School of CSE RV University Bengaluru India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of CSE Manipal India

ISBN: (纸本)9798350383652

In the drug discovery field, Graph Edit Distance (GED) has emerged as a powerful tool for calculating Activity Cliff Analysis (ACA). ACA is a crucial tool in drug discovery that helps identify pairs of compounds with similar structure while demonstrating vastly different biological activities. However, traditional GED algorithms face limitations in handling large and intricate molecular graphs. This paper demonstrates a novel approach that utilizes neural combinatorial optimization (NCO) to optimize the GED algorithm, enabling it to efficiently calculate GED values, enhancing its performance. This proposed NCO-based GED algorithm is applied to activity cliff analysis, where it successfully identifies pairs of compounds with similar structures but different biological activities. This NCO framework offers a promising avenue for optimizing various computational algorithms in drug discovery and other domains. By successfully implementing this NCO-based approach, ACA and its impact on drug discovery could be revolutionised leading to faster, more accurate activity cliff identification, and ultimately, the potential for the creation of life-saving drugs. © 2024 IEEE.

关键词： Activity Cliff Analysis Graph Edit Distance neural combinatorial optimization Structure Activity Relationships

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