Article
Management
Federico Alonso-Pecina, David Romero, Marco Antonio Cruz-Chavez
Summary: In the label printing problem, the objective is to print a set of labels in specified quantities using predefined templates. Each template can hold a fixed number of printing plates. The problem involves determining the partition of labels, the number of identical printing plates for each label, and the number of imprints for each template. The proposed Iterated Local Search heuristic has shown improvements over existing results and has been able to find optimal solutions for known instances.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Mathematics
Jose Garcia, Jose Lemus-Romani, Francisco Altimiras, Broderick Crawford, Ricardo Soto, Marcelo Becerra-Rozas, Paola Moraga, Alex Paz Becerra, Alvaro Pena Fritz, Jose-Miguel Rubio, Gino Astorga
Summary: This article proposes a hybrid algorithm that integrates the k-means algorithm into a binary version of cuckoo search to solve the NP-hard Set-Union Knapsack Problem. Numerical experiments show that the hybrid algorithm consistently produces superior results in most medium instances, but its performance degrades in large instances.
Article
Computer Science, Artificial Intelligence
Yi Chu, Chuan Luo, Holger H. Hoos, Haihang You
Summary: The maximum vertex weight clique problem (MVWCP) is a generalization of the maximum clique problem (MCP) with wide real-world applications. Stochastic local search (SLS) algorithms are commonly used to solve MVWCP when optimality guarantees are not required. However, finding the most suitable algorithm for each class of MVWCP instances is challenging. In this study, a new SLS framework based on Programming by Optimization (PbO) is developed, which achieves significant advancements in solving MVWCP.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Yuji Zou, Jin-Kao Hao, Qinghua Wu
Summary: This article presents an effective heuristic algorithm for the traveling salesman problem with job-times. The algorithm uses a breakout local search method to find high-quality local optimal solutions and incorporates a perturbation procedure to escape local optimum traps. Computational results show that the algorithm outperforms previous methods on benchmark instances.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Pengfei He, Jin-Kao Hao
Summary: The Colored Traveling Salesmen Problem (CTSP) is a generalization of the popular Traveling Salesman Problem, involving multiple salesmen; The goal is to determine the shortest Hamiltonian circuit for each salesman, satisfying specific conditions; It is known to be computationally challenging, and a solution has been proposed.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Esra Duygu Durmaz, Ramazan Sahin
Summary: A new and comprehensive multi-start iterated local search algorithm is developed for the corridor allocation problem, which utilizes variable neighborhood descent for local search to search the solution space effectively.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Mathematics
Alejandro Lara-Caballero, Diego Gonzalez-Moreno
Summary: The problem of identifying code for a given graph involves finding a minimum subset of vertices that uniquely specifies each vertex based on its nonempty neighborhood. This combinatorial optimization problem has various applications in location and detection schemes. Finding a minimum identifying code is computationally intractable, hence the use of heuristics. This work presents a new population-based local search algorithm that generates high-quality solutions in different types of graphs.
Article
Computer Science, Artificial Intelligence
Jose Garcia, Carlos Maureira
Summary: In this work, a hybrid algorithm incorporating the k-nearest neighbor technique was evaluated to enhance the results of a quantum cuckoo search algorithm for resource allocation. Experimental results demonstrate the significant contribution of the k-nearest neighbor technique to the final solutions, showing that the hybrid algorithm consistently outperforms state-of-the-art algorithms in most analyzed instances.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Leslie Perez Caceres, Ignacio Araya, Guillermo Cabrera-Guerrero
Summary: This paper discusses the treatment plan generation issue in Intensity Modulated Radiation Therapy (IMRT) and proposes a stochastic local search algorithm to solve the Direct Aperture Optimisation (DAO) problem. By alternating the search between two neighborhood definitions, the algorithm is capable of finding deliverable and clinically acceptable treatment plans.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Dangdang Niu, Bin Liu, Hongming Zhang, Minghao Yin
Summary: This paper presents a branch-and-bound algorithm BABWSCP for solving the weighted sum coloring problem (WSCP), as well as a method BABLS for improving local search. Experimental results demonstrate the effectiveness of these methods in benchmark tests.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Dangdang Niu, Bin Liu, Minghao Yin, Yupeng Zhou
Summary: In this paper, an efficient local search algorithm named LSGCR_DTP is proposed to solve the dominating tree problem (DTP). The algorithm incorporates four new strategies to improve its efficiency and outperform existing DTP solvers.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Liwen Li, Zequn Wei, Jin-Kao Hao, Kun He
Summary: The Budgeted Maximum Coverage Problem (BMCP) is a challenging NP-hard problem that involves budget constraints and profit maximization, requiring iteration through tabu search and probability learning perturbation phases. Researchers propose an effective algorithm through case studies and comparative analysis.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jintong Ren, Jin-Kao Hao, Feng Wu, Zhang-Hua Fu
Summary: In this study, an intensification-driven local search algorithm is proposed to solve the Traveling Repairman Problem with Profits. By intensively investigating the areas around very-high-quality local optima, the algorithm obtains high-quality solutions. Experimental results show that the algorithm performs well by improving 36 best-known results and achieving equal best-known results for 95 instances out of 140 benchmark instances.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Civil
Xinyu Wang, Shuai Shao, Jiafu Tang
Summary: This study introduces mathematical WVRP models and proposes an efficient heuristic method RI-ILS to solve the problem. RI-ILS method performs well in computational experiments, producing good results for both traditional vehicle routing problems and WVRPs.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Bruno Nogueira, Eduardo Tavares, Paulo Maciel
Summary: This paper presents an iterated local search heuristic for solving the weighted vertex coloring problem. The heuristic outperforms other methods in terms of solution quality and computational time, making it a good alternative for large instances that cannot be solved by exact methods.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Engineering, Industrial
Bing Chen, Ruibin Bai, Jiawei Li, Yueni Liu, Ning Xue, Jianfeng Ren
Summary: This paper discusses the current situation and limitations of current methods for addressing uncertainties in real-life optimization problems. The authors propose a novel framework that combines mathematical models and machine learning modules to overcome these limitations and demonstrate its practicality and feasibility through real-life and artificial bus scheduling instances. The proposed framework represents the first multi-objective bus-headway-optimisation method for non-timetabled bus schedules with major practical constraints being considered.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Management
Yuchang Zhang, Ruibin Bai, Rong Qu, Chaofan Tu, Jiahuan Jin
Summary: This paper presents the advancements in computational intelligence and operations research and identifies the limitations of current optimization methods in dealing with uncertainties. To address this research gap, a deep reinforcement learning based hyper-heuristic framework is proposed. Experimental results demonstrate the superior performance of this framework in solving real-world optimization problems.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Management
Xiaoping Jiang, Ruibin Bai, Jianfeng Ren, Jiawei Li, Graham Kendall
Summary: This paper uses the Lagrange dual problem to compute lower bounds for stochastic service network design, showing the superiority of the resulting optimal Lagrange dual bound. By employing an improved algorithm, the computing efficiency is enhanced, as demonstrated in computational experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Engineering, Industrial
Ruibin Bai, Xinan Chen, Zhi-Long Chen, Tianxiang Cui, Shuhui Gong, Wentao He, Xiaoping Jiang, Huan Jin, Jiahuan Jin, Graham Kendall, Jiawei Li, Zheng Lu, Jianfeng Ren, Paul Weng, Ning Xue, Huayan Zhang
Summary: This paper provides a comprehensive review of hybrid methods that combine machine learning techniques with analytical approaches to address the Vehicle Routing Problem (VRP). The review highlights the potential benefits of using machine learning in enhancing VRP modeling and improving the performance of VRP optimization algorithms, both in online and offline scenarios.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Multidisciplinary Sciences
Bingchen Lin, Jiawei Li, Ruibin Bai, Rong Qu, Tianxiang Cui, Huan Jin
Summary: This article introduces a solution to the online bin packing problem - pattern-based adaptive heuristics, which improves the efficiency of packing by predicting the distribution of items and incorporating their characteristics.
Article
Mathematics
Jiawei Li, Tianxiang Cui, Graham Kendall
Summary: This study examines the uniqueness of bargaining equilibrium between two sellers and two buyers, and finds that the equilibrium is reached when the division of two pies is equal. This result can be extended to bargaining games with n-sellers and n-buyers, providing insights into the general equilibrium of a market.
Article
Business, Finance
Shusheng Ding, Tianxiang Cui, Yongmin Zhang
Summary: Future markets play vital roles in supporting economic activities, and volatility forecasting in futures markets has gained increasing attention in financial research. This study utilizes big data analytics to improve the accuracy of volatility forecasting in futures markets and demonstrates the application of big data analytics in the financial spectrum. The empirical results indicate that the XGBoost method outperforms other models in terms of volatility forecasting accuracy.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2022)
Editorial Material
Engineering, Industrial
Ruibin Bai, Zhi-Long Chen, Graham Kendall
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Business, Finance
Shusheng Ding, Tianxiang Cui, Xiangling Wu, Min Du
Summary: This paper investigates the optimization of production plans in a supply chain based on a Central Bank Digital Currency (CBDC). By applying a volatility clustering model and a machine learning model, the authors reveal the impact of CBDC uncertainty and find that the machine learning model outperforms the GARCH model in prediction. The results suggest that the performance of manufacturing companies can be strengthened by reducing CBDC uncertainty.
RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE
(2022)
Article
Computer Science, Artificial Intelligence
Shihe Wang, Jianfeng Ren, Ruibin Bai
Summary: Recently, improved naive Bayes methods, including regularized naive Bayes (RNB), have been developed to enhance discrimination capabilities. However, these methods often result in significant information loss due to inadequate data discretization. To address this issue, we propose a semi-supervised adaptive discriminative discretization framework that utilizes both labeled and unlabeled data to better estimate the data distribution. Our proposed method, called RNB+, shows superior performance compared to state-of-the-art NB classifiers by significantly reducing information loss and improving discrimination power.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Chenglin Yao, Jianfeng Ren, Ruibin Bai, Heshan Du, Jiang Liu, Xudong Jiang
Summary: Detecting 3D mask attacks to a face recognition system is challenging due to unstable face alignment and weak rPPG signals. To address these issues, a landmark-anchored face stitching method and a weighted spatial-temporal representation of rPPG signals are proposed. A lightweight EfficientNet with a GRU is designed to extract features for classification. The proposed method outperforms other state-of-the-art rPPG-based methods for face spoofing detection.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Shihe Wang, Jianfeng Ren, Xiaoyu Lian, Ruibin Bai, Xudong Jiang
Summary: In this paper, a feature augmentation method using a stack auto-encoder is proposed to enhance the performance of naive Bayes by reducing noise in the data and boosting the discriminant power of features. Experimental results show that the proposed method consistently outperforms state-of-the-art naive Bayes classifiers.
2022 26TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Xinyang Du, Ruibin Bai, Tianxiang Cui, Rong Qu, Jiawei Li
Summary: The competitive traveling salesmen problem is a complex decision-making issue that lacks effective algorithms. This research explores an enhanced ant colony approach with a time dominance mechanism and revised pheromone depositing method to improve solution quality with reduced computational complexity. Simulation results demonstrate that the proposed algorithm surpasses current state-of-the-art algorithms.
2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2022)
Article
Engineering, Aerospace
Ning Zhou, Lawrence Lau, Ruibin Bai, Terry Moore
Summary: This paper proposes a robust particle-filter-based indoor positioning algorithm to mitigate the effects of delayed range measurements and improve positioning accuracy. The algorithm includes an outlier detection method for delayed measurement identification and a constrained particle sampling method to optimize the distribution of predicted particles.
NAVIGATION-JOURNAL OF THE INSTITUTE OF NAVIGATION
(2022)
