Article
Computer Science, Artificial Intelligence
Zhenzhong Wang, Kai Ye, Min Jiang, Junfeng Yao, Neal N. Xiong, Gary G. Yen
Summary: This study proposes a framework to reuse knee points in a new environment to address the Dynamic Vehicle Routing Problem based on Hybrid Charging Strategy. Reusing knee points helps generate a better initial population and brings convenience to decision makers.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Automation & Control Systems
Yushuang Hou, Hongfeng Wang, Xiaoliang Huang
Summary: Integrated production and distribution scheduling (IPDS) has gained attention in recent years. This study introduces a green distributed production and distribution scheduling problem and proposes a multi-objective evolutionary algorithm to solve it. The experimental results show that the algorithm has clear advantages in reducing total tardiness and carbon emissions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Computer Science, Artificial Intelligence
Derya Deliktas, Ender Ozcan, Ozden Ustun, Orhan Torkul
Summary: The study introduces evolutionary algorithms to solve the bi-objective flexible job shop scheduling problem and compares their performance across various configurations. The transgenerational memetic algorithm using weighted sum method outperforms others and achieves the best-known results for almost all instances of bi-objective flexible job shop cell scheduling.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Feng Wang, Fanshu Liao, Yixuan Li, Xuesong Yan, Xu Chen
Summary: This paper proposes a new algorithm EL-DMOEA for solving the Dynamic Vehicle Routing Problem with Time Window, using ensemble learning method to improve algorithm performance. Multiple strategies are employed during training process to enhance population diversity and accelerate convergence, with experimental results showing promising routing plans can be effectively developed by the proposed algorithm.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Automation & Control Systems
Sheng-Long Jiang, Qie Liu, I. David L. Bogle, Zhong Zheng
Summary: Scheduling is crucial in steelmaking manufacturing systems. This study introduces a resilient scheduling model that allows for flexible decisions and quick recovery from random disturbances in steelmaking plants. A dynamic multi-objective optimization problem (DMOP) is formulated and a resilient scheduling optimization framework is proposed to solve it. Experimental evidence confirms the effectiveness of the proposed model and framework in solving dynamic scheduling problems in steelmaking plants.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Xiaoshu Xiang, Ye Tian, Ran Cheng, Xingyi Zhang, Shengxiang Yang, Yaochu Jin
Summary: This study proposes a benchmark generator for online dynamic single-objective and multi-objective optimization problems. It adjusts the influence of solutions found in each environment on the problems in the next environment through different types of functions and predefined parameters, and suggests a test suite consisting of continuous and discrete online dynamic optimization problems. The proposed OL-DOP test suite exhibits time-deception compared to existing benchmark test suites and evaluates the ability of dynamic optimization algorithms to tackle the influence of solutions on successive environment problems.
INFORMATION SCIENCES
(2022)
Article
Construction & Building Technology
Nilotpal Chakraborty, Arijit Mondal, Samrat Mondal
Summary: This paper addresses the problem of charge scheduling and route management for electric vehicles, proposing an intelligent heuristic mechanism and formulating it as a multi-objective optimization problem, with a graph-based algorithm to quickly obtain solutions. The results show that the energy-aware-MoHA variant performs better in minimizing energy consumption compared to the time-aware-MoHA.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Automation & Control Systems
Su Nguyen, Mengjie Zhang, Damminda Alahakoon, Kay Chen Tan
Summary: A novel people-centric evolutionary system for dynamic production scheduling has been developed with new techniques and models to enhance the efficiency of genetic programming, outperforming existing algorithms in dynamic flexible job shop scheduling experiments.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Yoram Clapper, Joost Berkhout, Rene Bekker, Dennis Moeke
Summary: This paper presents a model-based evolutionary algorithm for the Home Health Care Routing and Scheduling Problem (HHCRSP). The algorithm generates routes with care activities and shift schedules, considering qualification levels. Performance is optimized in terms of travel time, time window waiting time, and shift overtime. Numerical experiments using real-life data show close-to-optimal performance for small instances and a 41% efficiency gain compared to a case study. Furthermore, the model-based evolutionary algorithm outperforms a traditional evolutionary algorithm, emphasizing the importance of learning and exploiting a model in HHCRSP optimization.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Thermodynamics
Mohammed Alqahtani, Mengqi Hu
Summary: This paper proposes a reinforcement learning model that utilizes electric vehicles to supply energy and addresses uncertainties in power supply and demand. The simulation results demonstrate that the model can reduce energy costs.
Article
Automation & Control Systems
Jianhua Xiao, Tao Zhang, Jingguo Du, Xingyi Zhang
Summary: This article proposes a heuristic algorithm, EMRG-HA, to tackle large-scale vehicle routing problems. By utilizing the divide and conquer framework and evolutionary multiobjective route grouping method, the algorithm shows superior performance in solving large-scale CVRPs and outperforms eight existing algorithms in terms of both computational efficiency and solution quality in experimental evaluations.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Xiangsong Kong, Yongkuan Yang, Zhisheng Lv, Jing Zhao, Rong Fu
Summary: This paper proposes a dynamic dual-population co-evolution multi-objective evolutionary algorithm (DDCMEA) to address the issue of balancing feasibility, convergence, and diversity in constrained multi-objective optimization problems. DDCMEA employs a dynamic dual-population co-evolution strategy to balance convergence and feasibility by adjusting the offspring number of the two populations. In the early stage, the algorithm focuses on convergence and generates more offspring of the first population, while in the late stage, it focuses on feasibility and generates more offspring of the second population. The results show that DDCMEA achieves competitive performance in handling constrained multi-objective optimization problems.
APPLIED SOFT COMPUTING
(2023)
Article
Operations Research & Management Science
Alain Quilliot, Antoine Sarbinowski, Helene Toussaint
Summary: This study addressed a non preemptive version of managing a one-way vehicle sharing system with strong makespan restrictions, aiming to link static and online paradigms based on a vehicle-driven approach that puts vehicle routing strategies at the core of decision-making process.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Chao Wang, Biao Ma, Jiye Sun
Summary: This paper proposes a co-evolutionary genetic algorithm with knowledge transfer to solve the CVRP problem with workload balancing. Experimental results show that the algorithm has faster convergence speed and superior convergence.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Jinhua Zheng, Qishuang Wu, Juan Zou, Shengxiang Yang, Yaru Hu
Summary: Responding quickly to environmental changes is crucial in solving dynamic multi-objective optimization problems (DMOPs). Most existing methods perform well on predicting individuals but struggle with improving the accuracy of the predicted population. This paper proposes an approach called RVCP, which combines an adjusted reference vector with a multi-objective evolutionary algorithm to predict the population and effectively tackle DMOPs.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Multidisciplinary
Gozde Alp, Ali Fuat Alkaya
Summary: This paper introduces a novel hybrid computational intelligence algorithm HHMBO for solving the fairness oriented integrated shift scheduling problem FOSSP of a manufacturing company. Experimental results demonstrate that HHMBO performs well for large sized instances of the specific problem, with high exploration capability, making it a promising technique for all optimization problems.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Rafael Praxedes, Teobaldo Bulhoes, Anand Subramanian, Eduardo Uchoa
Summary: The Vehicle Routing Problem with Simultaneous Pickup and Delivery is a classical optimization problem that aims to determine the least-cost routes while meeting pickup and delivery demands and vehicle capacity constraints. In this study, a unified algorithm is proposed to solve multiple variants of the problem, and extensive computational experiments are conducted to evaluate the algorithm's performance.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ragheb Rahmaniani, Teodor Gabriel Crainic, Michel Gendreau, Walter Rei
Summary: Benders decomposition (BD) is a popular solution algorithm for stochastic integer programs. However, existing parallelization methods often suffer from inefficiencies. This paper proposes an asynchronous parallel BD method and demonstrates its effectiveness through numerical studies and performance enhancement strategies.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Giulia Caselli, Maxence Delorme, Manuel Iori, Carlo Alberto Magni
Summary: This study addresses a real-world scheduling problem and proposes four exact methods to solve it. The methods are evaluated through computational experiments on different types of instances and show competitive advantages on specific subsets. The study also demonstrates the generalizability of the algorithms to related scheduling problems with contiguity constraints.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaowen Yao, Chao Tang, Hao Zhang, Songhuan Wu, Lijun Wei, Qiang Liu
Summary: This paper examines the problem of two-dimensional irregular multiple-size bin packing and proposes a solution that utilizes an iteratively doubling binary search algorithm to find the optimal bin combination, and further optimizes the result through an overlap minimization approach.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Decheng Wang, Ruiyou Zhang, Bin Qiu, Wenpeng Chen, Xiaolan Xie
Summary: Consideration of driver-related constraints, such as mandatory work break, in vehicle scheduling and routing is crucial for safety driving and protecting the interests of drivers. This paper addresses the drop-and-pull container drayage problem with flexible assignment of work break, proposing a mixed-integer programming model and an algorithm for solving realistic-sized instances. Experimental results show the effectiveness of the proposed algorithm in handling vehicle scheduling and routing with work break assignment.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
William N. Caballero, Jose Manuel Camacho, Tahir Ekin, Roi Naveiro
Summary: This research provides a novel probabilistic perspective on the manipulation of hidden Markov model inferences through corrupted data, highlighting the weaknesses of such models under adversarial activity and emphasizing the need for robustification techniques to ensure their security.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Davood Zaman Farsa, Shahryar Rahnamayan, Azam Asilian Bidgoli, H. R. Tizhoosh
Summary: This paper proposes a multi-objective evolutionary framework for compressing feature vectors using deep autoencoders. The framework achieves high classification accuracy and efficient image representation through a bi-level optimization scheme. Experimental results demonstrate the effectiveness and efficiency of the proposed framework in image processing tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Matthew E. Scherer, Raymond R. Hill, Brian J. Lunday, Bruce A. Cox, Edward D. White
Summary: This paper discusses instance generation methods for the multidemand multidimensional knapsack problem and introduces a primal problem instance generator (PPIG) to address feasibility issues in current instance generation methods.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Yin Yuan, Shukai Li, Lixing Yang, Ziyou Gao
Summary: This paper investigates the design of real-time train regulation strategies for urban rail networks to reduce train deviations and passenger waiting times. A mixed-integer nonlinear programming (MINLP) model is used and an efficient iterative optimization (IO) approach is proposed to address the complexity. The generalized Benders decomposition (GBD) technique is also incorporated. Numerical experiments show the effectiveness and computational efficiency of the proposed method.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xinghai Guo, Netirith Narthsirinth, Weidan Zhang, Yuzhen Hu
Summary: This study proposes a bi-level scheduling method that utilizes unmanned surface vehicles for container transportation. By formulating mission decision and path control models, efficient container transshipment and path planning are achieved. Experimental results demonstrate the effectiveness of the proposed approach in guiding unmanned surface vehicles to complete container transshipment tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Review
Computer Science, Interdisciplinary Applications
Jose-Fernando Camacho-Vallejo, Carlos Corpus, Juan G. Villegas
Summary: This study aims to review the published papers on implementing metaheuristics for solving bilevel problems and performs a bibliometric analysis to track the evolution of this topic. The study provides a detailed description of the components of the proposed metaheuristics and analyzes the common combinations of these components. Additionally, the study provides a detailed classification of how crucial bilevel aspects of the problem are handled in the metaheuristics, along with a discussion of interesting findings.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xudong Diao, Meng Qiu, Gangyan Xu
Summary: In this study, an optimization model for the design of an electric vehicle-based express service network is proposed, considering limited recharging resources and power management. The proposed method is validated through computational experiments on realistic instances.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ramon Piedra-de-la-Cuadra, Francisco A. Ortega
Summary: This study proposes a procedure to select candidate sites optimally for ensuring energy autonomy and reinforced service coverage for electric vehicles, while considering demand and budget restrictions.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Danny Blom, Christopher Hojny, Bart Smeulders
Summary: This paper focuses on a robust variant of the kidney exchange program problem with recourse, and proposes a cutting plane method for solving the attacker-defender subproblem. The results show a significant improvement in running time compared to the state-of-the-art, and the method can solve previously unsolved instances. Additionally, a new practical policy for recourse is proposed and its tractability for small to mid-size kidney exchange programs is demonstrated.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Anqi Li, Congying Han, Tiande Guo, Bonan Li
Summary: This study proposes a general framework for designing linear programming instances based on the preset optimal solution, and validates the effectiveness of the framework through experiments.
COMPUTERS & OPERATIONS RESEARCH
(2024)
