Article
Management
Jake Weiner, Andreas T. Ernst, Xiaodong Li, Yuan Sun, Kalyanmoy Deb
Summary: This paper introduces a new heuristic algorithm, LaPSO, which combines Lagrangian Relaxation and Particle Swarm Optimization, and incorporates a new repair heuristic called Largest Violation Perturbation (LVP) to solve the Maximum Edge Disjoint Paths (MEDP) problem. LaPSO outperforms both state-of-the-art heuristic methods and standard MIP solvers, demonstrating superior heuristic solutions and strong bounds within limited runtimes.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Douglas M. Miranda, Ricardo S. de Camargo, Samuel V. Conceicao, Marcelo F. Porto, Nilson T. R. Nunes
Summary: This paper discusses the school bus routing problem with bell adjustments, utilizing different strategies specifically designed for rural areas. The use of a memetic algorithm combining various search techniques resulted in significant cost savings, particularly for instances with fewer vehicles and more schools. Overall, the new strategy achieved up to 9% savings and 2.55% savings on consolidated results, showcasing its effectiveness in solving large scale instances.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Management
Pamela C. Nolz, Nabil Absi, Dominique Feillet, Clovis Seragiotto
Summary: This paper addresses a consistent vehicle routing problem for the delivery of parcels using electric vehicles. The problem considers the constraint that vehicles can only be charged between delivery and pickup tours, and aims to generate efficient vehicle routes while optimizing multiple objectives.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Management
Laura Calvet, Sergio Benito, Angel A. Juan, Ferran Prados
Summary: This paper discusses the application of metaheuristic algorithms in solving optimization problems in the field of bioinformatics, focusing on molecular docking, protein structure prediction, phylogenetic inference, and string problems.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2023)
Article
Multidisciplinary Sciences
Daisuke Inoue, Akihisa Okada, Tadayoshi Matsumori, Kazuyuki Aihara, Hiroaki Yoshida
Summary: This study develops a global traffic signal control method using a quantum annealing machine, showing its superiority in suppressing traffic imbalance and obtaining better solutions under certain conditions. The results also demonstrate the convergence of local and global control methods under specific probabilities.
SCIENTIFIC REPORTS
(2021)
Article
Computer Science, Information Systems
Zhi Lu, Jin-Kao Hao, Una Benlic, David Lesaint
Summary: This paper introduces an iterated multilevel simulated annealing algorithm for large-scale graph conductance minimization, demonstrating high performance on very large real-world sparse graphs, with publicly available source code.
INFORMATION SCIENCES
(2021)
Review
Management
Rafael Marti, Anna Martinez-Gavara, Sergio Perez-Pelo, Jesus Sanchez-Oro
Summary: This paper focuses on the problem of selecting a subset of elements from a given set in order to maximize the distance among the selected elements. The milestones in the development of this area are reviewed, and the connection and challenges among different models are analyzed. The benchmark instances are also revised and extended, and the best and more recently proposed procedures are empirically reviewed and compared to identify the state-of-the-art methods for the main diversity models.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Pengfei Sun, Chuanxin Zhang, Bo Jin, Qingyuan Wang, Haoran Geng
Summary: Timetable optimization is an effective solution to achieve energy-saving in urban rail transit. Previous studies neglected the energy exchange and line losses in the traction network, focusing only on the transfer and utilization of mechanical energy. This paper proposes a cross-substation energy transmission and utilization model considering the characteristics of actual traction power supply systems. A method for increasing the utilization of regenerative braking energy and reducing energy supply by compensating the current generated by accelerating and braking trains is presented. By adjusting the dwell time, the compensation current can be maximized, resulting in energy-saving. A mixed-integer linear programming model is constructed to linearize the highly nonlinear dynamic characteristics of the traction power supply systems, with the utilization of regenerative braking energy as the objective and dwell time as the variable. Numerical examples based on Beijing Batong Line verify the effectiveness of the method, showing significant improvement in energy efficiency compared to maximizing the overlap time between accelerating and braking trains.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Mathematics
Samuel Reong, Hui-Ming Wee, Yu-Lin Hsiao
Summary: This study uses bibliometric analysis to examine the scientific evolution of particle swarm optimization (PSO) for the vehicle routing problem (VRP) over the past 20 years. The findings of this study can guide future VRP research and underscore the importance of developing effective PSO metaheuristics.
Article
Computer Science, Artificial Intelligence
Farhad Soleimanian Gharehchopogh
Summary: Metaheuristic algorithms are efficient solutions for optimization problems, and quantum-inspired metaheuristic algorithms, which integrate quantum computing concepts, can achieve better results in solving complex optimization problems. Quantum computing plays a crucial role in enhancing the performance of metaheuristic algorithms.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Zhi Lu, Anna Martinez-Gavara, Jin-Kao Hao, Xiangjing Lai
Summary: This study addresses the capacitated dispersion problem in a weighted graph and proposes an effective and parameter-free heuristic algorithm based on solution-based tabu search. The algorithm employs a fast greedy construction heuristic and utilizes hash functions to identify eligible candidate solutions.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Atiq W. Siddiqui, Syed Arshad Raza
Summary: Timetabling is a common managerial issue with solution challenges that worsen with increasing problem size. Current heuristic methods face issues, prompting the proposal of a two-stage solution approach based on an ontology to address the problem.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Civil
D. Martinez-Munoz, J. Garcia, J. Marti, V Yepes
Summary: This study proposed a hybrid algorithm integrating k-means with swarm intelligence metaheuristics to optimize a box-girder steel-concrete composite bridge, achieving better results compared to other algorithms.
ENGINEERING STRUCTURES
(2022)
Article
Engineering, Civil
D. Martinez-Munoz, J. Garcia, J. Marti, V Yepes
Summary: This study proposes a hybrid algorithm that integrates the unsupervised learning technique of k-means with continuous swarm intelligence metaheuristics to optimize composite bridges. The results show that the hybrid proposal outperforms different algorithms designed.
ENGINEERING STRUCTURES
(2022)
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.
Review
Management
Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Philipp Schulze, Armin Scholl, Rico Walter
Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Stefano Nasini, Rabia Nessah
Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Bjorn Bokelmann, Stefan Lessmann
Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Congzheng Liu, Wenqi Zhu
Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Laszlo Csato
Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)