Article
Transportation Science & Technology
Nicolas Cabrera, Jean-Francois Cordeau, Jorge E. Mendoza
Summary: The park-and-loop routing problem is a variation of the vehicle routing problem that requires scheduling and optimization between vehicle and walking. In this study, we propose an exact solution method based on the branch-price-and-cut framework, and comparative experiments show that our method performs well in terms of solution quality and computational time efficiency.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Management
Artur Alves Pessoa, Michael Poss, Ruslan Sadykov, Francois Vanderbeck
Summary: The paper investigates the robust counterpart of the capacitated vehicle routing problem, introducing new techniques to improve algorithm efficiency and proposing a heuristic algorithm for problem-solving. Utilizing modern techniques and problem reformulation, the paper successfully solves almost all open instances.
OPERATIONS RESEARCH
(2021)
Article
Transportation Science & Technology
Wenqi Gao, Zhixing Luo, Houcai Shen
Summary: In this study, an exact branch-and-price-and-cut algorithm is proposed to solve the time-dependent pollution routing problem. The algorithm tackles the route selection problem and the time-dependent elementary shortest path problem to find the optimal solution. Compared to a commercial solver, the algorithm shows better performance and finds optimal solutions for more instances in less time.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Engineering, Industrial
Ece Naz Duman, Duygu Tas, Bulent Catay
Summary: This paper addresses the electric vehicle routing problem with time windows and proposes two methods based on a column generation algorithm. Experimental results indicate that the heuristic algorithm outperforms the exact algorithm in terms of computational time and number of instances solved, especially for larger instances. Both algorithms introduce new solutions to the literature.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Engineering, Multidisciplinary
Akang Wang, Anirudh Subramanyam, Chrysanthos E. Gounaris
Summary: This paper discusses the integration of branch-price-and-cut solvers with the robust optimization paradigm to address parametric uncertainty in vehicle routing problems. It proposes a novel approach that combines cutting-plane techniques with an advanced branch-price-and-cut algorithm to ensure complete robust feasibility against demand and travel time uncertainty. This approach is both modular and versatile, allowing the use of advanced branch-price-and-cut technologies without major modifications.
OPTIMIZATION AND ENGINEERING
(2022)
Article
Operations Research & Management Science
Guillaume Marques, Ruslan Sadykov, Remy Dupas, Jean-Christophe Deschamps
Summary: This paper studies the two-echelon capacitated vehicle routing problem with time windows and proposes a branch-cut-and-price algorithm to efficiently solve the problem. The experimental results show that the algorithm outperforms other methods in terms of computational efficiency.
TRANSPORTATION SCIENCE
(2022)
Article
Operations Research & Management Science
Xiangyi Zhang, Lu Chen, Michel Gendreau, Andre Langevin
Summary: This study proposes a method for solving the vehicle routing problem with two-dimensional loading constraints. By improving the data structure and dominance rule, and strengthening the linear relaxation, the method outperforms existing methods in solving instances with large rectangular items and achieves optimal solutions for 14 instances for the first time.
TRANSPORTATION SCIENCE
(2022)
Article
Management
Alexandre M. Florio, Michel Gendreau, Richard F. Hartl, Stefan Minner, Thibaut Vidal
Summary: This paper examines the stochastic variant of the Vehicle Routing Problem (VRP) called VRPSD, where demands are only revealed upon vehicle arrival at each customer. The paper summarizes recent progress in VRPSD research and introduces two major contributions: a branch-price-and-cut algorithm for optimal restocking and a demand model for correlated customer demands. Computational results demonstrate the effectiveness of the new algorithm and the potential cost savings of over 10% when considering demand correlation.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Management
Jorgen Skalnes, Henrik Andersson, Guy Desaulniers, Magnus Stalhane
Summary: This paper discusses the classic Inventory Routing Problem (IRP) and proposes a branch-and-cut algorithm based on a new mathematical formulation. The algorithm improves the lower bounds by using a convex combination of extreme points, called customer schedules, to deal with time-varying demands.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Hongyuan Luo, Mahjoub Dridi, Olivier Grunder
Summary: This paper investigates a time-dependent green vehicle routing problem (TDGVRP) considering traffic congestion. The objective is to design a vehicle scheduling plan that reduces carbon emissions, which are directly related to vehicle fuel consumption. To mathematically model traffic congestion, the study considers time-dependent travel speed, resulting in time-dependent travel time and carbon emissions. A set partitioning formulation (SPF) and a branch-price-and-cut (BPC) algorithm, incorporating a tailored labeling algorithm for the pricing problem, are proposed. Valid inequalities are also used to strengthen the SPF, leading to a more accurate lower bound. Extensive computational experiments demonstrate the effectiveness of the proposed BPC algorithm. This study contributes to the theoretical research on vehicle routing problems (VRPs) and provides a mathematical and reasonable method for logistics companies to formulate logistics plans considering fuel consumption, climate change, and carbon emissions reduction.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Economics
Menglei Jia, Feng Chen
Summary: Motivated by a famous automobile manufacturing company, this study addresses the upward scalable vehicle routing problem with time windows (USVRPTW) in the context of inbound logistics. The researchers propose a branch-and-price algorithm to solve the problem exactly and design a tree search algorithm based on the consideration of resource allocation. They also develop a heuristic algorithm for generating initial columns in the column generation process. Numerical experiments demonstrate the superiority of their algorithm compared to a commercial solver. Additionally, real-data experiments show that a slight increase in driving cost can greatly improve vehicle utilization, highlighting the significance of flexibility. The study provides management insights on reducing logistics cost through adopting the proposed flexibility mechanism.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2023)
Article
Computer Science, Interdisciplinary Applications
Xiaochang Liu, Dujuan Wang, Yunqiang Yin, T. C. E. Cheng
Summary: In this study, we consider the pickup and delivery problem with time windows involving battery-powered electric vehicles under demand uncertainty. We develop a two-stage adaptive robust model to find solutions that are insusceptible to deviations in demands.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Economics
Weibo Yang, Liangjun Ke, David Z. W. Wang, Jasmine Siu Lee Lam
Summary: This paper investigates the new variant of vehicle routing problem - VRPRD, aiming to minimize the total routing and weighted tardiness costs. By developing a new algorithm, the exact optimal solutions for over 75% of benchmark instances can be obtained within a short period of time.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2021)
Article
Computer Science, Artificial Intelligence
Xinxin Su, Gangyan Xu, Nan Huang, Hu Qin
Summary: This paper presents a solution to the manpower allocation and vehicle routing problem with staff qualifications and time windows (MAVRP-SQTW). The developed method, based on arc-flow and set-packing models, involves a branch-and-price-and-cut algorithm to optimize staff assignment and vehicle routing. Numerical results indicate that the set-packing model outperforms the arc-flow model, and sensitivity analyses highlight the significant effects of vehicle and staff numbers on the branch-and-price-and-cut algorithm. A primal heuristic is then proposed to improve the efficiency of solving large-scale instances.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Engineering, Civil
Mengyan Jiang, Yi Zhang, Y. Zhang
Summary: This paper addresses the e-bus scheduling problem under travel time uncertainty using robust optimization approaches. The study shows that by increasing operational costs, the delays and battery energy shortages caused by travel time uncertainty can be effectively reduced. A trade-off between reducing infeasibility rates and increasing operational costs is necessary when choosing an appropriate uncertainty budget.
JOURNAL OF ADVANCED TRANSPORTATION
(2021)
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)