Article
Computer Science, Artificial Intelligence
Marko Durasevic, Mateja Dumic
Summary: This paper investigates the application of genetic programming to automatically design effective relocation rules, which outperform manually designed rules and demonstrate good generalization performance across unseen problems, presenting a viable alternative to existing manual designs in the area of container relocation problems.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Xiaoyuan Hu, Chengji Liang, Daofang Chang, Yue Zhang
Summary: The study introduces a novel yard sharing strategy to address the shortage of storage space in container terminal yards by utilizing surplus storage space at dry ports. By developing a mathematical model and using genetic algorithms, the optimization of container storage space allocation is achieved, demonstrating the effectiveness and advantages of this strategy in practical applications.
ADVANCED ENGINEERING INFORMATICS
(2021)
Article
Management
Ahmed Azab, Hiroshi Morita
Summary: This paper introduces a new optimization problem, the Block Relocation Problem with Appointment Scheduling (BRPAS), to jointly address container relocation and appointment scheduling in container terminals. Two binary IP models are proposed to solve the problem and are extended to cover various operational aspects related to container pickup operations. Results show that the proposed approach can improve container relocation operations at terminal yards by coordinating with appointment scheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Tiziano Bacci, Sara Mattia, Paolo Ventura
Summary: The container relocation problem involves finding the minimum number of moves to retrieve all containers in a bay according to a given order, with the stochastic variant considering uncertain retrieval orders. Existing solution approaches often face complexity issues, where solutions grow exponentially with block numbers. A new heuristic approach is proposed to reduce solution space complexity and successfully solve previously unsolvable instances. Performance statistics demonstrate the impact of instance size on solution outcomes.
Article
Computer Science, Interdisciplinary Applications
Lebao Wu, Zuhua Jiang, Fuhua Wang
Summary: This paper investigates the problem of inbound and pre-processing operations in the steel plate yard of shipbuilding. It proposes a solution that combines the two operations and studies the stack inbound and pre-marshalling problem, where storage and relocation moves can alternate. The paper introduces a novel integer programming model that reduces the number of time periods and presents an exact branch-and-cut algorithm to handle the extra constraints. Experimental results show that the proposed method outperforms other ILP-based methods in the literature.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Management
Shunji Tanaka, Stefan Voss
Summary: This study introduces a novel exact algorithm for the restricted block relocation problem and demonstrates its effectiveness through computational experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Economics
Bo Jin, Zhishan Yu, Mingzhu Yu
Summary: This study addresses the inbound container remarshaling problem in an automated container terminal and proposes two new integer linear programming models that outperform existing models. One of the models demonstrates high computational efficiency and can obtain optimal solutions in just a few hundred milliseconds.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Economics
Lingrui Kong, Mingjun Ji, Zhendi Gao
Summary: This study investigates the scheduling problem of tandem quay crane at container terminals and proposes a novel integer linear programming model to minimize unloading time. By introducing valid inequalities and logic-based Benders decomposition algorithm, the convergence speed of the algorithm can be accelerated, and computational results show that the proposed model is superior to previous models.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Engineering, Chemical
Shuang Duan, Hongxing Zheng, Xiaomin Gan
Summary: A two-stage mixed integer programming model was established to optimize the pre-marshalling operation scheme in the export container block, improving the efficiency of loading operation. An improved artificial bee colony algorithm was designed to solve the optimization model of container reshuffling in the first stage, while an improved genetic algorithm was used to solve the optimization model of yard crane configuration and scheduling in the second stage. Comparative analysis showed that our algorithm outperformed CPLEX and traditional heuristic algorithms, providing a reference for improving port operation efficiency.
Article
Management
Omar Abou Kasm, Ali Diabat, Joseph Y. J. Chow
Summary: Quay crane scheduling at container terminals may see a revolution with the introduction of new patented quay cranes. These next-generation cranes have the capability to significantly improve service time and increase quay side capacity by accessing containers from both sides of the vessel and performing multiple container operations simultaneously. The study proposes a mixed integer program and two solution methodologies for the simultaneous scheduling of next-generation and traditional cranes. The results show that the proposed approach can effectively solve real cases in a reasonable amount of time, and a case study highlights the impact of crane positioning on service time and provides insights for further modeling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Economics
Dian Wang, Jun Zhao, Qiyuan Peng
Summary: In this paper, the authors address a problem of assigning and combining loaded inbound trains to heavier outbound trains at a heavy-haul marshalling station. They formulate the problem as a mixed integer linear programming model and propose an iterated search algorithm to efficiently solve large-scale instances. The computational results demonstrate the effectiveness of the proposed approaches.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Green & Sustainable Science & Technology
Yan Zheng, Meixian Xu, Zhaohu Wang, Yujie Xiao
Summary: Studied an integrated scheduling problem considering three types of handling equipment in container handling systems to ensure low-carbon operations and reduce energy consumption. Formulated the problem as a mixed integer linear programming (MILP) and developed a genetic algorithm (GA) to solve it.
Article
Computer Science, Interdisciplinary Applications
Oliviana Xavier do Nascimento, Thiago Alves de Queiroz, Leonardo Junqueira
Summary: This study addresses the Single Container Loading Problem by proposing an exact approach that iteratively solves integer linear programming and constraint programming models. Extensive computational experiments were conducted to demonstrate the performance of the proposed approach, showing that it could optimally solve instances with around ten item types and more than 70% of all instances.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Engineering, Civil
Rongye Ye, Rongguang Ye, Sisi Zheng
Summary: This paper explores the application of machine learning techniques in solving the blocks relocation problem and verifies the relationship between significant features and the number of container relocations, demonstrating the feasibility of machine learning in this problem.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Engineering, Industrial
Ziliang Wang, Chenhao Zhou, Ada Che, Jingkun Gao
Summary: This paper proposes an improved policy-based Monte Carlo tree search (P-MCTS) algorithm to solve the container pre-marshalling problem (CPMP). The CPMP is formulated as a Markov decision process (MDP) model to consider the sequential nature of the problem. The P-MCTS algorithm utilizes eight composite reshuffling rules and modified upper confidence bounds in the selection phase, and a well-designed heuristic algorithm in the simulation phase. Experimental results show that the P-MCTS outperforms all compared methods in scenarios with different priorities and scenarios where containers can share the same priority.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Operations Research & Management Science
Gilbert Laporte, Frederic Meunier, Roberto Wolfler Calvo
4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH
(2015)
Article
Operations Research & Management Science
Franck Butelle, Laurent Alfandari, Camille Coti, Lucian Finta, Lucas Letocart, Gerard Plateau, Frederic Roupin, Antoine Rozenknop, Roberto Wolfler Calvo
ANNALS OF OPERATIONS RESEARCH
(2016)
Article
Computer Science, Interdisciplinary Applications
Marcos Melo Silva, Anand Subramanian, Luiz Satoru Ochi
COMPUTERS & OPERATIONS RESEARCH
(2015)
Article
Management
Guenes Erdogan, Maria Battarra, Roberto Wolfler Calvo
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2015)
Article
Operations Research & Management Science
Michele Barbato, Roland Grappe, Mathieu Lacroix, Roberto Wolfler Calvo
DISCRETE OPTIMIZATION
(2016)
Article
Operations Research & Management Science
Paolo Gianessi, Laurent Alfandari, Lucas Letocart, Roberto Wolfler Calvo
TRANSPORTATION SCIENCE
(2016)
Article
Operations Research & Management Science
Roberto Baldacci, Sandra Ulrich Ngueveu, Roberto Wolfler Calvo
TRANSPORTATION SCIENCE
(2017)
Article
Management
Marcos de Melo da Silva, Gunes Erdogan, Maria Battarra, Vitaly Strusevich
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2018)
Article
Computer Science, Hardware & Architecture
Marco Casazza, Alberto Ceselli, Daniel Chemla, Frederic Meunier, Roberto Wolfler Calvo
Editorial Material
Computer Science, Hardware & Architecture
Roberto Wolfler Calvo, Lucas Letocart, Roberto Baldacci
Article
Operations Research & Management Science
Gilbert Laporte, Frederic Meunier, Roberto Wolfler Calvo
ANNALS OF OPERATIONS RESEARCH
(2018)
Article
Operations Research & Management Science
Stefania Pan, Roberto Wolfler Calvo, Mahuna Akplogan, Lucas Letocart, Nora Touati
DISCRETE OPTIMIZATION
(2019)
Proceedings Paper
Transportation
Paolo Gianessi, Laurent Alfandari, Lucas Letocart, Roberto Wolfler Calvo
NINTH INTERNATIONAL CONFERENCE ON CITY LOGISTICS
(2016)
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)