Article
Economics
Kaoutar Chargui, Tarik Zouadi, Abdellah El Fallahi, Mohamed Reghioui, Tarik Aouam
Summary: This study proposes a new approach for solving the problem of quay crane allocation and scheduling in container terminals, taking into account worker performance variability and yard truck deployment constraints. It is shown that integrative planning can minimize vessel departure time, while also providing a computationally efficient method to find a lower bound.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2021)
Article
Green & Sustainable Science & Technology
Meixian Jiang, Jiajia Feng, Jian Zhou, Lin Zhou, Fangzheng Ma, Guanghua Wu, Yuqiu Zhang
Summary: As container ports play a vital role in the global supply chain, many ports are consolidating their terminal resources to enhance their competitiveness. In the context of energy conservation and emission reduction, ports are now evaluating their competitiveness not only in terms of terminal size and throughput, but also in terms of low energy consumption and pollution. A nonlinear mixed-integer programming model is developed to minimize total cost by considering carbon cost in the uncertain multi-terminal berth and quay crane joint scheduling problem. The model takes into account factors like water depth and crane interference, and uses expected vessel arrival time and average operational efficiency as scheduling parameters. By combining simulated annealing mechanism, an improved adaptive genetic algorithm is developed, and numerical experiments are conducted. The results demonstrate that joint scheduling with uncertainties and a multi-terminal coordination mechanism can effectively reduce operating costs, including carbon costs and vessel departure delay rate, and improve resource utilization. Scheduling with the multi-terminal coordination mechanism yields even more significant improvements than scheduling with uncertainties alone.
Article
Engineering, Marine
Armi Kim, Hyun-Ji Park, Jin-Hyoung Park, Sung-Won Cho
Summary: This study proposes a methodology for rescheduling berth and quay cranes caused by updated information on the arrival time of vessels, using a mixed-integer linear programming model and a rolling-horizon approach. Numerical experiments conducted with empirical data from a container terminal in Busan, Korea show that the proposed model reduces penalty costs and overall delayed departure time compared to traditional terminal planner results.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2021)
Article
Operations Research & Management Science
Evrim Ursavas
Summary: This paper presents a decision support system for berth allocation decision in a container terminal. The system uses a dynamic discrete-event simulation model and an optimization tool to determine the priority controls for allocating berths to different vessels, addressing the complex service level requirements. A comprehensive case study from a Turkish container terminal and further experiments based on data from the Port of Rotterdam demonstrate the practical application and usability of the system.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Engineering, Chemical
Hongxing Zheng, Zhaoyang Wang, Hong Liu
Summary: This paper investigates the integrated rescheduling problem of berth allocation and quay crane assignment with vessel delay and unscheduled vessel arrival, in order to cope with the disturbance of terminal operations. By using a rolling time-domain approach, the rescheduling moment is determined, and an improved genetic algorithm is designed to obtain a rescheduling solution using various strategies. Through scenario experiments and comparisons, the effectiveness and superiority of this algorithm are verified.
Article
Engineering, Marine
Shuang Tang, Sudong Xu, Jianwen Gao, Mengdi Ma, Peng Liao
Summary: This article studies the evacuation strategy of container vessels in continuous terminals and analyzes the impact of service priority on the terminal by establishing a model and solving it with a genetic algorithm. The results show that using the square of handled container volume is more conducive to ensuring the shipping period of large vessels.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Marine
Min Tang, Bin Ji, Xiaoping Fang, Samson S. Yu
Summary: This study established a mixed-integer linear programming model for the continuous berth allocation and quay crane assignment problem and proposed a large neighborhood search algorithm and discretization strategy to improve the efficiency of solving the problem. Numerical results showed that the algorithm can effectively optimize solutions for small-scale instances.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Emmanouil Thanos, Tulio Toffolo, Haroldo Gambini Santos, Wim Vancroonenburg, Greet Vanden Berghe
Summary: This paper addresses the continuous Berth Allocation problem with Specific Quay Crane Assignments, aiming to minimize container transshipment distances within the terminal yard. An integrated mathematical formulation and a fast local search-based heuristic are proposed, evaluated against a state-of-the-art commercial solver, confirming the effectiveness of the heuristic in improving terminal operations efficiency.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Marine
Meixian Jiang, Jian Zhou, Jiajia Feng, Lin Zhou, Fangzheng Ma, Guanghua Wu
Summary: This work focuses on the integrated berth and crane scheduling problem in a tidal port with multiple terminals, considering uncertainties, tides, maximum crane coverage, and crane interference. To handle uncertainties, randomly generated samples are used to evaluate solutions and slack variables are introduced to mitigate the impact of vessel arrival and crane operational efficiency variations. A novel nonlinear mixed integer programming model is formulated to minimize the sum of the expected costs and variances under all samples. An improved adaptive genetic algorithm, incorporating a simulated annealing mechanism and a greedy construction strategy, is developed and implemented in MATLAB. Numerical experiments confirm the feasibility and effectiveness of the algorithm and demonstrate the benefits of multi-terminal collaborative scheduling strategy under uncertainty. The results show that the algorithm can achieve feasible scheduling solutions with higher quality. Compared to strategies considering only uncertainty or multi-terminal collaboration, considering both factors can effectively reduce costs and enhance port competitiveness.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Mathematics, Interdisciplinary Applications
Bowei Xu, Lingling Wang, Junjun Li
Summary: This paper constructs an uncertain event propagation network model of container terminal multilevel handlings using hypernetwork theory, analyzing its topological characteristics and simulation results, providing an effective method for analyzing and reducing the impact of uncertain events in container terminal operations.
DISCRETE DYNAMICS IN NATURE AND SOCIETY
(2021)
Article
Computer Science, Information Systems
Wenwen Guo, Mingjun Ji, Huiling Zhu
Summary: Berths and yards are critical parts of container terminals, and their efficiency directly impacts the terminal's operation efficiency. Multi-period coordinated optimization can effectively reduce truck travel distance and improve operational efficiency.
Article
Computer Science, Artificial Intelligence
Junliang He, Yu Wang, Caimao Tan, Hang Yu
Summary: This paper explores how factors related to QC drivers, such as high labor costs and differences in efficiency between day and night, can significantly impact the schedules of berth allocation and quay crane assignment. By developing a mixed integer programming model with acceleration algorithms and proposing a meta-heuristic framework, the study effectively addresses this problem and validates its approach through numerical experiments.
ADVANCED ENGINEERING INFORMATICS
(2021)
Article
Engineering, Marine
Wenqian Liu, Xiaoning Zhu, Li Wang, Siqiao Li
Summary: This paper investigates an intelligent scheduling approach considering uncertain arrival time of vessels and operation efficiency of quay cranes. A multi-objective robust model is designed, with the introduction of control parameters to handle uncertainties. Results indicate that the intelligent algorithm outperforms in solution quality and solving time for large-scale instances, with vessel delays showing a greater impact on the scheme than uncertain quay crane productivity, especially under medium uncertainty levels.
Article
Engineering, Industrial
Soroush Fatemi-Anaraki, Reza Tavakkoli-Moghaddam, Dorsa Abdolhamidi, Behdin Vahedi-Nouri
Summary: Maritime shipment is crucial for global goods transportation, involving ship visits to ports, cargo loading/unloading, and port departures. Resolving maritime transportation issues can be seen as a hybrid flow shop scheduling problem with unrelated parallel machines, machine eligibility constraint, and shared resources.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Maxim A. Dulebenets
Summary: This study proposes a novel optimization model for reactive berth allocation and scheduling at marine terminals, along with the development of a Diffused Memetic Optimizer (DMO) algorithm to address the computational complexity. The experiments show that DMO performs competitively against other methods, and the periodic migration between diffusion grid areas and problem-specific hybridization techniques are crucial for its explorative capabilities. Additionally, important managerial insights are revealed using the proposed methodology, aiding in berth schedule recovery.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Management
Mir Ehsan Hesam Sadati, Deniz Aksen, Necati Aras
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2020)
Article
Management
A. Tamer Unal, Semra Agrali, Z. Caner Taskin
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2020)
Article
Operations Research & Management Science
M. Asli Aydin, Z. Caner Taskin
RAIRO-OPERATIONS RESEARCH
(2020)
Article
Engineering, Industrial
Kubra Taninmis, Necati Aras, I. Kuban Altinel, Evren Guney
Article
Management
Serkan Kalay, Z. Caner Taskin
Summary: This study investigates the production planning problem in the context of float glass manufacturing, specifically focusing on high sequence dependent family setup costs and unique characteristics of the industry. Two mixed integer programming formulations were developed to address the problem, with additional computational experiments conducted to gain insights from a business perspective. The study provides valuable theoretical comparisons and practical implications for generating campaign plans in the float glass industry.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Management
Oylum Seker, Tinaz Ekim, Z. Caner Taskin
Summary: The paper investigates the selective graph coloring problem in perfect graphs using an exact cutting plane algorithm, and proposes a method to randomly generate perfect graphs. Computational experiments demonstrate that their solution strategy significantly improves the solvability of the problem.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Hardware & Architecture
Zeynep Suvak, I. Kuban Altinel, Necati Aras
Summary: The study focuses on the minimum cost flow problem with conflicts, introducing two exact solution algorithms and preprocessing procedures to reduce problem size. Extensive computational experiments show that these new algorithms are highly efficient.
Article
Management
Kuebra Taninmis, Necati Aras, I. Kuban Altinel
Summary: The paper proposes an improved x-space algorithm for solving a class of min-max bilevel optimization problems and applies it to the context of reducing the misinformation spread in social networks. The performance of the new algorithm is shown to be superior to that of the original algorithm and compares favorably with other related algorithms.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Operations Research & Management Science
Banu Kabakulak, Z. Caner Taskin, Ali Emre Pusane
Summary: In this study, a branch-and-price method is proposed to decode the received vector with minimum error, improving solvability significantly compared to a state-of-the-art IP decoder and outperforming the conventional sum-product algorithm in error performance. By introducing heuristic feasible solutions and valid cuts, the performance of the method is enhanced.
JOURNAL OF GLOBAL OPTIMIZATION
(2021)
Article
Computer Science, Hardware & Architecture
Betul Ahat, Ahmet Cihat Baktir, Necati Aras, I. Kuban Altinel, Atay Ozgovde, Cem Ersoy
Summary: A mixed-integer linear programming (MILP) model is proposed in this study to optimally design a multi-tier computation structure for new services under edge computing infrastructure. Scalability issues are addressed through a heuristic algorithm based on the Lagrangian relaxation of the MILP formulation, while a greedy heuristic approach is presented for operators to quickly find feasible solutions. Computational experiments on randomly generated topologies show that the proposed methods can obtain high-quality solutions within time constraints.
Article
Computer Science, Interdisciplinary Applications
Serkan Kalay, Z. Caner Taskin
Summary: The study focuses on production planning in process industries with costly sequence dependent family setups, specifically addressing the unique characteristics of float glass manufacturing. By developing a branch-and-price algorithm, consistent performance across different problem sizes is achieved and compared with previous work through computational experiments.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Operations Research & Management Science
Emel Seyma Kucukasci, Mustafa Gokce Baydogan, Z. Caner Taskin
Summary: Multiple instance learning (MIL) is a variation of supervised learning where data consists of labeled bags. This study presents a novel quadratic programming (QP)-based approach to classify bags, which overcomes the computational difficulties and improves efficiency compared to existing algorithms, and demonstrates high classification success in different learning applications.
JOURNAL OF GLOBAL OPTIMIZATION
(2022)
Article
Operations Research & Management Science
Oylum Seker, Pinar Heggernes, Tinaz Ekim, Z. Caner Taskin
Summary: This paper addresses the problem of generating chordal graphs and proposes an algorithm based on the intersection of subtrees of a host tree. Experimental results show that the method is capable of generating a diverse set of chordal graphs.
RAIRO-OPERATIONS RESEARCH
(2022)
Article
Engineering, Industrial
Bahadir Pamuk, Semra Agrali, Z. Caner Taskin, Banu Kabakulak
Summary: This paper considers the uncapacitated lot-sizing problem in co-production systems and proves that it is strongly NP-Hard. Various MILP formulations of the problem are developed, and their LP relaxations are shown to be equal. The proposed Branch & Cut algorithm is based on a separation algorithm, lower bounds, and a constructive heuristic. Experimental results on different datasets are provided.
Article
Computer Science, Artificial Intelligence
Emel Seyma KucukaSci, Mustafa Gokce Baydogan, Z. Caner TaSkin
Summary: Multiple instance learning (MIL) is a method for classifying objects with complex structures and is widely used in data mining applications. The study introduces a linear programming framework for learning instance contributions to bag labels without requiring specific assumptions.
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
(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)
