Article
Computer Science, Interdisciplinary Applications
Erfaneh Nikzad, Mahdi Bashiri, Babak Abbasi
Summary: A two-stage stochastic programming model is proposed for the staff dimensioning problem for temporary caregivers in home healthcare systems. An algorithm is developed to generate an initial solution and then improve it, considering uncertainty in required service, number of visits, and service time for each patient.
COMPUTERS & OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Fatemeh Sarayloo, Teodor Gabriel Crainic, Walter Rei
Summary: In this study, we address the Multicommodity Capacitated Fixed-charge Network Design problem with uncertain demands as a two-stage stochastic program. To tackle this problem, we propose an efficient matheuristic approach called Integrated Learning and Progressive Hedging, which combines a specialized learning-based matheuristic with the progressive hedging algorithm. We also introduce a novel reference point definition that utilizes subproblem information to enhance the PH algorithm at each aggregation step. Extensive computational experiments demonstrate that our proposed approach is the preferred method for quickly obtaining high-quality solutions to large instances of stochastic network problems.
JOURNAL OF HEURISTICS
(2023)
Article
Operations Research & Management Science
Xingbang Cui, Liping Zhang
Summary: In this paper, a localized PHA algorithm is proposed to solve nonmonotone SVIs, and it is shown to be effective based on the weaker assumption of locally elicitable maximal monotonicity. Numerical experiments confirm the efficiency of the proposed algorithm.
MATHEMATICS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Clea Martinez, Marie-Laure Espinouse, Maria Di Mascolo
Summary: This article presents a method to schedule and assign staff in home care agencies, taking into consideration the realistic constraints. By re-assigning the careworkers and redesigning their tours, the method addresses the issue of obsolete schedules caused by variations within the pool of patients or the staff.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Engineering, Multidisciplinary
Zhenguo Mu
Summary: This article introduces a randomized progressive hedging algorithm that addresses the high cost problem of PHA when the scenario set is large by controlling the per-iteration cost.
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
(2023)
Article
Computer Science, Artificial Intelligence
Mahdi Jalilvand, Mahdi Bashiri, Erfaneh Nikzad
Summary: This paper proposes an effective Progressive Hedging algorithm for the vehicle routing problem with two-layers time window assignment and stochastic service times. The algorithm aims to give more flexibility to carriers in serving more customers using less vehicles, and the validity of the model was verified through various numerical examples. The problem was formulated as a two-stage stochastic model and solved using a Progressive Hedging algorithm for large-scale instances, confirming the efficiency of the proposed solution approach in different scenarios.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Tiago F. D. Pinheiro, Santiago Ravelo, Luciana S. Buriol
Summary: This paper studies the k-labeled spanning forest problem and proposes a fix-and-optimize matheuristic to solve it. The proposed method outperforms other algorithms in most cases according to the experimental results.
2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2022)
Article
Management
Xinjie Xing, Dongping Song, Chengfeng Qiu, Paul R. Drake, Yuanzhu Zhan
Summary: This study aims to address the optimization of tank container demurrage policy and flow in order to maximize profits. By designing a progressive hedging algorithm, this algorithm can handle the challenges in terms of computational time and memory requirements, providing managers with an optimization tool to explore and understand operational costs and profits.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Pediatrics
Astrid Batlle, Imma Boada, Santiago Thio-Henestrosa, Mariona Fernandez de Sevilla, Juan Jose Garcia-Garcia
Summary: This study aimed to compare the effectiveness of traditional manual route planning with a route optimizer in a pediatric acute home-hospitalization program. The results showed that route-planning technology saved planning time, generated better plans, and was easy to use. All participants had a positive evaluation of the route planning tool.
FRONTIERS IN PEDIATRICS
(2022)
Article
Engineering, Multidisciplinary
Z. Entezari, M. Mahootchi
Summary: This paper develops a mathematical model for home health care companies, optimizing both economic factors and service quality. Real situations are considered to create a Mixed-Integer Linear Programming model and propose a Genetic Algorithm solution.
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)
