Article
Transportation Science & Technology
Karmel S. Shehadeh, Emily L. Tucker
Summary: This study addresses the problem of determining warehouse locations and prepositioning relief item inventory in preparation for a disaster season. It proposes two-stage stochastic programming and distributionally robust optimization models to tackle uncertainty. The experimental results show the potential of these methods in improving computational efficiency and operational performance.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Computer Science, Interdisciplinary Applications
Israa Ismail
Summary: This study presents a mathematical model to optimize the allocation of relief resources in emergency situations, considering social and deprivation costs, and using the Rolling Horizon method for solution. Empirical and case study results demonstrate the importance of considering demographic structures and dynamic changes in affected areas.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Management
Okan Dukkanci, Achim Koberstein, Bahar Y. Kara
Summary: This study introduces a post-disaster delivery problem that utilizes drones under uncertainty, focusing on distributing essential relief items to victims gathered at assembly points after a disaster, specifically an earthquake. As roads may become impassable after an earthquake, drones serve as the primary mode of transportation. Considering the unpredictability of earthquake impacts, this study takes into account uncertainties in demand and road networks. Through stochastic programming and scenario decomposition algorithms, the objective is to minimize unsatisfied demand while meeting time constraints, range limitations, and capacity restrictions of the drones. A case study conducted in Istanbul, Turkey, evaluates the scenario decomposition algorithm's performance and analyzes the value of stochasticity and expected value of perfect information under various parametric settings. Sensitivity analyses are also performed by varying key problem parameters such as time constraints and drone capacities.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Xun Zhang, Du Chen
Summary: Prepositioning relief network is effective for mitigating the impact of natural disasters and public health emergencies, but designing the network is challenging due to limited information and correlated demand uncertainty. The problem is formulated and reformulated as a mixed-integer two-stage distributionally robust location-inventory model and a mixed-integer conic problem based on copositive cones, respectively. A branching-and-pricing heuristic with a warm start is designed to accelerate the problem-solving process, and results show that explicitly modelling demand correlation can decrease unmet demand.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Economics
Seyed Reza Abazari, Amir Aghsami, Masoud Rabbani
Summary: This research focuses on the role of humanitarian supply chain in mitigating damages after natural disasters, introducing a multi-objective programming model for managing relief item distribution. By determining the location and quantity of relief centers and transportation plans, losses are effectively reduced, with the model applied to the 2019 Iran flood as a case study to demonstrate practical effectiveness.
SOCIO-ECONOMIC PLANNING SCIENCES
(2021)
Article
Operations Research & Management Science
Muer Yang, Sameer Kumar, Xinfang Wang, Michael J. Fry
Summary: This article presents an optimization model for stocking disaster relief items at strategic locations to enhance the effectiveness of humanitarian supply chain distribution networks in responding to disasters. The model provides robust solutions by addressing uncertain parameters using distribution-free uncertainty ranges, which are illustrated through a case study of hurricane preparedness in the Southeastern United States. Simulation studies further demonstrate the effectiveness of the approach in situations where conditions deviate from model assumptions.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Management
Joline Uichanco
Summary: The study focused on the problem faced by the Philippine Department of Social Welfare in prepositioning relief items before the landfall of an oncoming typhoon with an uncertain trajectory and wind speed. By collaborating with DSWD, a practically relevant stochastic prepositioning model was developed, which prioritizes regions with high demand and prepositions relief items in all affected regions proportional to their total demand. This research contributes to bridging the gap between theory and practice in relief inventory management and highlights the importance of collaboration with government and nongovernment agencies in developing effective relief distribution models.
M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
(2022)
Article
Management
Penghui Guo, Jianjun Zhu
Summary: This study develops two-stage stochastic models that incorporate prepositioning, physical capacity reservation, and production capacity reservation for reactive procurement. By minimizing the supply-side monetary costs and the demand-side social impacts, the models aim to reduce costs and improve efficiency. The logic-based Benders decomposition method is used, and a new type of logic-based subgradient cut is introduced. Extensive numerical results and a case study validate the efficiency of the solution method, the value of incorporating stochasticity, and the superiority of the capacity reservation.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Cybernetics
Sara Nodoust, Mir Saman Pishvaee, Seyed Mohammad Seyedhosseini
Summary: The research explores the complexity of demand uncertainty in distributing relief items after earthquakes through a robust scenario-based possibilistic-stochastic programming model. Results demonstrate a significantly higher demand satisfaction level compared to traditional scenario-based stochastic programming models.
Article
Engineering, Industrial
Adrian F. Rivera, Neale R. Smith, Esteban Ogazon, Angel Ruiz
Summary: A key strategic issue in pre-disaster planning for humanitarian logistics is the establishment of adequate capacity and resources for efficient relief operations. This paper presents a scenario-based stochastic mixed-integer optimization formulation to support managers in adapting their network and preparedness decisions, considering uncertainty in demand and infrastructure availability. The formulation was applied to the case of hurricane Odile in Mexico, and numerical experiments showed improved decision-making compared to actual events. Further comparisons and analyses are provided.
EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING
(2023)
Article
Geosciences, Multidisciplinary
Ali Anjomshoae, Adnan Hassan, Kuan Yew Wong, Ruth Banomyong
Summary: This paper discusses the development of performance-based measurement models and systems in humanitarian supply chains, proposing a practical approach to handling imprecise and uncertain information. Through a hierarchical multi-stage Fuzzy Inference System, humanitarian performance indicators are clustered into four Balanced Scorecard type categories, achieving a detailed evaluation of humanitarian operational performance.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2021)
Article
Geosciences, Multidisciplinary
Yash V. Marthak, Eduardo Perez, Francis A. Mendez Mediavilla
Summary: This paper introduces a stochastic programming model considering prepositioning strategies among food bank facilities in high-risk areas to minimize the number of people not receiving needed supplies during natural disasters. The model takes into account the uncertainty associated with each facility's supplies, donations, and demand, as well as the impact of facility closures post-disaster on prepositioned supplies.
Article
Management
Jomon A. Paul, Minjiao Zhang, Muer Yang, Chong Xu
Summary: This paper proposes postponing the decision on humanitarian response to natural disasters and using social media data to estimate demands more accurately. The study finds that deploying relief supplies 12 hours before hurricane landfall can reduce total costs by 13%, and utilizing social media information can reduce costs by approximately 15%.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Engineering, Industrial
Ali Mehdi Nezhadroshan, Amir Mohammad Fathollahi-Fard, Mostafa Hajiaghaei-Keshteli
Summary: The paper discusses the design of a humanitarian logistics network with multiple central warehouses and local distribution centers to address operational and disruptive risks and ensure the delivery of essential supplies to beneficiaries.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Javaiz Parappathodi, Claudia Archetti
Summary: This paper proposes a crowdsourced humanitarian relief vehicle routing problem and presents a heuristic algorithm to generate high-quality solutions. Extensive computational studies are conducted to analyze the algorithm's performance and the impact of problem features on solution quality.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Green & Sustainable Science & Technology
M. Boronoos, M. Mousazadeh, S. Ali Torabi
Summary: This study introduces a novel multi-objective mixed integer nonlinear programming model for closed-loop green supply chain network design problem, aiming to minimize total costs, total CO2 emissions, and robustness costs simultaneously. By utilizing a robust flexible-possibilistic programming approach to handle flexible constraints and uncertainty in parameters, the study suggests that the carbon cap-and-trade policy is superior in most cases.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2021)
Article
Engineering, Industrial
Jafar Namdar, S. Ali Torabi, Navid Sahebjamnia, Ninad Nilkanth Pradhan
Summary: This paper proposes a novel framework for designing a resilient supply chain network to address operational and disruption risks. The framework includes quantifying the resilience score of facilities, identifying critical processes and business continuity metrics, and designing a multi-echelon, multi-product supply chain network model. The model aims to incorporate risk attitudes into the design process and provides useful managerial insights through sensitivity analyses on hypothetical disruptions and risk attitudes.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Engineering, Industrial
Reza Alikhani, S. Ali Torabi, Nezih Altay
Summary: This study proposes a two-stage stochastic optimization framework for designing a resilient retail supply chain network, considering various resilience strategies. Through stress testing, the research shows that implementing a combination of these resilience capabilities leads to a synergistic effect, increasing the network's resilience and decreasing post-disruption costs significantly.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2021)
Article
Operations Research & Management Science
Iman Kazemian, S. Ali Torabi, Christopher W. Zobel, Yuhong Li, Milad Baghersad
Summary: This study introduces a supply chain resilience assessment framework that quantifies structural factors and their relationships to different resilience strategies, aiding decision makers in planning more effective resilience improvement actions.
OPERATIONAL RESEARCH
(2022)
Article
Green & Sustainable Science & Technology
Sina Nayeri, S. Ali Torabi, Mahdieh Tavakoli, Zeinab Sazvar
Summary: This study presents a multi-objective mixed-integer programming model for designing a sustainable supply chain network while taking into account resilience and responsiveness measures. By using a new optimization approach and meta-goal programming, the uncertainty in dynamic business environments is addressed. A case study in the water heater industry validates the effectiveness of the proposed model and solution approach, while offering useful insights through sensitivity analyses on key parameters.
JOURNAL OF CLEANER PRODUCTION
(2021)
Correction
Operations Research & Management Science
Iman Kazemian, S. Ali Torabi, Christopher W. Zobel, Yuhong Li, Milad Baghersad
OPERATIONAL RESEARCH
(2022)
Article
Engineering, Industrial
Hassan Gharoun, Mahdi Hamid, S. Ali Torabi
Summary: This paper introduces a new bi-objective model for integrated production planning and reliability-based multi-level preventive maintenance scheduling, aiming to minimize total cost while maximizing customer satisfaction. It identifies the most profitable customers using a multi-attribute decision making approach, develops efficient meta-heuristic algorithms for large-scale problems, and employs the TOPSIS method to select the most desirable solution among the obtained Pareto solutions. A case study demonstrates the applicability of the proposed approach.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Zeynab Mosanna, Jafar Heydari, S. Ali Torabi, M. Ali Ulkue
Summary: This paper studies the optimal design of a coordination mechanism for a socially responsible supply chain, focusing on the relationship between a manufacturer and a retailer in meeting the demands of socially aware consumers. The research finds that using a two-part tariff contract can achieve coordination in the supply chain, and the manufacturer can attract socially aware consumers by pledging donations to charity.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Mojtaba Ranjbar, Mohammad Mahdi Nasiri, S. Ali Torabi
Summary: This study utilizes a fuzzy hybrid multi-criteria method and a fuzzy bi-objective mathematical programming model to address the project portfolio selection and scheduling problem, optimizing the project portfolio through weighted qualitative criteria and a bi-objective fuzzy mathematical model.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Industrial
Mojtaba Khorram Niaki, Fabio Nonino, Keivan Tafakkori, S. Ali Torabi, Iman Kazemian
Summary: This paper presents a theoretical model incorporating manufacturing competitive capabilities and contingency concepts and validates it through an empirical study on 105 manufacturing firms using AM. The study finds that production volume, material type, country's economic development, and firm's experience have contingency effects on AM's competitive capabilities.
JOURNAL OF MANUFACTURING TECHNOLOGY MANAGEMENT
(2022)
Article
Operations Research & Management Science
Azadeh Farsi, S. Ali Torabi, Mandi Mokhtarzadeh
Summary: The complexity of surgery scheduling negatively affects the efficiency of surgical staff and patient satisfaction. This paper proposes an integrated scheduling approach using a constraint programming model and a hybrid method of NSGA-II and MODA to minimize makespan and maximize satisfaction. Results show that the proposed method outperforms existing approaches in providing high-quality solutions efficiently.
INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT
(2022)
Article
Economics
Ali Ghavamifar, S. Ali Torabi, Mohammad Moshtari
Summary: This paper proposes a novel hybrid relief procurement contract that effectively coordinates the supply of relief items between a supplier and a humanitarian organization. By categorizing different provinces according to risk approach and conducting sensitivity analyses, the study demonstrates that using this contract can significantly improve the procurement process in humanitarian organizations.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Engineering, Multidisciplinary
A. Mohammadbagher, S. Ali Torabi
Summary: This study addresses a new variant of the vehicle routing problem for a mixed fleet of electric and combustion vehicles under the presence of time windows and charging stations. A bi-objective mixed-integer programming model is developed to minimize cost and pollution level concurrently. The study presents a framework that can find a set of Pareto optimal solutions considering different combinations of electric and combustion vehicles.
INTERNATIONAL JOURNAL OF ENGINEERING
(2022)
Article
Computer Science, Information Systems
Morad Danishvar, Sebelan Danishvar, Evina Katsou, S. Afshin Mansouri, Alireza Mousavi
Summary: The study introduces a multi-objective batch-based flowshop scheduling optimization method utilizing a deep neural network to optimize production system schedules, aiming to reduce energy consumption, costs, and completion times. Through real-time and look ahead discrete event simulation, the robustness and practicality of the optimal schedules are effectively validated.
Article
Operations Research & Management Science
Nafiseh Shamsi Gamchi, S. Ali Torabi, Fariborz Jolai
Summary: This paper addresses a novel bi-objective vehicle routing problem for distributing vaccines to control the spread of communicable diseases after a disaster. A hybrid solution procedure is developed to minimize social costs incurred by considering different priority groups and vehicle costs. The performance of the model and solution approach is evaluated through small test problems and a real case-inspired example.
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)
