Article
Computer Science, Artificial Intelligence
Domenic Kellner, Maximilian Lowin, Oliver Hinz
Summary: Managing an extreme event like a healthcare disaster requires accurate information about the event's circumstances. However, obtaining optimal information quality is often challenging due to delays in reporting. To address this issue, we propose using data from online social networks to create indices for forecasting COVID-19 case numbers and hospitalization rates. By combining different data sources like Twitter and Reddit, we are able to generate more accurate predictions compared to using a single source alone. Furthermore, we show that our predictions can anticipate official COVID-19 cases by up to 14 days. Additionally, we highlight the importance of adjusting the model when new information becomes available or the underlying data changes.
DECISION SUPPORT SYSTEMS
(2023)
Article
Construction & Building Technology
Chujun Zong, Manuel Margesin, Johannes Staudt, Fatma Deghim, Werner Lang
Summary: This paper proposes a multi-objective stochastic optimization framework for decision-making in the early design phase of building facade design under uncertainty. The framework aims to narrow down the material choices and provide robust solutions. Through a case study, the effectiveness of the framework was validated. The results indicate that insulation and outer wall cladding are the most important parameters in building facade design.
BUILDING AND ENVIRONMENT
(2022)
Article
Operations Research & Management Science
Milos Kopa, Tomas Rusy
Summary: This study introduces a stochastic programming asset-liability management model that handles decision-dependent randomness, focusing on pricing and management problems related to consumer loans. Factors such as customer rejection, default, prepayment, and market interest rates are considered, with a scenario tree capturing their evolution. The model provides optimal decisions for companies and a sensitivity analysis for different customer types, highlighting the potential losses for not acting optimally.
ANNALS OF OPERATIONS RESEARCH
(2021)
Review
Geosciences, Multidisciplinary
Camilla Pezzica, Valerio Cutini, Clarice Bleil de Souza
Summary: The growing awareness of long-term impacts in emergency management plans has shifted the focus of post-disaster housing studies towards integrated recovery and development. A systematic review of literature on temporary housing post-natural hazards from a decision-making perspective identified critical components and necessary synergies at operational, managerial, and strategic levels. This structured overview highlights the importance of decision-making alignment and in-depth examination of dichotomies for novel methods and tools in disaster recovery planning.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2021)
Article
Management
Xiaomei Mi, Huchang Liao, Xiao-Jun Zeng
Summary: This study introduces an approximate transitivity-based consistency threshold to ensure the reliability of ranking alternatives in group decision making with reciprocal preference relations. The natural inconsistency of reciprocal preference relations is analyzed through numerical experiments, leading to the introduction of transitivity-based consistency thresholds. Additionally, a transitivity checking process is incorporated in the group decision-making process, and a model for integrated transitivity checking is provided for application.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2022)
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
Duc-Cuong Dang, Christine S. M. Currie, Bhakti Stephan Onggo, Diah Chaerani, Audi Luqmanul Hakim Achmad
Summary: This paper examines a variant of the lot sizing problem in the context of disaster management. Different formulations, including classical robust optimization, risk-minimization stochastic programming, and adjustable robust optimization, are proposed to address uncertainties. Experiments using data from West Java, Indonesia are conducted to evaluate the advantages and drawbacks of each method. Overall, this research provides decision makers with a toolbox for procurement decisions and offers insights into budget allocation and storage management in disaster scenarios.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Economics
Tarun Rambha, Linda K. Nozick, Rachel Davidson, Wenqi Yi, Kun Yang
Summary: Hurricanes lead to large scale evacuations each year, with the decision of whether to evacuate hospitals during emergencies posing particular challenges. Lack of clear guidelines in emergency response plans has resulted in poor decisions in the past. A stochastic optimization formulation has been developed to address this problem, emphasizing the importance of balancing cost and risk in evacuating patients.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2021)
Article
Mathematics
Nadide Caglayan, Sule Itir Satoglu
Summary: Disaster management involves mitigating, preparing, responding, and recovering from disasters. A multi-objective stochastic programming model was developed in this study to minimize casualties and improve efficiency in disaster response.
Article
Geosciences, Multidisciplinary
Ehsan Shakeri, Bela Vizvari, Ramtin Nazerian
Summary: This paper reviews, compares, and analyzes the legal and institutional frameworks of Disaster Management (DM) systems in India and Nigeria, finding that India has developed a more effective system compared to Nigeria. However, both countries face challenges in mobilizing and managing DM funds due to a lack of transparency. The study also highlights the focus on natural disasters and the lack of emphasis on man-made hazards in both countries' DM systems.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2021)
Article
Management
Vincent Guigues, Anatoli Juditsky, Arkadi Nemirovski
Summary: This paper introduces a new class of decision rules, Constant Depth Decision Rules (CDDRs), for multistage optimization under linear constraints with uncertainty-affected right-hand sides. It demonstrates through mathematical models and application examples the effectiveness of these decision rules in solving complex problems.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Engineering, Industrial
Tadeusz Sawik, Bartosz Sawik
Summary: This paper applies stochastic optimisation of CVaR to maintain risk-averse viability and improve resilience of a supply chain under propagated disruptions. Two stochastic optimisation models are developed with conflicting objectives, and a stochastic mixed integer quadratic programming model is used to select a risk-averse viable production trajectory. The proposed approach is applied to smartphone manufacturing, and the findings show that more risk-aversive decision-making leads to a larger viability space and higher resilience of the supply chain. Single-objective decision-making may reduce supply chain viability.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Economics
Weiqiao Wang, Kai Yang, Lixing Yang, Ziyou Gao
Summary: This paper explores several practical features in disaster relief management and proposes a distributionally robust optimization model, evaluates its performance, and applies it to a real-world case study, resulting in managerial implications and insights.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2021)
Article
Management
Mary Dillon, Ilmari Vauhkonen, Mikko Arvas, Jarkko Ihalainen, Eeva Vilkkumaa, Fabricio Oliveira
Summary: This research develops a methodological framework for modelling platelet inventories and proposes a two-stage stochastic programming model to optimize inventory decisions. The model is validated with the cooperation of the Finnish Red Cross Blood Service. The study evaluates the costs and impacts of extending the shelf life of platelets from five to seven days.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Engineering, Industrial
Amir Azaron, Uday Venkatadri, Alireza Farhang Doost
Summary: In this study, a multi-objective two-stage stochastic programming model is developed to address various decision-making aspects within a supply chain network. By utilizing the epsilon-constraint method to generate a set of Pareto optimal solutions, treating uncertain parameters as continuous random variables, and employing the SAA scheme for near optimal solutions, the efficiency of the proposed solution methodology is demonstrated through computational studies involving hypothetical and real supply chain networks of different sizes.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Chemistry, Analytical
Samad Barri Khojasteh, Jose R. Villar, Camelia Chira, Victor M. Gonzalez, Enrique de la Cal
Article
Computer Science, Artificial Intelligence
Jose R. Villar, Camelia Chira, Enrique de la Cal, Victor M. Gonzalez, Javier Sedano, Samad B. Khojasteh
Summary: Research on fall detection systems is crucial for supporting the elderly population in their daily lives. Challenges include the need for real fall data sets, studying models that learn from users, adapting to user performance, and exploring hybrid and ensemble approaches.
Article
Computer Science, Artificial Intelligence
Mirko Fanez, Jose R. Villar, Enrique de la Cal, Victor M. Gonzalez, Javier Sedano, Samad B. Khojasteh
Summary: Fall detection using wearable devices has been a focus of research, but current solutions still have issues that need further research and improvement. The proposed method consists of three stages, including event detection, one-class problem classification, and final classification, to achieve accurate detection and classification of fall events.
Proceedings Paper
Computer Science, Artificial Intelligence
Samad Barri Khojasteh, Jose R. Villar, Enrique de la Cal, Victor M. Gonzalez, Javier Sedano
INTERNATIONAL JOINT CONFERENCE SOCO'18-CISIS'18- ICEUTE'18
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Samad Barri Khojasteh, Jose R. Villar, Enrique de la Cal, Victor M. Gonzalez, Javier Sedano, Harun Resit Yazgan
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS (HAIS 2018)
(2018)
