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A stochastic optimization model for patient evacuation from health care facilities during hurricanes

INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2024年 108卷

作者： Kim, Kyoung Yoon Toplu-Tutay, Gizem Kutanoglu, Erhan Hasenbein, John J. Univ Texas Austin Operat Res & Ind Engn Austin TX 78712 USA

We propose a rigorous modeling and methodological effort that integrates statistical implementation of hydrology models in predicting inland and coastal flood scenarios due to hurricanes and a scenario -based stochastic integer programming model which suggests resource and staging area decisions in the first stage and the evacuation decisions in the second stage. This novel study combines physics -based flood prediction models and stochastic optimization for largescale multi -facility coordination of hospital and nursing home evacuations before impending hurricanes. The optimization model considers scenario -dependent evacuation demand, transport vehicles with varying capacities, and both critical and non -critical patients. Utilizing Hurricane Harvey of 2017 as a case study and actual healthcare facility locations in southeast Texas, we explore various evacuation policies, demonstrating the impact of routing strategies, staging area decisions, flood thresholds, and receiving facility capacities on evacuation outcomes. One of the findings is that choosing staging area(s) and deploying evacuation vehicles optimally considering the uncertainty of the hurricane's path at the time of decision making could have significant effect on the total cost of the operation and evacuation time experienced by the evacuees. We also show the non -negligible value of the scenario -based staging and routing solution conservatively calculated in relation to a single scenario solution using the concept of value of stochastic solution.

关键词： Hurricanes Flood scenarios Hospital evacuation two-stage stochastic programming

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Electric bus charging facility planning with uncertainties: Model formulation and algorithm design

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2023年第1期150卷

作者： Zhou, Yu Ong, Ghim Ping Meng, Qiang Cui, Haipeng Natl Univ Singapore Dept Civil & Environm Engn Singapore 117576 Singapore Shenzhen Univ Coll Civil & Transportat Engn Shenzhen 518060 Peoples R China

This paper investigates the electric bus charging facility planning (EB-CFP) problem for a bus transit company operating a heterogeneous electric bus (EB) fleet to provide public transportation services, taking into account uncertainties in both EB travel time and battery degradation. The goal of the EB-CFP problem is to determine the number and type of EB chargers that should be deployed at bus terminals and depots to meet daily EB charging demand while minimizing total cost. The problem is formulated as a two-stage stochastic programming model, with the first stage determining the EB charger deployment scheme and the second stage estimating the EB daily operational cost with respect to a predetermined EB trip timetable for a given EB charger deployment scheme. To effectively address the second stage problem, a multi -agent EB transit simulation system that mimics the daily EB operation process is developed. We then design two heuristic methods, the reinforcement learning (RL)-based method and the surrogate-based optimization (SBO), that use the developed multi-agent EB transit simulation system to solve the two-stage stochastic programming model for large-scale instances. Lastly, we run a series of experiments on a fictitious EB transit network and a real-world EB transit network in Singapore to evaluate the performance of the models and algorithms. In order to improve the performance of the EB transit system, some managerial insights are also provided to urban bus transit companies.

关键词： Electric bus Charging facility deployment two-stage stochastic programming Multi-agent simulation Reinforcement learning Surrogate-based optimization

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Evaluation of network expansion decisions for resilient interdependent critical infrastructures with different topologies

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INTERNATIONAL JOURNAL OF CRITICAL INFRASTRUCTURE PROTECTION 2023年 42卷

作者： Tiong, Achara Vergara, Hector A. Oregon State Univ Sch Mech Ind & Mfg Engn 204 Rogers Hall Corvallis OR 97331 USA

Resilient interdependent critical infrastructures (CIs) can better withstand cascading failures in disruptive events. This study proposes network expansion as a resilience improvement strategy for interdependent CIs and evaluates the influence of topology in interdependent network design for resilience optimization under disruption uncertainty. A resilience score consisting of network complexity and unmet demand metrics is introduced to quantify the resilience of expanded networks. Five synthetic interdependent network instances with random and hub-and-spoke (i.e., cluster) topologies are generated to represent CIs with heterogeneous node functions. Different network expansion opportunities are considered and critical node disruption scenarios are used to evaluate the impact of uncertain disruptions. We apply a two-stage stochastic multi-objective resilience optimization model to determine strategic investment decisions using the expected total cost and expected resilience score as competing objectives. Compromise solutions of expanded network designs are identified from Pareto optimal solutions and they are characterized according to their graph properties. The results show that expanded networks have improved resilience and the extent of improvement is affected by the network topology and type of disruption. Under critical node disruptions, a random network is more resilient than a hub-and-spoke structure due to its better connectivity. Characteristics of highly connected interdependent networks are high average node degree, high clustering coefficient, and low average shortest path length. Resilience improvement is more limited in expanded networks with a hub-and-spoke structure due to the negative impact of hub failures.

关键词： Interdependent networks Resilience Network expansion two-stage stochastic programming Multi-objective optimization Network topology

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Home healthcare staff dimensioning problem for temporary caregivers: A matheuristic solution approach

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COMPUTERS & OPERATIONS RESEARCH 2023年第1期152卷

作者： Nikzad, Erfaneh Bashiri, Mahdi Abbasi, Babak Shahed Univ Fac Engn Ind Engn Dept Tehran Iran Coventry Univ Ctr Business Soc Coventry England RMIT Univ Sch Accounting Informat Syst & Supply Chain Melbourne Australia

Staff dimensioning, defined as determining the required numbers of caregivers with different types of skills, is a key decision for home healthcare systems. Home healthcare providers often use a combination of permanent and temporary (casual) caregivers. Determining the required number of temporary caregivers with different skill sets considering uncertainty and routing cost is the main objective of this study. To this end, we propose a two-stage stochastic programming model for the staff dimensioning problem for temporary caregivers, taking into account uncertainties in the required class of service, the required number of visits, and the required service time for each patient. Staff dimensioning decisions are defined in the first stage, and assignment with routing are positioned in the second stage of the model. To solve the problem, a two-phase matheuristic algorithm is developed where an initial solution is generated in the first phase by using an intermediate mathematical model and solving a series of Traveling Salesman Problems (TSPs), then a fix-and-optimize strategy is developed in the second phase to improve the obtained solution. The efficiency of the proposed matheuristic algorithm is examined by various test problems. The results highlight that the proposed model and solution method can be used by HHC providers to effectively utilize the option of recruitment of temporary caregivers in their resource planning considering inevitable uncertain parameters.

关键词： Home health care Staff dimensioning Matheuristic algorithm two-stage stochastic programming

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Supplier Selection for Agriculture Industry Under Uncertainty: Machine Learning Based Sample Average Approximation Method 2nd

Supplier Selection for Agriculture Industry Under Uncertaint...

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2nd International Conference on Artificial Intelligence: Towards Sustainable Intelligence, AI4S 2024

作者： Yılmaz, Ömer Faruk Yılmaz, Beren Gürsoy Department of Industrial Engineering Karadeniz Technical University Trabzon61080 Turkey Department of Industrial and Systems Engineering University of Florida Gainesville32608 United States

ISBN: (纸本)9783031813689

This paper addresses the problem of supplier selection for a hazelnut producer in the agriculture industry under the uncertainty of product quality, with the objective of cost minimization. To mathematically represent the problem, a two-stage stochastic programming model is developed to make decisions both before and after the realization of uncertainty. The first stage involves supplier selection decisions, while the second stage determines the amount of product received from each supplier. After the hazelnuts are produced, if they do not meet quality standards, customers may return the products, causing significant customer satisfaction issues and increased costs. Product quality is assessed based on four features: (I) Aflatoxin, (II) Acidity, (III) Moisture, and (IV) Sifter. To address this problem within the context of intelligent agriculture, four different scenario reduction techniques are employed: (I) Sample Average Approximation (SAA), (II) SAA combined with Machine Learning-1 (SAA + ML1), (III) SAA + ML2, and (IV) SAA + ML3. Each method utilizes the K-means clustering algorithm with specific problem-related characteristics. Computational results indicate that when the SAA approach is combined with K-means clustering and scenarios are reduced based on clustering results, better outcomes are achieved in reducing costs associated with low-quality products. Specifically, higher numbers of suppliers and higher probabilities of generating scenarios from relevant clusters contribute to improved cost reductions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Intelligent Agriculture K-means clustering Sample average approximation two-stage stochastic programming

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two-stage stochastic program for environmental resettlement decision-making

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SOCIO-ECONOMIC PLANNING SCIENCES 2024年 93卷

作者： Cilali, Buket Barker, Kash Salo, Ahti Gonzalez, Andres D. Univ Oklahoma Sch Ind & Syst Engn Norman OK 73019 USA Aalto Univ Dept Math & Syst Anal Syst Anal Lab Sch Sci Helsinki Finland

Large and irregular movements of forcibly displaced people are an escalating global issue today. As a result, forced displacement has become an international problem. It requires planning for the relocation and integration of displaced people. However, forced displacement is not a one-time event. On the contrary, it is an ongoing, long-term process with dynamic parameters. We are interested in the long-term displacement problem, especially for climate-driven cases in which people will be forced to leave uninhabitable regions to escape slow-onset climate change impacts. Creating an optimization model that could cover all the requirements and considerations of such a problem would encounter many challenges. Nonetheless, planning in advance for the upcoming climate-change-driven displacement crisis is critical. Unfortunately, this topic has not been explored sufficiently, especially in the operations research literature. To fill this gap, we focus on developing a mathematical model for the long-term relocation planning of people displaced due to climate change. In this model, uncertainty in demand is represented with various demand scenarios, demand and capacity are managed dynamically, and integration outcomes and related costs are optimized. Ultimately, the purpose of this paper is to provide decisionmakers with an initial resource for preparing for upcoming movements under uncertainty. To this end, we propose a two-stage stochastic model to determine optimal flow and location decisions. We run this model under three different demand scenarios with and without considering societal impacts measured by diversity indicators. The results show how profoundly the uncertainty in demand and differences in our focus change the future steps that should be taken.

关键词： Climate change displacement Humanitarian operations research for societal good Long-term planning two-stage stochastic programming

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Risk-averse two-stage stochastic programming with an application to disaster management

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COMPUTERS & OPERATIONS RESEARCH 2012年第3期39卷 541-559页

作者： Noyan, Nilay Sabanci Univ Mfg Syst Ind Engn Program TR-34956 Istanbul Turkey

Traditional two-stage stochastic programming is risk-neutral;that is, it considers the expectation as the preference criterion while comparing the random variables (e.g., total cost) to identify the best decisions. However, in the presence of variability risk measures should be incorporated into decision making problems in order to model its effects. In this study, we consider a risk-averse two-stage stochastic programming model, where we specify the conditional-value-at-risk (CVaR) as the risk measure. We construct two decomposition algorithms based on the generic Benders-decomposition approach to solve such problems. Both single-cut and multicut versions of the proposed decomposition algorithms are presented. We adapt the concepts of the value of perfect information (VPI) and the value of the stochastic solution (VSS) for the proposed risk-averse two-stage stochastic programming framework and define two stochastic measures on the VPI and VSS. We apply the proposed model to disaster management, which is one of the research fields that can significantly benefit from risk-averse two-stage stochastic programming models. In particular, we consider the problem of determining the response facility locations and the inventory levels of the relief supplies at each facility in the presence of uncertainty in demand and the damage level of the disaster network. We present numerical results to discuss how incorporating a risk measure affects the optimal solutions and demonstrate the computational effectiveness of the proposed methods. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： two-stage stochastic programming Conditional-value-at-risk Decomposition Value of information Value of stochastic solution Facility location Emergency supplies Disaster relief Disaster preparedness

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two-stage stochastic facility location problem with disruptions and restricted shortages

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期183卷

作者： Koca, Esra Sabanci Univ Ind Engn Program Istanbul Turkiye

In this study, we consider a reliable facility location problem where an uncertain disruptive event might affect the demand of the nodes, the allocation costs, and the available capacities at the facilities. We assume that the decision maker decides the locations and the types (size and reliability levels) of the facilities under uncertainty, while the allocation and shortage decisions are made in response to the realizations of the uncertain parameters. Motivated from a humanitarian facility location problem, we enforce an upper bound on the risk of shortages under any scenario using the popular risk measure conditional value-at-risk (CVaR). We formulate the problem as a two-stage stochastic programming model, and solve it using the L-Shaped method accelerated with additional enhancements. Our computational experiments show the value of our modeling scheme, and the effectiveness of our solution method.

关键词： Capacitated facility location problem Facility disruptions two-stage stochastic programming L-shaped method Conditional value-at-risk

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Benders Adaptive-Cuts Method for two-stage stochastic Programs

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TRANSPORTATION SCIENCE 2023年第5期57卷 1252页

作者： Ramirez-Pico, Cristian Ljubic, Ivana Moreno, Eduardo Univ Adolfo Ibanez Fac Engn & Sci Santiago 7941169 Chile ESSEC Business Sch Paris F-95021 Cergy Pontoise France

Benders decomposition is one of the most applied methods to solve two-stage stochastic problems (TSSP) with a large number of scenarios. The main idea behind the Benders decomposition is to solve a large problem by replacing the values of the second stage subproblems with individual variables and progressively forcing those variables to reach the optimal value of the subproblems, dynamically inserting additional valid constraints, known as Benders cuts. Most traditional implementations add a cut for each scenario (multicut) or a single cut that includes all scenarios. In this paper, we present a novel Benders adaptive-cuts method, where the Benders cuts are aggregated according to a partition of the scenarios, which is dynamically refined using the linear program-dual information of the subproblems. This scenario aggregation/disaggregation is based on the Generalized Adaptive Partitioning Method (GAPM), which has been successfully applied to TSSPs. We formalize this hybridization of Benders decomposition and the GAPM by providing sufficient conditions under which an optimal solution of the deterministic equivalent can be obtained in a finite number of iterations. Our new method can be interpreted as a compromise between the Benders single-cuts and multicuts methods, drawing on the advantages of both sides, by rendering the initial iterations faster (as for the single-cuts Benders) and ensuring the overall faster convergence (as for the multicuts Benders). Computational experiments on three TSSPs [the stochastic Electricity Planning, stochastic Multi Commodity Flow, and conditional value-at-risk (CVaR) Facility Location] validate these statements, showing that the new method outperforms the other implementations of Benders methods, as well as other standard methods for solving TSSPs, in particular when the number of scenarios is very large. Moreover, our study demonstrates that the method is not only effective for the risk-neutral decision makers, but also that it ca

关键词： two-stage stochastic programming Benders decomposition network flow conditional value-at-risk facility location

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Optimal Siting and Sizing of Hydrogen Production Modules in Distribution Networks with Photovoltaic Uncertainties

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ENERGIES 2023年第22期16卷 7636-7636页

作者： Li, Zhiyong Wu, Wenbin Si, Yang Chen, Xiaotao Qinghai Univ Dept Comp Technol & Applicat Xining 810016 Peoples R China Univ Qinghai Sch Energy & Elect Engn Qinghai Key Lab Efficient Utilizat Clean Energy Xining 810016 Peoples R China

Hydrogen production modules (HPMs) play a crucial role in harnessing abundant photovoltaic power by producing and supplying hydrogen to factories, resulting in significant operational cost reductions and efficient utilization of the photovoltaic panel output. However, the output of photovoltaic power is stochastic, which will affect the revenue of investing in an HPM. This paper presents a comprehensive analysis of HPMs, starting with the modeling of their operational process and investigating their influence on distribution system operations. Building upon these discussions, a deterministic optimization model is established to address the corresponding challenges. Furthermore, a two-stage stochastic planning model is proposed to determine optimal locations and sizes of HPMs in distribution systems, accounting for uncertainties. The objective of the two-stage stochastic planning model is to minimize the distribution system's operational costs plus the investment costs of the HPM subject to power flow constraints. To tackle the stochastic nature of photovoltaic power, a data-driven algorithm is introduced to cluster historical data into representative scenarios, effectively reducing the planning model's scale. To ensure an efficient solution, a Benders' decomposition-based algorithm is proposed, which is an iterative method with a fast convergence speed. The proposed model and algorithms are validated using a widely utilized IEEE 33-bus system through numerical experiments, demonstrating the optimality of the HPM plan generated by the algorithm. The proposed model and algorithms offer an effective approach for decision-makers in managing uncertainties and optimizing HPM deployment, paving the way for sustainable and efficient energy solutions in distribution systems. Sensitivity analysis verifies the optimality of the HPM's siting and sizing obtained by the proposed algorithm, which also reveals immense economic and environmental benefits.

关键词： hydrogen production two-stage stochastic programming optimal planning data-driven algorithm Benders' decomposition

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