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optimization models for disaster response operations: a literature review

OR SPECTRUM 2024年第3期46卷 737-783页

作者： Kamyabniya, Afshin Saure, Antoine Salman, F. Sibel Benichou, Noureddine Patrick, Jonathan Univ New Brunswick Fac Management 7 MacAulay Lane Fredericton NB E3B 5A3 Canada Univ Ottawa Telfer Sch Management 55 Laurier Ave East Ottawa ON K1N 6N5 Canada Koc Univ Dept Ind Engn TR-34450 Istanbul Turkiye CNR 1200 Montreal Rd Ottawa ON K1A 0R6 Canada

Disaster operations management (DOM) seeks to mitigate the harmful impact of natural disasters on individuals, society, infrastructure, economic activities, and the environment. Due to the increasing number of people affected worldwide, and the increase in weather-related disasters, DOM has become increasingly important. In this survey, we focus on the post-disaster stage of DOM that involves response operations. We review studies that propose optimization models to supporting the following four relief logistics operations: (i) relief items distribution, (ii) location of relief facilities and temporary shelters, (iii) integrated relief items distribution and shelter location, and (iv) transportation of affected population. optimization models from 127 articles published between 2013 and 2022, focusing on relief logistics operations during natural disasters, are categorized by disaster type and thoroughly analyzed. Each model provides a case study illustrating its application in addressing key relief logistics operations. We also analyse the extent to which these studies address the critical assumptions and methodological gaps identified by Galindo and Batta (Eur J Oper Res 230:201-211, 2013), Caunhye et al. (Socio-econ Plan Sci 46:4-13, 2012), and Kovacs and Moshtari (Eur J Oper Res 276:395-408, 2019) and the neglected research directions noted by the authors of other relevant review papers. Based on our findings, we provide avenues for potential future research. Our analysis shows a slow increase in the total number of papers published until 2018-2019 and a sharp decrease afterwards, the latter most likely as a consequence of the COVID-19 pandemic. More than half of the papers in our selection concern earthquakes while less than ten papers deal with wildfires, cyclones, or tsunamis. The majority of the stochastic optimization models consider uncertainty in the demand and supply of relief items, while some other crucial sources of uncertainty such as funding availab

关键词： Disaster operations management Natural disasters Humanitarian logistics optimization models

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Comparative Study of Portfolio optimization models for Cryptocurrency and Stock Markets

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IEEE ACCESS 2025年 13卷 49861-49873页

作者： Alidaee, Bahram Wang, Haibo Wang, Wendy Univ Mississippi Sch Business Adm University MS 38677 USA Texas A&M Int Univ AR Sanchez Jr Sch Business Laredo TX 78045 USA Univ North Alabama Sanders Coll Business & Technol Florence AL 35630 USA

Since the introduction of Modern Portfolio Theory (MPT) in 1952, its practical applications, associated challenges, and computational efficiency on large and high-frequency datasets, particularly datasets not used to develop and optimize the model, have drawn extensive research interest. This study has examined the performance of various portfolio models that have explored the concept of MPT on U.S. stock and cryptocurrency markets, i.e., discrete Markowitz portfolio selection (DMPS), the optimal dynamic portfolio (ODP), the binary unconstrained ODP (BUODP) with a quantum annealing solver, and the 1/N naive diversification (ND). Their performance is then compared to the indices that measure the performance of corresponding market exchange-traded funds (ETFs) for stock markets. Our findings show that the DMPS and ODP perform better than other models, delivering better returns in a shorter period. Both run significantly faster than the BUODP (with quantum annealing) with computation time of approximately 0.5 seconds for the S&P 400, 500, and 600 markets whereas BUODP takes 30 seconds;they also mitigate risk and outperform ETFs and ND model for the out-of-sample test with diversified portfolios combining NASDAQ stocks and cryptocurrencies. Furthermore, we have analyzed the impact of data with different frequency intervals, i.e., weekly, daily, hourly, and one-minute, on portfolio performance of the stock markets. The results suggest that data collection frequencies do not make differences in portfolio selections and weights. This study contributes to the advancement of portfolio theory, providing insights and practical values, especially in addressing computational efficiency for high-frequency and large-scale datasets and saving computational costs.

关键词： Portfolios Cryptocurrency Computational modeling Stock markets optimization Security Finance Quantum annealing Annealing optimization models Discrete Markowitz portfolio selection (DMPS) optimal dynamic portfolio (ODP) model quantum annealing solver US stock and cryptocurrency market

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Cost efficiency in water supply systems: An applied review on optimization models for the pump scheduling problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第1期323卷 1-19页

作者： Bras, Marlene Moura, Ana Andrade-Campos, Antonio Univ Aveiro Dept Econ Management Ind Engn & Tourism Res Unit Governance Competitiveness & Publ Pol GOV Aveiro Portugal Univ Aveiro Ctr Mech Technol & Automat DEM TEMA Dept Mech Engn Aveiro Portugal Intelligent Syst Associate Lab LASI Guimaraes Portugal

The need for efficient pump operation in water supply systems (WSS) has become increasingly important over time, driven by the growing energy consumption and the associated energy costs. Forecasts for 2050 anticipate a global increase in water demand by 55%, indicating an increasing surge in WSS energy consumption. Control of pumping stations, which consume 70% of the energy in WSS, is the most critical area for optimization. This optimization challenge is commonly referred as the pump scheduling problem (PSP), and can be addressed using a variety of mathematical formulations. While numerous formulations exist to solve this optimization problem, the large majority of the studies are focus on the optimization techniques, sidelining the problem formulation. Due to the unique physical characteristics of each WSS, individual mathematical formulations may exhibit different levels of performance. In addition to general pumps' operation optimization, the employment of variable speed pumps (VSP) can lead to significant energy savings compared to fixed speed pumps (FSP). However, despite their apparent benefits, many established optimization models for the PSP have not yet incorporated VSP decision variables into their formulations. Therefore, this work aims to review the main mathematical formulations for the pump scheduling problem for WSS with VSP and to present a quantitative comparative study of three mathematical formulations applied to a case study in the literature. The comparative analysis here presented revealed that the optimization model based on duty cycles is more cost-efficient when compared to alternative approaches discussed in the literature.

关键词： Nonlinear programming Linear programming Water supply systems Pump scheduling problem optimization models

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ASP-DRL: A Novel Framework for Unifying IoT Energy Usage Flexibilities Characterized by Neural Networks and optimization models

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IEEE INTERNET OF THINGS JOURNAL 2025年第2期12卷 2251-2264页

作者： Qian, Tao Fang, Mingyu Zhu, Yongxu Liang, Zeyu Huang, Yuxiong Hu, Qinran Wu, Zaijun Southeast Univ Sch Elect Engn Nanjing 210000 Peoples R China Southeast Univ Jiangsu Key Lab Smart Grid Technol & Equipment Nanjing 210000 Peoples R China Southeast Univ Natl Commun Res Lab Nanjing 210096 Peoples R China Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China

Managing energy usage flexibility has been identified as an effective way to coordinate Internet of Things (IoT) technologies and transition to a low-carbon future. However, the various models of these flexibilities may not be fully understood, and some may even be characterized by closed-box neural networks, such as those used for electric vehicles (EVs) charging. In this article, we propose a novel framework called augmented shadow-price deep reinforcement learning (ASP-DRL) for the online, distributed management of IoT under multiple sources of uncertainties, including renewable energy sources (RESs), wholesale electricity prices, and EV behavior patterns. To address these challenges, the proposed framework combines scheduling mechanisms for neural networks and optimization models to maximize total social welfare. Within the ASP-DRL framework, the policy network adaptively learns about system uncertainties and delivers actions to different distributed entities, either to form augmented objective functions for optimization models or to guide neural networks. We also present a distribution correction algorithm that enhances the vanilla soft Actor-Critic method with attention-based maximal corrective feedback, resulting in faster convergence and better performance. Our numerical studies demonstrate the superiority of the proposed ASP-DRL framework compared to conventional deep reinforcement learning (DRL) and optimization-based approaches.

关键词： Uncertainty Neural networks Internet of Things optimization models Electric vehicle charging Stochastic processes Energy management Microgrids Renewable energy sources Electricity Deep reinforcement learning (DRL) electric vehicles (EVs) energy management Internet of Things (IoT) neural networks

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Minimum Adjustment Consensus optimization models With Fuzzy Preference Relations: The Perspective of Cardinal and Ordinal Consensus

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2025年第2期33卷 680-693页

作者： Liu, Zhengmin Wang, Wenxin Ding, Ruxue Liu, Peide Shandong Univ Finance & Econ Sch Management Sci & Engn Jinan 250014 Peoples R China

In group decision-making (GDM), traditional consensus models have primarily focused on cardinal consensus. In reality, irrespective of whether the objective of GDM is to select the optimal alternative or to rank alternatives, it is imperative to establish a ranking that garners the utmost assent from all decision-makers (DMs). When preferences are articulated through fuzzy preference relations (FPRs), cardinal information emerges in numerical form, quantifying the degree of preference for alternatives, while ordinal relations are implicitly embedded within pairwise comparisons. To delve into both cardinal and ordinal consensus among DMs, this study introduces two consensus optimization models that strive to minimize adjustments to FPRs while fostering consensus in terms of preference intensity and ranking. To this end, we first propose two ordinal consensus measurement methods: one precisely discerns whether DMs have achieved consensus on the selection of the best alternative, while the other assesses the consistency of different preference rankings, taking into account the importance of positions. Based on these methods, two systems of inequalities are designed to explicitly govern both types of ordinal consensus. Subsequently, two consensus control rules are formulated, tailored to distinct objectives. These rules necessitate not only cardinal consensus among all DMs, but also their alignment in terms of either the selection of the best alternative or the preference ranking. Ultimately, these rules are integrated as constraints into two mixed-integer programming models aimed at minimizing preference adjustments. The proposed models have been applied in a case study, confirming their practicality, with thorough comparative analyses demonstrating their effectiveness.

关键词： Biological system modeling optimization models Consensus control optimization Fuzzy systems Economics Rendering (computer graphics) Position measurement Object recognition Numerical models Cardinal and ordinal consensus fuzzy preference relation (FPR) group decision-making (GDM) minimum adjustment mixed-integer programming model

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optimization models in Cyber-Physical Power Systems: A Review

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IEEE ACCESS 2022年 10卷 130469-130486页

作者： Karamdel, Shahab Liang, Xiaodong Faried, Sherif O. Mitolo, Massimo Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK Canada Irvine Valley Coll Irvine CA USA

The growing interconnection between information networks and power grids enables a more efficient and economical operation, but also introduces significant challenges in modern cyber-physical power systems, such as malicious cyber-attacks. Several cyber-attacks have been reported in the recent decade, affecting hundreds of thousands of people. These attacks can lead to large power system blackouts, and optimization models are essential tools for optimal decision-making in reliable and secure operation of cyber-physical power systems. In this paper, optimization models in cyber-physical power systems are extensively reviewed and classified based on their applications, including cyber-security and optimal operation. One major application of optimization is in cyber-security evaluation of smart grids. This paper investigates the models to implement cyber-attacks against state estimation, coordinated cyber-physical attacks aiming to cover each other, and financially-motivated attacks in electricity markets;optimal defense strategies and interactions between the attacker and the defender are also introduced. Furthermore, optimization models used in the operation and dispatching of cyber-physical power and energy systems, optimal routing of information networks, and privacy-preserving models are presented. Finally, as solving optimization models is a crucial step in optimal decision-making, solvers in the literature are also introduced.

关键词： Cyber-physical power systems cyber-security optimal operation optimization models smart grids

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Current trends in single-thermodynamic optimization models towards sustainable designs of central concentrating solar plants

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SOLAR ENERGY 2024年 279卷

作者： Levario-Medina, S. Valencia-Ortega, G. UP Zacatenco Escuela Super Computo Dept Formac Bas Inst Politecn Nacl Ave Miguel Bernal Esq Juan Dios Batiz Mexico City 07738 Mexico Univ Nacl Autonoma Mexico Fac Estudios Super Acatlan Div Matemat Ingn Ave Alcanfores & San Juan Totoltepec Santa Cruz Acatlan 53150 Estado Mexico Mexico

One of the main problems in current numerical and simulation methodologies, for optimizing the demand- generation of electricity by concentrating solar power plants, is the lack of thermodynamic parameters which involves the non-equilibrium energy conversion. In this work, the fundamentals of heat transport and energy conversion features are exposed, within the context of the Finite-Time Thermodynamics. From a radiative-conductive heat engine model, three real-world solar concentrating power plants are analyzed, by characterizing some of their less dissipative operation modes via a generalized thermodynamic function called k -efficient power, since it leads us to establish general trade-offs between the operation modes and internal/external design features of indirect solar power systems. Two ways to establish the optimization are defined as follows: The energy reconfiguration process lies in improving the thermal storage system for Eurelios, Gemasolar and Ivanpah plants, while the internal design restructuring propose modifying the heliostats (AH)/receptor A H )/receptor area (A(R)) );for example, the receptor area should be reduced namely, Eurelios: A(R)*< 16 m2, 2 , Gemasolar: A(R)( )(* )< 251 m2 2 and Ivanpah: A(R)* < 4 , 018 m2. 2 . These optimal processes could be fed back into future works using exergoeconomic and exergoenvironmental analysis.

关键词： Nonequilibrium thermodynamics Energetic analysis optimization models Solar concentrators

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Sustainable closed-loop supply chains and their optimization models: a review of the literature

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CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2024年第4期26卷 999-1023页

作者： Lozano-Oviedo, Jonathan Cortes, Cristian E. Rey, Pablo A. Univ Chile Programa Doctorado Sistemas Ingn Santiago Chile Univ Chile Dept Ingn Civil Santiago Chile Inst Sistemas Complejos Ingn ISCI Santiago Chile Univ Tecnol Metropolitana Fac Ingn Dept Ind Santiago Chile Univ Tecnol Metropolitana Inst Univ Invest & Desarrollo Tecnol Santiago Chile

Sustainability and the circular economy have become indispensable attributes of the supply chains of any industry or field of action in the effort to prevent damage to the environment and its negative economic and social impacts. Currently, most supply chains have focused on maximizing economic returns, neglecting the environmental and social impacts generated from the acquisition/processing of raw materials until the supply of products in the market and the post-consumer stage. An emerging alternative for achieving these attributes is the sustainable closed-loop supply chain (SCLSC). This paper offers a critical review of the literature on SCLSCs, their formulation as mathematical problems and the associated optimization models. No literature reviews were found with the focus, structure, and scope proposed in the present article, given that most publications associated with SCLSCs have a focus on the managerial perspective, including different topics, such as green supply chains, sustainable linear supply chains, reverse logistics management, collaborative practices, qualitative reviews of the effects of sustainability and circular economy. This review aims to identify gaps in the existing studies and the consequent opportunities for advancing the frontier of knowledge in future research on SCLSC optimization. We find that although studies for various specific industries that propose SCLSC optimization models have been published, they focus mainly on the economic and environmental dimensions of sustainability and with varying degrees of success, generally neglecting the social dimension. Furthermore, not all the technical cycles of the circular economy are dealt with in the literature in sufficient depth or detail, and, overall, the proposed models do not consider frameworks of reference in terms of economic, environmental, and social sustainability.

关键词： Sustainable closed-loop supply chain optimization models Circular economy Literature review

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Methodology for Deriving Parameters for optimization models of Systems of Flexible Energy Resources

IEEE OPEN JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY

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IEEE OPEN JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY 2024年 5卷 737-757页

作者： Wagner, Lukas Peter Fay, Alexander Helmut Schmidt Univ Inst Automat Technol D-22043 Hamburg Germany Ruhr Univ Chair Automat D-44801 Bochum Germany

The increasing share of renewable energy generation poses a challenge to maintaining the adequacy of power generation and demand. Planning the operation of flexible energy resources helps stabilize this balance. optimization models are needed for operation planning. The use of a predefined and validated optimization model avoids modeling errors, but parameterization is still necessary and error-prone. This work presents a methodology for determining parameters of optimization models for various kinds of flexible energy resources. The parameters are derived from time series data of the resource operation after preprocessing. This methodology includes algorithms for determining operational boundaries, the input-output relationship, system states, and parameters for storage systems. Connections of flows between individual resources within a system are extracted from a standardized information model. A case study of a combined heat and power system demonstrates the applicability of the methodology by deriving a set of parameters from time series data and an information model of the system's structure. The model parameterized by means of the methodology shows very good alignment with a validation time series data set with a normalized root mean square error of 1% (generator), respectively, of 6% (heat exchanger).

关键词： Energy resources optimization models Time series analysis Data models Industrial electronics optimization Load modeling Energy flexibility parameter derivation optimization model

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optimization models and solving approaches in relief distribution concerning victims' satisfaction: A review

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APPLIED SOFT COMPUTING 2023年 143卷

作者： Luo, Jia Shi, Lei Xue, Rui El-baz, Didier Beijing Univ Technol Coll Econ & Management Beijing 100124 Peoples R China Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China Univ Toulouse CNRS LAAS F-31400 Toulouse France

Relief distribution is one of the most widely studied topics in the domain of emergency logistics. optimization models and solving approaches have become one of the most powerful tools for tackling relief distribution problems. In this context, victims' satisfaction should be considered as one significant indicator to evaluate relief distribution operations. Therefore, this survey addresses some of the most representative publications working with optimization models and solving approaches in relief distribution concerning victims' satisfaction. Firstly, collected models are discussed from the commonly used objectives for describing victims' satisfaction: the shortest distribution time, the lowest unmet demand, and the maximum fairness. Second, gathered solving approaches are analyzed from exact algorithms, heuristic algorithms, and machine learning algorithms respectively. Heuristic algorithms are further studied into four groups: genetic algorithm, ant colony optimization, particle swarm optimization, and others. Finally, development trends of models and approaches in relief distribution concerning victims' satisfaction and potential interest in the cross-disciplinary are showcased.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Relief distribution Victims' satisfaction optimization models Solving approaches Survey

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