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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 availability and donations of relief items (e.g., blood products) remain understudied. Furthermore, most of the papers in our selection fail to incorporate key characteristics of disaster relief operations such as its dynamic nature and information updates during the response phase. Finally, a large number of studies use exact commercial software to solve their models, which may not be computationally efficient or practical for large-scale problems, specifically under uncertainty.
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This research was partially supported by the National Research Council of Canada (NRC) [Grant 18122020] and by the Government of Ontario, Canada [Ontario Trillium Scholarship 711110202278].
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Afshin Kamyabniya
Telfer School of Management, University of Ottawa, 55 Laurier Avenue East, Ottawa, ON, K1N 6N5, Canada
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Department of Industrial Engineering, Koç University, 34450, Istanbul, Turkey
F. Sibel Salman
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Kamyabniya, A., Sauré, A., Salman, F.S. et al. Optimization models for disaster response operations: a literature review. OR Spectrum 46 , 737–783 (2024). https://doi.org/10.1007/s00291-024-00750-6
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DOI : https://doi.org/10.1007/s00291-024-00750-6
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In case of public emergency such as natural disasters, accidents, public health and social security, if people fail to take timely effective response measures, the consequences would be incredibly bad. So, how to optimize emergency transportation and logistics system scientifically, and enhance the emergency support and emergency response capacity of the entire emergency management and emergency system, are the keys to improve stability, reliability and timeliness of public emergency warning defense system. Many scholars have done a lot of researches in dealing with emergencies. On the base of studying a large number of related documents, this paper summarized the optimization method of emergency transportation and logistics, and analyzed the existing shortcomings, and finally came to the conclusion and future research directions.
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Advancements in deep learning techniques for time series forecasting in maritime applications: a comprehensive review.
2. literature collection procedure.
3.1. artificial neural network (ann), 3.1.1. multilayer perceptron (mlp)/deep neural networks (dnn), 3.1.2. wavenet, 3.1.3. randomized neural network, 3.2. convolutional neural network (cnn), 3.3. recurrent neural network (rnn), 3.3.1. long short-term memory (lstm), 3.3.2. gated recurrent unit (gru), 3.4. attention mechanism (am)/transformer, 3.5. overview of algorithms usage, 4. time series forecasting in maritime applications, 4.1. ship operation-related applications, 4.1.1. ship trajectory prediction, 4.1.2. meteorological factor prediction, 4.1.3. ship fuel consumption prediction, 4.1.4. others, 4.2. port operation-related applications, 4.3. shipping market-related applications, 4.4. overview of time series forecasting in maritime applications, 5. overall analysis, 5.1. literature description, 5.1.1. literature distribution, 5.1.2. literature classification, 5.2. data utilized in maritime research, 5.2.1. automatic identification system data (ais data), 5.2.2. high-frequency radar data and sensor data, 5.2.3. container throughput data, 5.2.4. other datasets, 5.3. evaluation parameters, 5.4. real-world application examples, 5.5. future research directions, 5.5.1. data processing and feature extraction, 5.5.2. model optimization and application of new technologies, 5.5.3. specific application scenarios, 5.5.4. practical applications and long-term predictions, 5.5.5. environmental impact, fault prediction, and cross-domain applications, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.
Click here to enlarge figure
Ref. | Architecture | Dataset | Advantage |
---|---|---|---|
[ ] | MSCNN-GRU-AM | HF radar | It is applicable for high-frequency radar ship track prediction in environments with significant clutter and interference |
[ ] | CNN-BiLSTM-Attention | 6L34DF dual fuel diesel engine | The high prediction accuracy and early warning timeliness can provide interpretable fault prediction results |
[ ] | LSTM | Two LNG carriers | Enables early anomaly detection in new ships and new equipment |
[ ] | LSTM | sensors | better and high-precision effects |
[ ] | Self-Attention-BiLSTM | A real military ship | Not only can it better capture complex ship attitude changes, but it also shows greater accuracy and stability in long-term forecasting tasks |
[ ] | CNN–GRU–AM | A C11 containership | better accuracy of forecasting |
[ ] | GRU | A scaled model test | good prediction accuracy |
[ ] | CNN | A bulk carrier | good prediction accuracy |
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Wang, M.; Guo, X.; She, Y.; Zhou, Y.; Liang, M.; Chen, Z.S. Advancements in Deep Learning Techniques for Time Series Forecasting in Maritime Applications: A Comprehensive Review. Information 2024 , 15 , 507. https://doi.org/10.3390/info15080507
Wang M, Guo X, She Y, Zhou Y, Liang M, Chen ZS. Advancements in Deep Learning Techniques for Time Series Forecasting in Maritime Applications: A Comprehensive Review. Information . 2024; 15(8):507. https://doi.org/10.3390/info15080507
Wang, Meng, Xinyan Guo, Yanling She, Yang Zhou, Maohan Liang, and Zhong Shuo Chen. 2024. "Advancements in Deep Learning Techniques for Time Series Forecasting in Maritime Applications: A Comprehensive Review" Information 15, no. 8: 507. https://doi.org/10.3390/info15080507
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IMAGES
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Abstract. Optimization modeling has become a powerful tool to tackle emergency logistics problems since its first adoption in maritime disaster situations in the 1970s. Using techniques of content analysis, this paper reviews optimization models utilized in emergency logistics. Disaster operations can be performed before or after disaster ...
Abstract. Optimization modeling has become a powerful tool to tackle emergency logistics problems since its first adoption in maritime disaster situations in the 1970s. Using techniques of content ...
An object-oriented modeling frame of emergency logistics scheduling based on disruptions is put forward and supply a new perspective for the research of theory and decision support application in the disposal of disruptions and real time controlling during logistics distribution. Expand. 3.
Caunhye AM, Nie X, Pokharel S. Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sciences . 2012 Mar 1;46(1):4-13. Epub 2011 Apr 30. doi: 10.1016/j.seps.2011.04.004
Downloadable (with restrictions)! Author(s): Caunhye, Aakil M. & Nie, Xiaofeng & Pokharel, Shaligram. 2012 Abstract: Optimization modeling has become a powerful tool to tackle emergency logistics problems since its first adoption in maritime disaster situations in the 1970s. Using techniques of content analysis, this paper reviews optimization models utilized in emergency logistics.
Optimization models in emergency logistics: A literature review. Aakil M. Caunhye, Xiaofeng Nie and Shaligram Pokharel. Socio-Economic Planning Sciences, 2012, vol. 46, issue 1, 4-13 . Abstract: Optimization modeling has become a powerful tool to tackle emergency logistics problems since its first adoption in maritime disaster situations in the 1970s.
Optimization modeling has become a powerful tool to tackle emergency logistics problems since its first adoption in maritime disaster situations in the 1970s. Using techniques of content analysis, this paper reviews optimization models utilized in emergency logistics. Disaster operations can be performed before or after disaster occurrence.
Check access options. Date 2012. Author Caunhye, Aakil M. Nie, Xiaofeng Pokharel, Shaligram
Optimization models in emergency logistics: A literature review. Author(s): ...
Abstract. Optimization modeling has become a powerful tool to tackle emergency logistics problems since its first adoption in maritime disaster situations in the 1970s. Using techniques of content analysis, this paper reviews optimization models utilized in emergency logistics. Disaster operations can be performed before or after disaster ...
Optimization modeling has become a powerful tool to tackle emergency logistics problems since its first adoption in maritime disaster situations in the 1970s. Using techniques of content analysis, this paper reviews optimization models utilized in emergency logistics. Disaster operations can be performed before or after disaster occurrence. Short-notice evacuation, facility location, and stock ...
摘要:. Optimization modeling has become a powerful tool to tackle emergency logistics problems since its first adoption in maritime disaster situations in the 1970s. Using techniques of content analysis, this paper reviews optimization models utilized in emergency logistics. Disaster operations can be performed before or after disaster ...
This paper proposes a dynamic optimization model for emergency logistics that takes multiple periods, frequencies, and types into account, in contrast to traditional emergency logistics, which only consider a single type of emergency material and a single period. ... Section 2 presents the literature review. Section 3 introduces the assessment ...
optimization models for emergency response and post-disaster delivery logistics: a review of current approaches August 2020 International Journal of Engineering Technologies and Management ...
Liberatore et al. [11] presented the main concepts used in emergency and disaster management through a review of the literature related to models and support systems for aid decisions applied to humanitarian logistics, classifying works according to the phase considered in the disaster management, and the specific problem addressed. They also ...
Socio-Economic Planning Sciences, 46 (1), 4-13. Optimization modeling has become a powerful tool to tackle emergency logistics problems since its first adoption in maritime disaster situations in the 1970s. Using techniques of content analysis, this paper reviews optimization models utilized in emergency logistics.
Around 19 papers in our literature review applied robust optimization methods to model uncertainty by restricting the uncertain parameters to uncertainty sets and optimizing over the worst-case scenario within that restricted set. ... (2012) Optimization models in emergency logistics: a literature review. Socio-econ Plan Sci 46:4-13. Google ...
Downloadable (with restrictions)! Optimization modeling has become a powerful tool to tackle emergency logistics problems since its first adoption in maritime disaster situations in the 1970s. Using techniques of content analysis, this paper reviews optimization models utilized in emergency logistics. Disaster operations can be performed before or after disaster occurrence.
Notice that the above literature review related to developing a model for emergency logistics management mostly focuses on minimizing logistics cost, travel time, travel distance, operations risk, survivors, restored paths, pre-positioning of relief supplies, and evacuation (Chang et al., 2007; Li et al., 2012; Najafi et al., 2013, Yi and Kumar ...
Literature Review 2.1. Emergency Logistics Information System Decision-Making ... An emergency vehicle path optimization model with a soft time window is established,
In case of public emergency such as natural disasters, accidents, public health and social security, if people fail to take timely effective response measures, the consequences would be incredibly bad. So, how to optimize emergency transportation and logistics system scientifically, and enhance the emergency support and emergency response capacity of the entire emergency management and ...
Optimization modeling has become a powerful tool to tackle emergency logistics problems since its first adoption in maritime disaster situations in the 1970s. Using techniques of content analysis, this paper reviews optimization models utilized in emergency logistics. Disaster operations can be performed before or after disaster occurrence.
The maritime industry is integral to global trade and heavily depends on precise forecasting to maintain efficiency, safety, and economic sustainability. Adopting deep learning for predictive analysis has markedly improved operational accuracy, cost efficiency, and decision-making. This technology facilitates advanced time series analysis, vital for optimizing maritime operations. This paper ...
Hence, to address these ever-increasing emergency and humanitarian challenges, an integrated emergency logistics system and beneficiary-centric model grounded on behavioral and intelligent operations planning and management is the need of the hour. The literature review conducted in this paper has two major purposes.