optimization models in emergency logistics a literature review

The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.

• Login or register account

INFONA - science communication portal

• advanced search
• conferences
• Collections

Review Optimization models in emergency logistics: A literature review $("#expandableTitles").expandable();

• Contributors

Fields of science

• Bibliography

Socio-Economic Planning Sciences > 2012 > 46 > 1 > 4-13

Identifiers

User assignment

Assignment remove confirmation

You're going to remove this assignment. are you sure.

Aakil M. Caunhye

• Division of Systems and Engineering Management, School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore

Xiaofeng Nie

Shaligram pokharel.

• Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, P.O. Box 2713, Doha, Qatar

Emergency logistics Optimization Modeling Operations

Additional information

• Read online
• Add to read later
• Add to collection
• Add to followed

Export to bibliography

• Terms of service

Accessibility options

• Report an error / abuse

Reporting an error / abuse

Sending the report failed.

Submitting the report failed. Please, try again. If the error persists, contact the administrator by writing to [email protected].

You can adjust the font size by pressing a combination of keys:

• CONTROL + + increase font size
• CONTROL + – decrease font

You can change the active elements on the page (buttons and links) by pressing a combination of keys:

• TAB go to the next element
• SHIFT + TAB go to the previous element

Optimization models in emergency logistics: A literature review

AM Caunhye ， X Nie ， S Pokharel

展开 

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 pre-positioning are drafted as the main pre-disaster operations, while relief distribution and casualty transportation are categorized as post-disaster operations. According to these operations, works in the literature are broken down into three parts: facility location, relief distribution and casualty transportation, and other operations. For the first two parts, the literature is structured and analyzed based on the model types, decisions, objectives, and constraints. Finally, through the content analysis framework, several research gaps are identified and future research directions are proposed.

Emergency logistics Optimization Modeling Operations

10.1016/j.seps.2011.04.004

通过 文献互助 平台发起求助，成功后即可免费获取论文全文。

我们已与文献出版商建立了直接购买合作。

你可以通过身份认证进行实名认证，认证成功后本次下载的费用将由您所在的图书馆支付

您可以直接购买此文献，1~5分钟即可下载全文，部分资源由于网络原因可能需要更长时间，请您耐心等待哦~

百度学术集成海量学术资源，融合人工智能、深度学习、大数据分析等技术，为科研工作者提供全面快捷的学术服务。在这里我们保持学习的态度，不忘初心，砥砺前行。 了解更多>>

©2024 Baidu 百度学术声明 使用百度前必读

• Show simple item record
• Show full item record
• Export item record

SCOPUS TM     Citations 1

Page view(s) 5.

Google Scholar TM

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.

Optimization models for disaster response operations: a literature review

• Original Article
• Published: 10 March 2024
• Volume 46 , pages 737–783, ( 2024 )

Cite this article

• Afshin Kamyabniya 1 ,
• Antoine Sauré 2 ,
• F. Sibel Salman 3 ,
• Noureddine Bénichou 4 &
• Jonathan Patrick 2  

1086 Accesses

2 Citations

Explore all metrics

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

A Comparative Study of Multiple Objectives for Disaster Relief Logistics

Facilities Location Under Risk Mitigation Concerns

From Preparedness to Recovery: A Tri-Level Programming Model for Disaster Relief Planning

Explore related subjects.

• Artificial Intelligence

Abazari SR, Aghsami A, Rabbani M (2021) Prepositioning and distributing relief items in humanitarian logistics with uncertain parameters. Socio-Econ Plan Sci 74:100933

Google Scholar  

Acar M, Kaya O (2019) A healthcare network design model with mobile hospitals for disaster preparedness: a case study for Istanbul earthquake. Transp Res Part E Logist Transp Rev 130:273–292

Ahmadi M, Seifi A, Tootooni B (2015) A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: a case study on San Francisco district. Transp Res Part E Logist Transp Rev 75:145–163

Akbarpour M, Torabi SA, Ghavamifar A (2020) Designing an integrated pharmaceutical relief chain network under demand uncertainty. Transp Res Part E Logist Transp Rev 136:101867

Alem D, Clark A, Moreno A (2016) Stochastic network models for logistics planning in disaster relief. Eur J Oper Res 255:187–206

Altay N, Green WG III (2006) OR/MS research in disaster operations management. Eur J Oper Res 175:475–493

Amideo AE, Scaparra MP, Kotiadis K (2019) Optimising shelter location and evacuation routing operations: the critical issues. Eur J Oper Res 279:279–295

An S, Cui N, Li X, Ouyang Y (2013) Location planning for transit-based evacuation under the risk of service disruptions. Transp Res Part B Methodol 54:1–16

Anaya-Arenas AM, Renaud J, Ruiz A (2014) Relief distribution networks: a systematic review. Ann Oper Res 223:53–79

Baharmand H, Comes T, Lauras M (2019) Bi-objective multi-layer location-allocation model for the immediate aftermath of sudden-onset disasters. Transp Res Part E Logist Transp Rev 127:86–110

Baharmand H, Comes T, Lauras M (2020) Supporting group decision makers to locate temporary relief distribution centres after sudden-onset disasters: a case study of the 2015 Nepal earthquake. Int J Disaster Risk Reduct 45:101455

Balcik B, Beamon BM, Krejci CC, Muramatsu KM, Ramirez M (2010) Coordination in humanitarian relief chains: practices, challenges and opportunities. Int J Prod Econ 126:22–34

Baskaya S, Ertem MA, Duran S (2017) Pre-positioning of relief items in humanitarian logistics considering lateral transhipment opportunities. Socio-Econ Plan Sci 57:50–60

Bayram V (2016) Optimization models for large scale network evacuation planning and management: a literature review. Surv Oper Res Manag Sci 21:63–84

Behl A, Dutta P (2019) Humanitarian supply chain management: a thematic literature review and future directions of research. Ann Oper Res 283:1001–1044

Bozorgi-Amiri A, Jabalameli MS, Al-e Hashem SM (2013) A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR Spectr 35:905–933

Caunhye AM, Nie X, Pokharel S (2012) Optimization models in emergency logistics: a literature review. Socio-econ Plan Sci 46:4–13

Cavdur F, Kose-Kucuk M, Sebatli A (2016) Allocation of temporary disaster response facilities under demand uncertainty: an earthquake case study. Int J Disaster Risk Reduct 19:159–166

Chen AY, Yu TY (2016) Network based temporary facility location for the emergency medical services considering the disaster induced demand and the transportation infrastructure in disaster response. Transp Res Part B Methodol 91:408–423

Cheraghi S, Hosseini-Motlagh SM (2020) Responsive and reliable injured-oriented blood supply chain for disaster relief: a real case study. Ann Oper Res 291:129–167

Davis LB, Samanlioglu F, Qu X, Root S (2013) Inventory planning and coordination in disaster relief efforts. Int J Prod Econ 141:561–573

Doan XV, Shaw D (2019) Resource allocation when planning for simultaneous disasters. Eur J Oper Res 274:687–709

Dönmez Z, Kara BY, Karsu Ö, Saldanha-da Gama F (2021) Humanitarian facility location under uncertainty: critical review and future prospects. Omega 102:102393

Douris J, Kim G (2021) The atlas of mortality and economic losses from weather, climate and water extremes (1970–2019)

Eriskin L, Karatas M (2022) Applying robust optimization to the shelter location-allocation problem: a case study for Istanbul. Ann Oper Res 1–47

Escudero LF, Garín MA, Monge JF, Unzueta A (2018) On preparedness resource allocation planning for natural disaster relief under endogenous uncertainty with time-consistent risk-averse management. Comput Oper Res 98:84–102

Espejo-Díaz JA, Guerrero WJ (2021) A multiagent approach to solving the dynamic postdisaster relief distribution problem. Oper Manag Res 14:177–193

Faghih-Mohammadi F, Nasiri MM, Konur D (2022) Cross-dock facility for disaster relief operations. Ann Oper Res 1–42

Fazli-Khalaf M, Khalilpourazari S, Mohammadi M (2019) Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design. Ann Oper Res 283:1079–1109

Federation of Red Cross and Red Crescent Societies (2018). World Disasters Report: Leaving No One Behind

Federation of Red Cross and Red Crescent Societies, (2020). World Disasters Report: Come Heat or High Water

Galindo G, Batta R (2013) Review of recent developments in OR/MS research in disaster operations management. Eur J Oper Res 230:201–211

Gao X (2019) A bi-level stochastic optimization model for multi-commodity rebalancing under uncertainty in disaster response. Ann Oper Res 1–34

Gao X, Cao C (2020) Multi-commodity rebalancing and transportation planning considering traffic congestion and uncertainties in disaster response. Comput Ind Eng 149:106782

García-Alviz J, Galindo G, Arellana J, Yie-Pinedo R (2021) Planning road network restoration and relief distribution under heterogeneous road disruptions. OR Spectr 43:941–981

Garrido RA, Lamas P, Pino FJ (2015) A stochastic programming approach for floods emergency logistics. Transp Res Part E Logist Transp Rev 75:18–31

Gharib M, Fatemi Ghomi SMT, Jolai F (2021) A dynamic dispatching problem to allocate relief vehicles after a disaster. Eng Optim 53:1999–2016

Ghasemi P, Khalili-Damghani K, Hafezalkotob A, Raissi S (2020) Stochastic optimization model for distribution and evacuation planning (a case study of tehran earthquake). Socio-econ Plan Sci 71:100745

Grass E, Fischer K (2016) Two-stage stochastic programming in disaster management: a literature survey. Surv Oper Res Manag Sci 21:85–100

Grass E, Fischer K, Rams A (2020) An accelerated l-shaped method for solving two-stage stochastic programs in disaster management. Ann Oper Res 284:557–582

Gu J, Zhou Y, Das A, Moon I, Lee GM (2018) Medical relief shelter location problem with patient severity under a limited relief budget. Comput Ind Eng 125:720–728

Gupta S, Starr MK, Farahani RZ, Matinrad N (2016) Disaster management from a POM perspective: mapping a new domain. Prod Oper Manag 25:1611–1637

Habibi-Kouchaksaraei M, Paydar MM, Asadi-Gangraj E (2018) Designing a bi-objective multi-echelon robust blood supply chain in a disaster. Appl Math Model 55:583–599

Haeri A, Hosseini-Motlagh SM, Samani MRG, Rezaei M (2020) A bi-level programming approach for improving relief logistics operations: a real case in Kermanshah earthquake. Comput Ind Eng 145:106532

Hasani A, Mokhtari H (2018) Redesign strategies of a comprehensive robust relief network for disaster management. Socio-econ Plan Sci 64:92–102

Holguín-Veras J, Pérez N, Jaller M, Van Wassenhove LN, Aros-Vera F (2013) On the appropriate objective function for post-disaster humanitarian logistics models. J Ope Manag 31:262–280

Hosseini-Motlagh SM, Samani MRG, Homaei S (2020) Toward a coordination of inventory and distribution schedules for blood in disasters. Socio-econ Plan Sci 72:100897

Hu S, Han C, Dong ZS, Meng L (2019) A multi-stage stochastic programming model for relief distribution considering the state of road network. Transp Res Part B Methodol 123:64–87

Hu SL, Han CF, Meng LP (2017) Stochastic optimization for joint decision making of inventory and procurement in humanitarian relief. Comput Ind Eng 111:39–49

Huang K, Jiang Y, Yuan Y, Zhao L (2015) Modeling multiple humanitarian objectives in emergency response to large-scale disasters. Transp Res Part E Logist Transp Rev 75:1–17

Huang K, Rafiei R (2019) Equitable last mile distribution in emergency response. Comput Ind Eng 127:887–900

Jabbarzadeh A, Fahimnia B, Seuring S (2014) Dynamic supply chain network design for the supply of blood in disasters: a robust model with real world application. Transp Res Part E Logist Transp Rev 70:225–244

Jana RK, Sharma DK, Mehta P (2021) A probabilistic fuzzy goal programming model for managing the supply of emergency relief materials. Ann Oper Res 1–24

Kamyabniya A, Lotfi M, Cai H, Hosseininasab H, Yaghoubi S, Yih Y (2019) A two-phase coordinated logistics planning approach to platelets provision in humanitarian relief operations. IISE Trans 51:1–21

Kamyabniya A, Lotfi M, Naderpour M, Yih Y (2018a) Robust platelet logistics planning in disaster relief operations under uncertainty: a coordinated approach. Inf Syst Front 20:759–782

Kamyabniya A, Lotfi MM, Hosseini Nasab H, Yaghoubi S (2018)b Multiple-organizational coordination planning for humanitarian relief operations. Journal of Industrial and Systems Engineering 11, 29–42

Kamyabniya A, Noormohammadzadeh Z, Sauré A, Patrick J (2021) A robust integrated logistics model for age-based multi-group platelets in disaster relief operations. Transp Res Part E Logist Transp Rev 152:102371

Kelle P, Schneider H, Yi H (2014) Decision alternatives between expected cost minimization and worst case scenario in emergency supply-second revision. Int J Prod Econ 157:250–260

Khalili-Damghani K, Tavana M, Ghasemi P (2022) A stochastic bi-objective simulation-optimization model for cascade disaster location-allocation-distribution problems. Ann Oper Res 309:103–141

Khalilpourazari S, Khamseh AA (2019) Bi-objective emergency blood supply chain network design in earthquake considering earthquake magnitude: a comprehensive study with real world application. Ann Oper Res 283:355–393

Khalilpourazari S, Soltanzadeh S, Weber GW, Roy SK (2020) Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study. Ann Oper Res 289:123–152

Khanchehzarrin S, Panah MG, Mahdavi-Amiri N, Shiripour S (2022) A bi-level multi-objective location-routing optimization model for disaster relief operations considering public donations. Socio-econ Plan Sci 80:101165

Kılcı F, Kara BY, Bozkaya B (2015) Locating temporary shelter areas after an earthquake: a case for turkey. Eur J Oper Res 243:323–332

Kınay ÖB, Kara BY, Saldanha-da Gama F, Correia I (2018) Modeling the shelter site location problem using chance constraints: a case study for Istanbul. Eur J Oper Res 270:132–145

Kovacs G, Moshtari M (2019) A roadmap for higher research quality in humanitarian operations: a methodological perspective. Eur J Oper Res 276:395–408

Kunz N, Reiner G (2012) A meta-analysis of humanitarian logistics research. J Humanit Logist Supply Chain Manag 2:116

Leiras A, de Brito Jr I, Peres EQ, Bertazzo TR, Yoshizaki HTY (2014) Literature review of humanitarian logistics research: trends and challenges. J Humanit Logist Supply Chain Manag 4:95

Li B, Hernandez I, Milburn AB, Ramirez-Marquez JE (2018) Integrating uncertain user-generated demand data when locating facilities for disaster response commodity distribution. Socio-econ Plan Sci 62:84–103

Li H, Zhao L, Huang R, Hu Q (2017) Hierarchical earthquake shelter planning in urban areas: a case for shanghai in China. Int J Disaster Risk Reduct 22:431–446

Li Y, Zhang J, Yu G (2020) A scenario-based hybrid robust and stochastic approach for joint planning of relief logistics and casualty distribution considering secondary disasters. Transp Res Part E Logist Transp Rev 141:102029

Liberatore F, Ortuño MT, Tirado G, Vitoriano B, Scaparra MP (2014) A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in humanitarian logistics. Comput Oper Res 42:3–13

Liu K (2020) Post-earthquake medical evacuation system design based on hierarchical multi-objective optimization model: an earthquake case study. Int J Disaster Risk Reduct 51:101785

Liu K, Zhang H, Zhang ZH (2021) The efficiency, equity and effectiveness of location strategies in humanitarian logistics: A robust chance-constrained approach. Transp Res Part E Logist Transp Rev 156:102521

Liu Y, Cui N, Zhang J (2019a) Integrated temporary facility location and casualty allocation planning for post-disaster humanitarian medical service. Transp Res Part E Logist Transp Rev 128:1–16

Liu Y, Lei H, Wu Z, Zhang D (2019b) A robust model predictive control approach for post-disaster relief distribution. Comput Ind Eng 135:1253–1270

Liu Y, Lei H, Zhang D, Wu Z (2018) Robust optimization for relief logistics planning under uncertainties in demand and transportation time. Appl Math Model 55:262–280

Loree N, Aros-Vera F (2018) Points of distribution location and inventory management model for post-disaster humanitarian logistics. Transp Res Part E Logist Transp Rev 116:1–24

Lu CC, Ying KC, Chen HJ (2016) Real-time relief distribution in the aftermath of disasters-a rolling horizon approach. Transp Res Part E Logist Transp Rev 93:1–20

Maharjan R, Hanaoka S (2020) A credibility-based multi-objective temporary logistics hub location-allocation model for relief supply and distribution under uncertainty. Socio-econ Plan Sci 70:100727

Mahootchi M, Golmohammadi S (2018) Developing a new stochastic model considering bi-directional relations in a natural disaster: a possible earthquake in tehran (the capital of islamic republic of iran). Ann Oper Res 269:439–473

Manopiniwes W, Irohara T (2017) Stochastic optimisation model for integrated decisions on relief supply chains: preparedness for disaster response. Int J Prod Res 55:979–996

Mejia-Argueta C, Gaytan J, Caballero R, Molina J, Vitoriano B (2018) Multicriteria optimization approach to deploy humanitarian logistic operations integrally during floods. Int Trans Oper Res 25:1053–1079

Mills AF, Argon NT, Ziya S (2018) Dynamic distribution of patients to medical facilities in the aftermath of a disaster. Oper Res 66:716–732

Mohamadi A, Yaghoubi S (2017) A bi-objective stochastic model for emergency medical services network design with backup services for disasters under disruptions: An earthquake case study. Int J Disaster Risk Reduct 23:204–217

Mohammadi R, Ghomi SF, Jolai F (2016) Prepositioning emergency earthquake response supplies: a new multi-objective particle swarm optimization algorithm. Appl Math Model 40:5183–5199

Mohammadi S, Darestani SA, Vahdani B, Alinezhad A (2020) A robust neutrosophic fuzzy-based approach to integrate reliable facility location and routing decisions for disaster relief under fairness and aftershocks concerns. Comput Ind Eng 148:106734

Mollah AK, Sadhukhan S, Das P, Anis MZ (2018) A cost optimization model and solutions for shelter allocation and relief distribution in flood scenario. Int J Disaster Risk Reduct 31:1187–1198

Mondal T, Boral N, Bhattacharya I, Das J, Pramanik P (2019) Distribution of deficient resources in disaster response situation using particle swarm optimization. Int J Disaster Risk Reduct 41:101308

Moreno A, Alem D, Ferreira D (2016) Heuristic approaches for the multiperiod location-transportation problem with reuse of vehicles in emergency logistics. Comput Oper Res 69:79–96

Moreno A, Alem D, Ferreira D, Clark A (2018) An effective two-stage stochastic multi-trip location-transportation model with social concerns in relief supply chains. Eur J Oper Res 269:1050–1071

Nabavi S, Vahdani B, Nadjafi BA, Adibi M (2022) Synchronizing victim evacuation and debris removal: a data-driven robust prediction approach. Eur J Oper Res 300:689–712

Nagurney A, Flores EA, Soylu C (2016) A generalized Nash equilibrium network model for post-disaster humanitarian relief. Transp Res Part E Logist Transp Rev 95:1–18

Najafi M, Eshghi K, Dullaert W (2013) A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transp Res Part E Logist Transp Rev 49:217–249

Najafi M, Eshghi K, de Leeuw S (2014) A dynamic dispatching and routing model to plan/re-plan logistics activities in response to an earthquake. OR Spectr 36:323–356

Ni W, Shu J, Song M (2018) Location and emergency inventory pre-positioning for disaster response operations: min–max robust model and a case study of Yushu earthquake. Prod Oper Manag 27:160–183

Noyan N, Kahvecioğlu G (2018) Stochastic last mile relief network design with resource reallocation. OR Spectr 40:187–231

Oksuz MK, Satoglu SI (2020) A two-stage stochastic model for location planning of temporary medical centers for disaster response. Int J Disaster Risk Reduct 44:101426

Ozbay E, Çavuş Ö, Kara BY (2019) Shelter site location under multi-hazard scenarios. Comput Oper Res 106:102–118

Paul JA, MacDonald L (2016) Location and capacity allocations decisions to mitigate the impacts of unexpected disasters. Eur J Oper Res 251:252–263

Paul JA, Zhang M (2019) Supply location and transportation planning for hurricanes: a two-stage stochastic programming framework. Eur J Oper Res 274:108–125

Pedraza-Martinez AJ, Van Wassenhove LN (2016) Empirically grounded research in humanitarian operations management: the way forward. J Oper Manag 45:1–10

Pérez-Galarce F, Canales LJ, Vergara C, Candia-Véjar A (2017) An optimization model for the location of disaster refuges. Socio-econ Plan Sci 59:56–66

Rahmani D, Zandi A, Peyghaleh E, Siamakmanesh N (2018) A robust model for a humanitarian relief network with backup covering under disruptions: a real world application. Int J Disaster Risk Reduct 28:56–68

Ransikarbum K, Mason SJ (2016) Goal programming-based post-disaster decision making for integrated relief distribution and early-stage network restoration. Int J Prod Econ 182:324–341

Ransikarbum K, Mason SJ (2021) A bi-objective optimisation of post-disaster relief distribution and short-term network restoration using hybrid nsga-ii algorithm. International Journal of Production Research, 1–25

Razavi N, Gholizadeh H, Nayeri S, Ashrafi TA (2021) A robust optimization model of the field hospitals in the sustainable blood supply chain in crisis logistics. J Oper Res Soc 72:2804–2828

Rennemo SJ, Rø KF, Hvattum LM, Tirado G (2014) A three-stage stochastic facility routing model for disaster response planning. Transp Res Part E Logist Transp Rev 62:116–135

Rezaei-Malek M, Tavakkoli-Moghaddam R, Zahiri B, Bozorgi-Amiri A (2016) An interactive approach for designing a robust disaster relief logistics network with perishable commodities. Comput Ind Eng 94:201–215

Rivera-Royero D, Galindo G, Yie-Pinedo R (2016) A dynamic model for disaster response considering prioritized demand points. Socio-econ Plan Sci 55:59–75

Rivera-Royero D, Galindo G, Yie-Pinedo R (2020) Planning the delivery of relief supplies upon the occurrence of a natural disaster while considering the assembly process of the relief kits. Socio-econ Plan Sci 69:100682

Rodríguez-Espíndola O, Albores P, Brewster C (2018a) Disaster preparedness in humanitarian logistics: a collaborative approach for resource management in floods. Eur J Oper Res 264:978–993

Rodríguez-Espíndola O, Albores P, Brewster C (2018b) Dynamic formulation for humanitarian response operations incorporating multiple organisations. Int J Prod Econ 204:83–98

Sabbaghtorkan M, Batta R, He Q (2020) Prepositioning of assets and supplies in disaster operations management: review and research gap identification. Eur J Oper Res 284:1–19

Sabouhi F, Bozorgi-Amiri A, Moshref-Javadi M, Heydari M (2019) An integrated routing and scheduling model for evacuation and commodity distribution in large-scale disaster relief operations: a case study. Ann Oper Res 283:643–677

Safaei AS, Farsad S, Paydar MM (2018) Robust bi-level optimization of relief logistics operations. Appl Math Model 56:359–380

Sakiani R, Seifi A, Khorshiddoust RR (2020) Inventory routing and dynamic redistribution of relief goods in post-disaster operations. Comput Ind Eng 140:106219

Salman FS, Gül S (2014) Deployment of field hospitals in mass casualty incidents. Comput Ind Eng 74:37–51

Samani MRG, Torabi SA, Hosseini-Motlagh SM (2018) Integrated blood supply chain planning for disaster relief. Int J Disaster Risk Reduct 27:168–188

Sanci E, Daskin MS (2019) Integrating location and network restoration decisions in relief networks under uncertainty. Eur J Oper Res 279:335–350

SCImago (2021) J Country Rank. https://www.scimagojr.com/journalrank.php?category=1803

Seraji H, Tavakkoli-Moghaddam R, Asian S, Kaur H (2021) An integrative location-allocation model for humanitarian logistics with distributive injustice and dissatisfaction under uncertainty. Ann Oper Res 1–47

Setiawan E, Liu J, French A (2019) Resource location for relief distribution and victim evacuation after a sudden-onset disaster. IISE Trans 51:830–846

Shahparvari S, Abbasi B (2017) Robust stochastic vehicle routing and scheduling for bushfire emergency evacuation: An australian case study. Transp Res Part A Policy Pract 104:32–49

Shahparvari S, Abbasi B, Chhetri P (2017) Possibilistic scheduling routing for short-notice bushfire emergency evacuation under uncertainties: An australian case study. Omega 72:96–117

Shahparvari S, Abbasi B, Chhetri P, Abareshi A (2019) Fleet routing and scheduling in bushfire emergency evacuation: A regional case study of the black saturday bushfires in australia. Transp Res Part D Transp Environ 67:703–722

Shahparvari S, Bodaghi B (2018) Risk reduction for distribution of the perishable rescue items; a possibilistic programming approach. Int J Disaster Risk Reduct 31:886–901

Shahparvari S, Chhetri P, Abbasi B, Abareshi A (2016) Enhancing emergency evacuation response of late evacuees: revisiting the case of Australian black Saturday Bushfire. Transp Res Part E Logist Transp Rev 93:148–176

Sharma B, Ramkumar M, Subramanian N, Malhotra B (2019) Dynamic temporary blood facility location-allocation during and post-disaster periods. Ann Oper Res 283:705–736

Shehadeh KS, Tucker EL (2022) Stochastic optimization models for location and inventory prepositioning of disaster relief supplies. Transp Res Part C Emerg Technol 144:103871

Sheu JB (2014) Post-disaster relief-service centralized logistics distribution with survivor resilience maximization. Transp Res Part B Methodol 68:288–314

Sheu JB, Pan C (2014) A method for designing centralized emergency supply network to respond to large-scale natural disasters. Transp Res Part B Methodol 67:284–305

Shiripour S, Mahdavi-Amiri N (2019) Optimal distribution of the injured in a multi-type transportation network with damage-dependent travel times: two metaheuristic approaches. Socio-econ Plan Sci 68:100660

Sun H, Wang Y, Xue Y (2021) A bi-objective robust optimization model for disaster response planning under uncertainties. Comput Ind Eng 155:107213

Thomé AMT, Scavarda LF, Scavarda AJ (2016) Conducting systematic literature review in operations management. Prod Plan Control 27:408–420

Tippong D, Petrovic S, Akbari V (2021) A review of applications of operational research in healthcare coordination in disaster management. Eur J Oper Res (Available Online)

Tofighi S, Torabi SA, Mansouri SA (2016) Humanitarian logistics network design under mixed uncertainty. Eur J Oper Res 250:239–250

Torabi SA, Shokr I, Tofighi S, Heydari J (2018) Integrated relief pre-positioning and procurement planning in humanitarian supply chains. Transp Res Part E Logist Transp Rev 113:123–146

Velasquez GA, Mayorga ME, Özaltın OY (2020) Prepositioning disaster relief supplies using robust optimization. IISE Trans 52:1122–1140

Wang C, Chen S (2020) A distributionally robust optimization for blood supply network considering disasters. Transp Res Part E Logist Transp Rev 134:101840

Wang H, Du L, Ma S (2014) Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake. Transp Res Part E Logist Transp Rev 69:160–179

Wang W, Yang K, Yang L, Gao Z (2021a) Two-stage distributionally robust programming based on worst-case mean-CVAR criterion and application to disaster relief management. Transp Res Part E Logist Transp Rev 149:102332

Wang Y, Dong ZS, Hu S (2021b) A stochastic prepositioning model for distribution of disaster supplies considering lateral transshipment. Socio-Economic Planning Sciences 74:100930

Wei X, Qiu H, Wang D, Duan J, Wang Y, Cheng T (2020) An integrated location-routing problem with post-disaster relief distribution. Comput Ind Eng 147:106632

Yahyaei M, Bozorgi-Amiri A (2019) Robust reliable humanitarian relief network design: an integration of shelter and supply facility location. Ann Oper Res 283:897–916

Yang M, Kumar S, Wang X, Fry MJ (2021a) Scenario-robust pre-disaster planning for multiple relief items. Ann Oper Res 1–26

Yang M, Liu Y, Yang G (2021b) Multi-period dynamic distributionally robust pre-positioning of emergency supplies under demand uncertainty. Appl Math Model 89:1433–1458

Yi W, Nozick L, Davidson R, Blanton B, Colle B (2017) Optimization of the issuance of evacuation orders under evolving hurricane conditions. Transp Res Part B Methodol 95:285–304

Zarrinpoor N, Fallahnezhad MS, Pishvaee MS (2017) Design of a reliable hierarchical location-allocation model under disruptions for health service networks: A two-stage robust approach. Comput Ind Eng 109:130–150

Zhang J, Liu H, Yu G, Ruan J, Chan FT (2019a) A three-stage and multi-objective stochastic programming model to improve the sustainable rescue ability by considering secondary disasters in emergency logistics. Comput Ind Eng 135:1145–1154

Zhang J, Wang Z, Ren F (2019b) Optimization of humanitarian relief supply chain reliability: a case study of the Ya’an earthquake. Ann Oper Res 283:1551–1572

Zhang J, Zhang X (2022) A multi-trip electric bus routing model considering equity during short-notice evacuations. Transp Res Part D Transp Environ 110:103397

Zhang P, Liu Y, Yang G, Zhang G (2020) A multi-objective distributionally robust model for sustainable last mile relief network design problem. Ann Oper Res 1–42

Zhou S, Erdogan A (2019) A spatial optimization model for resource allocation for wildfire suppression and resident evacuation. Comput Ind Eng 138:106101

Zhou Y, Liu J, Zhang Y, Gan X (2017) A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems. Transp Res Part E Logist Transp Rev 99:77–95

Zhu J, Shi Y, Venkatesh V, Islam S, Hou Z, Arisian S (2022) Dynamic collaborative optimization for disaster relief supply chains under information ambiguity. Ann Oper Res 1–27

Zhu L, Gong Y, Xu Y, Gu J (2019) Emergency relief routing models for injured victims considering equity and priority. Ann Oper Res 283:1573–1606

Zokaee S, Bozorgi-Amiri A, Sadjadi SJ (2016) A robust optimization model for humanitarian relief chain design under uncertainty. Appl Math Model 40:7996–8016

Download references

Acknowledgements

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].

Author information

Authors and affiliations.

Faculty of Management, University of New Brunswick, 7 MacAulay Lane, Fredericton, NB, E3B 5A3, Canada

Afshin Kamyabniya

Telfer School of Management, University of Ottawa, 55 Laurier Avenue East, Ottawa, ON, K1N 6N5, Canada

Antoine Sauré & Jonathan Patrick

Department of Industrial Engineering, Koç University, 34450, Istanbul, Turkey

F. Sibel Salman

National Research Council, 1200 Montreal Road, Ottawa, ON, K1A 0R6, Canada

Noureddine Bénichou

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Afshin Kamyabniya .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (pdf 611 KB)

Rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

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

Download citation

Received : 31 January 2023

Accepted : 24 January 2024

Published : 10 March 2024

Issue Date : September 2024

DOI : https://doi.org/10.1007/s00291-024-00750-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Disaster operations management
• Natural disasters
• Humanitarian logistics
• Optimization models
• Find a journal
• Publish with us
• Track your research

Browse Econ Literature

• Working papers
• Software components
• Book chapters
• JEL classification

More features

• Subscribe to new research

RePEc Biblio

Author registration.

• Economics Virtual Seminar Calendar NEW!

Some searches may not work properly. We apologize for the inconvenience.

Optimization models in emergency logistics: A literature review

• Author & abstract
• 29 References
• 151 Citations
• Most related
• Related works & more

Corrections

• Caunhye, Aakil M.
• Nie, Xiaofeng
• Pokharel, Shaligram

Suggested Citation

Download full text from publisher, references listed on ideas.

Follow serials, authors, keywords & more

Public profiles for Economics researchers

Various research rankings in Economics

RePEc Genealogy

Who was a student of whom, using RePEc

Curated articles & papers on economics topics

Upload your paper to be listed on RePEc and IDEAS

New papers by email

Subscribe to new additions to RePEc

EconAcademics

Blog aggregator for economics research

Cases of plagiarism in Economics

About RePEc

Initiative for open bibliographies in Economics

News about RePEc

Questions about IDEAS and RePEc

RePEc volunteers

Participating archives

Publishers indexing in RePEc

Privacy statement

Found an error or omission?

Opportunities to help RePEc

Get papers listed

Have your research listed on RePEc

Open a RePEc archive

Have your institution's/publisher's output listed on RePEc

Get RePEc data

Use data assembled by RePEc

• Registration Log In

A Literature Review on the Optimization Method of Emergency Transportation and Logistics System

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.

Periodical:

https://doi.org/10.4028/www.scientific.net/AMR.711.747

Cite this paper

Online since:

Permissions:

Request Permissions

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

[1] Sinuany. Simulating the evacuation of a small city: the effects of traffic factors. Socio-Economic Planning Sciences, Vol.27(1993),pp.97-108.

DOI: 10.1016/0038-0121(93)90010-g

Google Scholar

[2] Pidd. A Simulation Model for Emergency Evacuation. European Journal of Operational Research, Vol.90(1996), pp.413-419.

DOI: 10.1016/0377-2217(95)00112-3

[3] Xuwei Chen. Agent-based modeling and simulation of urban evacuation: relative effectiveness of simultaneous and evacuation strategies. Transportation Research Board, Vol.329(2004), pp.98-104.

DOI: 10.1057/9781137453648_6

[4] Yang Fan. Dynamic traffic flow modeling and simulation of emergency evacuation under large-scale activities(Wuhan University of technology Publications, Wuhan 2009).

[5] C.E. Dunn and D.Newton. Optimal routes in GIS and emergency planning applications. Area, Vol.24(1992),pp.259-267.

[6] T. Yamada. A network flow approach to a city emergency evacuation planning. International Journal of Systems Science, Vol.27(1996),pp.931-936.

DOI: 10.1080/00207729608929296

[7] J. C. Thomas and P. J. Justin. A network flow model for lane-based evacuation routing. Transportation Research Part A, Vol.37(2003),pp.579-604.

DOI: 10.1016/s0965-8564(03)00007-7

[8] Ziyao Li. Fuzzy clustering analysis of emergency rescue route. Artificial Intelligence, Vol.10(2011),pp.1148-1151.

DOI: 10.1109/aimsec.2011.6010690

[9] Chi Wenxue. Using VB to realize the shortest path analysis based on MapObjects, the NetEngine. Transaction of East China University of Science and Technology, Vol.28(2005),pp.282-285.

[10] FanWenjing and Ma Zujun. Optimization of urban emergency rescue paths in time-dependent network. Computer Applications, Vol.31(2011),pp.125-128.

[11] H. Matsutomi and H. Ishii. An emergency service facility location problem with fuzzy objective and constraint. Fuzzy Systems, Vol.12(1992),pp.315-322.

DOI: 10.1109/fuzzy.1992.258635

[12] Chen Zhizong and You Jianxin. Modeling for the hierarchical location problem of urban disaster prevention facilities. Journal of Natural Disaster, Vol.14(2005),pp.131-135.

[13] Wei Yi. A dynamic logistics coordination model for evacuation. European Journal of Operational Research, Vol.179(2007),pp.1177-1193.

DOI: 10.1016/j.ejor.2005.03.077

[14] P. Beraldi and M.E. Bruni. A probabilistic model applied to emergency service vehicle location. European Journal of Operational Research, Vol.196(2009),pp.323-331.

DOI: 10.1016/j.ejor.2008.02.027

[15] Revelle and Laporte. The plant location problem: New models and research prospects Operations Research,Vol.44(1996),pp.864-874.

DOI: 10.1287/opre.44.6.864

[16] Laporte. Models and exact solutions for a class of stochastic location-routing problems European Journal of Operational Research, Vol.39(1989),pp.71-78.

DOI: 10.1016/0377-2217(89)90354-8

[17] WangBaohua, HeShiwei. Robust optimization model and its algorithm of logistics center location under uncertain environment. Traffic and Transportation System Engineering and Information, Vol.9(2009),pp.69-74.

DOI: 10.1016/s1570-6672(08)60056-2

[18] L. Equi. A Combined Transportation and Scheduling Problem. European Journal of Operational Research,Vol.97(1997),pp.94-104.

[19] Kannan. The Multi-commodity Maximal Covering Network Design Problem for Planning Critical Routes for Earthquake Response. Transportation Research Board, Vol.98(2003), pp.568-588.

DOI: 10.3141/1857-01

[20] Barbarosoglu. A Two-stage Stochastic Programming Framework for Transportation Planning in Disaster Response. Journal of the Operational Research, Vol.55 (2004), pp.43-53.

DOI: 10.1057/palgrave.jors.2601652

[21] Linet. Emergency Logistics Planning in Natural Disasters. Annals of Operation Research, Vol.129(2004),pp.218-219.

[22] He Jianmin and Liu Chunlin. Fuzzy optimization method of emergency vehicle routing problem under time restriction. Control and Decision Making, Vol.16(2001),pp.318-321.

[23] He Zhengwen.Vehicle routing problem of emergency material scheduling based on prohibition of time windows. Operations research and management, Vol.18(2009),p.:1-6.

[24] Zeng Mingang. Locating and routing problem for natural disaster emergency rescue system. Journal of South China University, Vol.10(2008),p.29.

[25] Xu Qin and Ma Zujun. Location-Routing problem of emergency logistics in urban emergency. Journal of Huazhong University, Vol.22(2008),pp.36-40.

Paper price:

After payment, you will receive an email with instructions and a link to download the purchased paper.

You may also check the possible access via personal account by logging in or/and check access through your institution.

This paper has been added to your cart

• For Libraries
• For Publication
• Policy & Ethics
• Privacy Policy
• All Conferences
• All Special Issues
• Read & Publish Agreements

© 2024 Trans Tech Publications Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For open access content, terms of the Creative Commons licensing CC-BY are applied. Scientific.Net is a registered trademark of Trans Tech Publications Ltd.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Author Biographies
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Advancements in deep learning techniques for time series forecasting in maritime applications: a comprehensive review.

1. Introduction

2. literature collection procedure.

• Search scope: Titles, Keywords, and Abstracts
• Keywords 1: ‘deep’ AND ‘learning’, AND
• Keywords 2: ‘time AND series’, AND
• Keywords 3: ‘maritime’, OR
• Keywords 4: ‘vessel’, OR
• Keywords 5: ‘shipping’, OR
• Keywords 6: ‘marine’, OR
• Keywords 7: ‘ship’, OR
• Keywords 8: ‘port’, OR
• Keywords 9: ‘terminal’
• Retain only articles related to maritime operations. For example, studies on ship-surrounding weather and risk prediction based on ship data will be kept, while research solely focused on marine weather or wave prediction that is unrelated to any aspect of maritime operations will be excluded.
• Exclude neural network studies that do not employ deep learning techniques, such as ANN or MLP with only one hidden layer.
• The language of the publications must be English.
• The original data used in the papers must include time series sequences.

3. Deep Learning Algorithms

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.

• UNCTAD. Review of Maritime Transport 2023 ; United Nations Conference on Trade and Development: Geneva, Switzerland, 2023; Available online: https://www.un-ilibrary.org/content/books/9789213584569 (accessed on 1 April 2024).
• Liang, M.; Liu, R.W.; Zhan, Y.; Li, H.; Zhu, F.; Wang, F.Y. Fine-Grained Vessel Traffic Flow Prediction With a Spatio-Temporal Multigraph Convolutional Network. IEEE Trans. Intell. Transp. Syst. 2022 , 23 , 23694–23707. [ Google Scholar ] [ CrossRef ]
• Liu, R.W.; Liang, M.; Nie, J.; Lim, W.Y.B.; Zhang, Y.; Guizani, M. Deep Learning-Powered Vessel Trajectory Prediction for Improving Smart Traffic Services in Maritime Internet of Things. IEEE Trans. Netw. Sci. Eng. 2022 , 9 , 3080–3094. [ Google Scholar ] [ CrossRef ]
• Dui, H.; Zheng, X.; Wu, S. Resilience analysis of maritime transportation systems based on importance measures. Reliab. Eng. Syst. Saf. 2021 , 209 , 107461. [ Google Scholar ] [ CrossRef ]
• Liang, M.; Li, H.; Liu, R.W.; Lam, J.S.L.; Yang, Z. PiracyAnalyzer: Spatial temporal patterns analysis of global piracy incidents. Reliab. Eng. Syst. Saf. 2024 , 243 , 109877. [ Google Scholar ] [ CrossRef ]
• Chen, Z.S.; Lam, J.S.L.; Xiao, Z. Prediction of harbour vessel emissions based on machine learning approach. Transp. Res. Part D Transp. Environ. 2024 , 131 , 104214. [ Google Scholar ] [ CrossRef ]
• Chen, Z.S.; Lam, J.S.L.; Xiao, Z. Prediction of harbour vessel fuel consumption based on machine learning approach. Ocean Eng. 2023 , 278 , 114483. [ Google Scholar ] [ CrossRef ]
• Liang, M.; Weng, L.; Gao, R.; Li, Y.; Du, L. Unsupervised maritime anomaly detection for intelligent situational awareness using AIS data. Knowl.-Based Syst. 2024 , 284 , 111313. [ Google Scholar ] [ CrossRef ]
• Dave, V.S.; Dutta, K. Neural network based models for software effort estimation: A review. Artif. Intell. Rev. 2014 , 42 , 295–307. [ Google Scholar ] [ CrossRef ]
• Uslu, S.; Celik, M.B. Prediction of engine emissions and performance with artificial neural networks in a single cylinder diesel engine using diethyl ether. Eng. Sci. Technol. Int. J. 2018 , 21 , 1194–1201. [ Google Scholar ] [ CrossRef ]
• Chaudhary, L.; Sharma, S.; Sajwan, M. Systematic Literature Review of Various Neural Network Techniques for Sea Surface Temperature Prediction Using Remote Sensing Data. Arch. Comput. Methods Eng. 2023 , 30 , 5071–5103. [ Google Scholar ] [ CrossRef ]
• Dharia, A.; Adeli, H. Neural network model for rapid forecasting of freeway link travel time. Eng. Appl. Artif. Intell. 2003 , 16 , 607–613. [ Google Scholar ] [ CrossRef ]
• Hecht-Nielsen, R. Applications of counterpropagation networks. Neural Netw. 1988 , 1 , 131–139. [ Google Scholar ] [ CrossRef ]
• Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning ; MIT Press: Cambridge, MA, USA, 2016. [ Google Scholar ]
• Veerappa, M.; Anneken, M.; Burkart, N. Evaluation of Interpretable Association Rule Mining Methods on Time-Series in the Maritime Domain. Springer International Publishing: Cham, Switzerland, 2021; pp. 204–218. [ Google Scholar ]
• Frizzell, J.; Furth, M. Prediction of Vessel RAOs: Applications of Deep Learning to Assist in Design. In Proceedings of the SNAME 27th Offshore Symposium, Houston, TX, USA, 22 February 2022. [ Google Scholar ] [ CrossRef ]
• Van Den Oord, A.; Dieleman, S.; Zen, H.; Simonyan, K.; Vinyals, O.; Graves, A.; Kalchbrenner, N.; Senior, A.; Kavukcuoglu, K. Wavenet: A generative model for raw audio. arXiv 2016 , arXiv:1609.03499. [ Google Scholar ]
• He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778. [ Google Scholar ]
• Ning, C.X.; Xie, Y.Z.; Sun, L.J. LSTM, WaveNet, and 2D CNN for nonlinear time history prediction of seismic responses. Eng. Struct. 2023 , 286 , 116083. [ Google Scholar ] [ CrossRef ]
• Schmidt, W.F.; Kraaijveld, M.A.; Duin, R.P. Feed forward neural networks with random weights. In International Conference on Pattern Recognition ; IEEE Computer Society Press: Washington, DC, USA, 1992; pp. 1–4. [ Google Scholar ]
• Huang, G.B.; Zhu, Q.Y.; Siew, C.K. Extreme learning machine: Theory and applications. Neurocomputing 2006 , 70 , 489–501. [ Google Scholar ] [ CrossRef ]
• Pao, Y.H.; Park, G.H.; Sobajic, D.J. Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 1994 , 6 , 163–180. [ Google Scholar ] [ CrossRef ]
• Zhang, L.; Suganthan, P.N. A comprehensive evaluation of random vector functional link networks. Inf. Sci. 2016 , 367 , 1094–1105. [ Google Scholar ] [ CrossRef ]
• Huang, G.; Huang, G.B.; Song, S.J.; You, K.Y. Trends in extreme learning machines: A review. Neural Netw. 2015 , 61 , 32–48. [ Google Scholar ] [ CrossRef ]
• Shi, Q.S.; Katuwal, R.; Suganthan, P.N.; Tanveer, M. Random vector functional link neural network based ensemble deep learning. Pattern Recognit. 2021 , 117 , 107978. [ Google Scholar ] [ CrossRef ]
• Du, L.; Gao, R.B.; Suganthan, P.N.; Wang, D.Z.W. Graph ensemble deep random vector functional link network for traffic forecasting. Appl. Soft Comput. 2022 , 131 , 109809. [ Google Scholar ] [ CrossRef ]
• Rehman, A.; Xing, H.L.; Hussain, M.; Gulzar, N.; Khan, M.A.; Hussain, A.; Mahmood, S. HCDP-DELM: Heterogeneous chronic disease prediction with temporal perspective enabled deep extreme learning machine. Knowl.-Based Syst. 2024 , 284 , 111316. [ Google Scholar ] [ CrossRef ]
• Gao, R.B.; Li, R.L.; Hu, M.H.; Suganthan, P.N.; Yuen, K.F. Online dynamic ensemble deep random vector functional link neural network for forecasting. Neural Netw. 2023 , 166 , 51–69. [ Google Scholar ] [ CrossRef ]
• Lecun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 1998 , 86 , 2278–2324. [ Google Scholar ] [ CrossRef ]
• Palaz, D.; Magimai-Doss, M.; Collobert, R. End-to-end acoustic modeling using convolutional neural networks for HMM-based automatic speech recognition. Speech Commun. 2019 , 108 , 15–32. [ Google Scholar ] [ CrossRef ]
• Fang, W.; Love, P.E.D.; Luo, H.; Ding, L. Computer vision for behaviour-based safety in construction: A review and future directions. Adv. Eng. Inform. 2020 , 43 , 100980. [ Google Scholar ] [ CrossRef ]
• Qin, L.; Yu, N.; Zhao, D. Applying the convolutional neural network deep learning technology to behavioural recognition in intelligent video. Teh. Vjesn. 2018 , 25 , 528–535. [ Google Scholar ]
• Hoseinzade, E.; Haratizadeh, S. CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Syst. Appl. 2019 , 129 , 273–285. [ Google Scholar ] [ CrossRef ]
• Rasp, S.; Dueben, P.D.; Scher, S.; Weyn, J.A.; Mouatadid, S.; Thuerey, N. WeatherBench: A Benchmark Data Set for Data-Driven Weather Forecasting. J. Adv. Model. Earth Syst. 2020 , 12 , e2020MS002203. [ Google Scholar ] [ CrossRef ]
• Crivellari, A.; Beinat, E.; Caetano, S.; Seydoux, A.; Cardoso, T. Multi-target CNN-LSTM regressor for predicting urban distribution of short-term food delivery demand. J. Bus. Res. 2022 , 144 , 844–853. [ Google Scholar ] [ CrossRef ]
• Bai, S.; Kolter, J.Z.; Koltun, V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv 2018 , arXiv:1803.01271. [ Google Scholar ]
• Lin, Z.; Yue, W.; Huang, J.; Wan, J. Ship Trajectory Prediction Based on the TTCN-Attention-GRU Model. Electronics 2023 , 12 , 2556. [ Google Scholar ] [ CrossRef ]
• He, K.; Zhang, X.; Ren, S.; Sun, J. Identity Mappings in Deep Residual Networks. In Computer Vision–ECCV 2016 ; Leibe, B., Matas, J., Sebe, N., Welling, M., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 630–645. [ Google Scholar ]
• Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 2014 , 15 , 1929–1958. [ Google Scholar ]
• Bin Syed, M.A.; Ahmed, I. A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data. Sensors 2023 , 23 , 6400. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, M.-W.; Xu, D.-Y.; Geng, J.; Hong, W.-C. A hybrid approach for forecasting ship motion using CNN–GRU–AM and GCWOA. Appl. Soft Comput. 2022 , 114 , 108084. [ Google Scholar ] [ CrossRef ]
• Zhang, B.; Wang, S.; Deng, L.; Jia, M.; Xu, J. Ship motion attitude prediction model based on IWOA-TCN-Attention. Ocean Eng. 2023 , 272 , 113911. [ Google Scholar ] [ CrossRef ]
• Elman, J.L. Finding Structure in Time. Cogn. Sci. 1990 , 14 , 179–211. [ Google Scholar ] [ CrossRef ]
• Shan, F.; He, X.; Armaghani, D.J.; Sheng, D. Effects of data smoothing and recurrent neural network (RNN) algorithms for real-time forecasting of tunnel boring machine (TBM) performance. J. Rock Mech. Geotech. Eng. 2024 , 16 , 1538–1551. [ Google Scholar ] [ CrossRef ]
• Apaydin, H.; Feizi, H.; Sattari, M.T.; Colak, M.S.; Shamshirband, S.; Chau, K.-W. Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting. Water 2020 , 12 , 1500. [ Google Scholar ] [ CrossRef ]
• Ma, Z.; Zhang, H.; Liu, J. MM-RNN: A Multimodal RNN for Precipitation Nowcasting. IEEE Trans. Geosci. Remote Sens. 2023 , 61 , 1–14. [ Google Scholar ] [ CrossRef ]
• Lu, M.; Xu, X. TRNN: An efficient time-series recurrent neural network for stock price prediction. Inf. Sci. 2024 , 657 , 119951. [ Google Scholar ] [ CrossRef ]
• Bengio, Y.; Simard, P.; Frasconi, P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 1994 , 5 , 157–166. [ Google Scholar ] [ CrossRef ]
• Kratzert, F.; Klotz, D.; Brenner, C.; Schulz, K.; Herrnegger, M. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks. Hydrol. Earth Syst. Sci. 2018 , 22 , 6005–6022. [ Google Scholar ] [ CrossRef ]
• Hochreiter, S. Untersuchungen zu dynamischen neuronalen Netzen. Diploma Tech. Univ. München 1991 , 91 , 31. [ Google Scholar ]
• Hochreiter, S.; Schmidhuber, J. Long Short-Term Memory. Neural Comput. 1997 , 9 , 1735–1780. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Gers, F.A.; Schmidhuber, J.; Cummins, F. Learning to Forget: Continual Prediction with LSTM. Neural Comput. 2000 , 12 , 2451–2471. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Witten, I.H.; Frank, E. Data mining: Practical machine learning tools and techniques with Java implementations. SIGMOD Rec. 2002 , 31 , 76–77. [ Google Scholar ] [ CrossRef ]
• Mo, J.X.; Gao, R.B.; Liu, J.H.; Du, L.; Yuen, K.F. Annual dilated convolutional LSTM network for time charter rate forecasting. Appl. Soft Comput. 2022 , 126 , 109259. [ Google Scholar ] [ CrossRef ]
• Cho, K.; Van Merriënboer, B.; Bahdanau, D.; Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches. arXiv 2014 , arXiv:1409.1259. [ Google Scholar ]
• Yang, S.; Yu, X.; Zhou, Y. LSTM and GRU Neural Network Performance Comparison Study: Taking Yelp Review Dataset as an Example. In Proceedings of the 2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI), Shanghai, China, 12–14 June 2020; pp. 98–101. [ Google Scholar ] [ CrossRef ]
• Zhao, Z.N.; Yun, S.N.; Jia, L.Y.; Guo, J.X.; Meng, Y.; He, N.; Li, X.J.; Shi, J.R.; Yang, L. Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features. Eng. Appl. Artif. Intell. 2023 , 121 , 105982. [ Google Scholar ] [ CrossRef ]
• Pan, N.; Ding, Y.; Fu, J.; Wang, J.; Zheng, H. Research on Ship Arrival Law Based on Route Matching and Deep Learning. J. Phys. Conf. Ser. 2021 , 1952 , 022023. [ Google Scholar ] [ CrossRef ]
• Ma, J.; Li, W.K.; Jia, C.F.; Zhang, C.W.; Zhang, Y. Risk Prediction for Ship Encounter Situation Awareness Using Long Short-Term Memory Based Deep Learning on Intership Behaviors. J. Adv. Transp. 2020 , 2020 , 8897700. [ Google Scholar ] [ CrossRef ]
• Suo, Y.F.; Chen, W.K.; Claramunt, C.; Yang, S.H. A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network. Sensors 2020 , 20 , 5133. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bahdanau, D.; Cho, K.; Bengio, Y. Neural machine translation by jointly learning to align and translate. arXiv 2014 , arXiv:1409.0473. [ Google Scholar ] [ CrossRef ]
• Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, Ł.; Polosukhin, I. Attention is all you need. Adv. Neural Inf. Process. Syst. 2017 , 30 , 03762. [ Google Scholar ]
• Nascimento, E.G.S.; de Melo, T.A.C.; Moreira, D.M. A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy. Energy 2023 , 278 , 127678. [ Google Scholar ] [ CrossRef ]
• Zhang, L.; Zhang, J.; Niu, J.; Wu, Q.M.J.; Li, G. Track Prediction for HF Radar Vessels Submerged in Strong Clutter Based on MSCNN Fusion with GRU-AM and AR Model. Remote Sens. 2021 , 13 , 2164. [ Google Scholar ] [ CrossRef ]
• Zhang, X.; Fu, X.; Xiao, Z.; Xu, H.; Zhang, W.; Koh, J.; Qin, Z. A Dynamic Context-Aware Approach for Vessel Trajectory Prediction Based on Multi-Stage Deep Learning. IEEE Trans. Intell. Veh. 2024 , 1–16. [ Google Scholar ] [ CrossRef ]
• Jiang, D.; Shi, G.; Li, N.; Ma, L.; Li, W.; Shi, J. TRFM-LS: Transformer-Based Deep Learning Method for Vessel Trajectory Prediction. J. Mar. Sci. Eng. 2023 , 11 , 880. [ Google Scholar ] [ CrossRef ]
• Violos, J.; Tsanakas, S.; Androutsopoulou, M.; Palaiokrassas, G.; Varvarigou, T. Next Position Prediction Using LSTM Neural Networks. In Proceedings of the 11th Hellenic Conference on Artificial Intelligence, Athens, Greece, 2–4 September 2020; pp. 232–240. [ Google Scholar ] [ CrossRef ]
• Hoque, X.; Sharma, S.K. Ensembled Deep Learning Approach for Maritime Anomaly Detection System. In Proceedings of the 1st International Conference on Emerging Trends in Information Technology (ICETIT), Inst Informat Technol & Management, New Delhi, India, 21–22 June 2020; In Lecture Notes in Electrical Engineering. Volume 605, pp. 862–869. [ Google Scholar ]
• Wang, Y.; Zhang, M.; Fu, H.; Wang, Q. Research on Prediction Method of Ship Rolling Motion Based on Deep Learning. In Proceedings of the 2020 39th Chinese Control Conference (CCC), Shenyang, China, 27–29 July 2020; pp. 7182–7187. [ Google Scholar ] [ CrossRef ]
• Choi, J. Predicting the Frequency of Marine Accidents by Navigators’ Watch Duty Time in South Korea Using LSTM. Appl. Sci. 2022 , 12 , 11724. [ Google Scholar ] [ CrossRef ]
• Li, T.; Li, Y.B. Prediction of ship trajectory based on deep learning. J. Phys. Conf. Ser. 2023 , 2613 , 012023. [ Google Scholar ] [ CrossRef ]
• Chondrodima, E.; Pelekis, N.; Pikrakis, A.; Theodoridis, Y. An Efficient LSTM Neural Network-Based Framework for Vessel Location Forecasting. IEEE Trans. Intell. Transp. Syst. 2023 , 24 , 4872–4888. [ Google Scholar ] [ CrossRef ]
• Long, Z.; Suyuan, W.; Zhongma, C.; Jiaqi, F.; Xiaoting, Y.; Wei, D. Lira-YOLO: A lightweight model for ship detection in radar images. J. Syst. Eng. Electron. 2020 , 31 , 950–956. [ Google Scholar ] [ CrossRef ]
• Cheng, X.; Li, G.; Skulstad, R.; Zhang, H.; Chen, S. SpectralSeaNet: Spectrogram and Convolutional Network-based Sea State Estimation. In Proceedings of the IECON 2020 the 46th Annual Conference of the IEEE Industrial Electronics Society, Singapore, 18–21 October 2020; pp. 5069–5074. [ Google Scholar ] [ CrossRef ]
• Wang, K.; Cheng, X.; Shi, F. Learning Dynamic Graph Structures for Sea State Estimation with Deep Neural Networks. In Proceedings of the 2023 6th International Conference on Intelligent Autonomous Systems (ICoIAS), Qinhuangdao, China, 22–24 September 2023; pp. 161–166. [ Google Scholar ]
• Yu, J.; Huang, D.; Shi, X.; Li, W.; Wang, X. Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence. Appl. Sci. 2023 , 13 , 2584. [ Google Scholar ] [ CrossRef ]
• Ilias, L.; Kapsalis, P.; Mouzakitis, S.; Askounis, D. A Multitask Learning Framework for Predicting Ship Fuel Oil Consumption. IEEE Access 2023 , 11 , 132576–132589. [ Google Scholar ] [ CrossRef ]
• Selimovic, D.; Hrzic, F.; Prpic-Orsic, J.; Lerga, J. Estimation of sea state parameters from ship motion responses using attention-based neural networks. Ocean Eng. 2023 , 281 , 114915. [ Google Scholar ] [ CrossRef ]
• Ma, J.; Jia, C.; Yang, X.; Cheng, X.; Li, W.; Zhang, C. A Data-Driven Approach for Collision Risk Early Warning in Vessel Encounter Situations Using Attention-BiLSTM. IEEE Access 2020 , 8 , 188771–188783. [ Google Scholar ] [ CrossRef ]
• Ji, Z.; Gan, H.; Liu, B. A Deep Learning-Based Fault Warning Model for Exhaust Temperature Prediction and Fault Warning of Marine Diesel Engine. J. Mar. Sci. Eng. 2023 , 11 , 1509. [ Google Scholar ] [ CrossRef ]
• Liu, Y.; Gan, H.; Cong, Y.; Hu, G. Research on fault prediction of marine diesel engine based on attention-LSTM. Proc. Inst. Mech. Eng. Part M J. Eng. Marit. Environ. 2023 , 237 , 508–519. [ Google Scholar ] [ CrossRef ]
• Li, M.W.; Xu, D.Y.; Geng, J.; Hong, W.C. A ship motion forecasting approach based on empirical mode decomposition method hybrid deep learning network and quantum butterfly optimization algorithm. Nonlinear Dyn. 2022 , 107 , 2447–2467. [ Google Scholar ] [ CrossRef ]
• Yang, C.H.; Chang, P.Y. Forecasting the Demand for Container Throughput Using a Mixed-Precision Neural Architecture Based on CNN–LSTM. Mathematics 2020 , 8 , 1784. [ Google Scholar ] [ CrossRef ]
• Zhang, W.; Wu, P.; Peng, Y.; Liu, D. Roll Motion Prediction of Unmanned Surface Vehicle Based on Coupled CNN and LSTM. Future Internet 2019 , 11 , 243. [ Google Scholar ] [ CrossRef ]
• Kamal, I.M.; Bae, H.; Sunghyun, S.; Yun, H. DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index. Appl. Sci. 2020 , 10 , 1504. [ Google Scholar ] [ CrossRef ]
• Li, M.Z.; Li, B.; Qi, Z.G.; Li, J.S.; Wu, J.W. Enhancing Maritime Navigational Safety: Ship Trajectory Prediction Using ACoAtt–LSTM and AIS Data. ISPRS Int. J. Geo-Inform. 2024 , 13 , 85. [ Google Scholar ] [ CrossRef ]
• Yu, T.; Zhang, Y.; Zhao, S.; Yang, J.; Li, W.; Guo, W. Vessel trajectory prediction based on modified LSTM with attention mechanism. In Proceedings of the 2024 4th International Conference on Neural Networks, Information and Communication Engineering, NNICE, Guangzhou, China, 19–21 January 2024; pp. 912–918. [ Google Scholar ] [ CrossRef ]
• Xia, C.; Peng, Y.; Qu, D. A pre-trained model specialized for ship trajectory prediction. In Proceedings of the IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 15–17 March 2024; pp. 1857–1860. [ Google Scholar ] [ CrossRef ]
• Cheng, X.; Li, G.; Skulstad, R.; Chen, S.; Hildre, H.P.; Zhang, H. Modeling and Analysis of Motion Data from Dynamically Positioned Vessels for Sea State Estimation. In Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 20–24 May 2019; pp. 6644–6650. [ Google Scholar ] [ CrossRef ]
• Xia, C.; Qu, D.; Zheng, Y. TATBformer: A Divide-and-Conquer Approach to Ship Trajectory Prediction Modeling. In Proceedings of the 2023 IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, 8–10 December 2023; pp. 335–339. [ Google Scholar ] [ CrossRef ]
• Ran, Y.; Shi, G.; Li, W. Ship Track Prediction Model based on Automatic Identification System Data and Bidirectional Cyclic Neural Network. In Proceedings of the 2021 4th International Symposium on Traffic Transportation and Civil Architecture, ISTTCA, Suzhou, China, 12–14 November 2021; pp. 297–301. [ Google Scholar ] [ CrossRef ]
• Yang, C.H.; Wu, C.H.; Shao, J.C.; Wang, Y.C.; Hsieh, C.M. AIS-Based Intelligent Vessel Trajectory Prediction Using Bi-LSTM. IEEE Access 2022 , 10 , 24302–24315. [ Google Scholar ] [ CrossRef ]
• Sadeghi, Z.; Matwin, S. Anomaly detection for maritime navigation based on probability density function of error of reconstruction. J. Intell. Syst. 2023 , 32 , 20220270. [ Google Scholar ] [ CrossRef ]
• Perumal, V.; Murugaiyan, S.; Ravichandran, P.; Venkatesan, R.; Sundar, R. Real time identification of anomalous events in coastal regions using deep learning techniques. Concurr. Comput. Pract. Exp. 2021 , 33 , e6421. [ Google Scholar ] [ CrossRef ]
• Xie, J.L.; Shi, W.F.; Shi, Y.Q. Research on Fault Diagnosis of Six-Phase Propulsion Motor Drive Inverter for Marine Electric Propulsion System Based on Res-BiLSTM. Machines 2022 , 10 , 736. [ Google Scholar ] [ CrossRef ]
• Han, P.; Li, G.; Skulstad, R.; Skjong, S.; Zhang, H. A Deep Learning Approach to Detect and Isolate Thruster Failures for Dynamically Positioned Vessels Using Motion Data. IEEE Trans. Instrum. Meas. 2021 , 70 , 1–11. [ Google Scholar ] [ CrossRef ]
• Cheng, X.; Wang, K.; Liu, X.; Yu, Q.; Shi, F.; Ren, Z.; Chen, S. A Novel Class-Imbalanced Ship Motion Data-Based Cross-Scale Model for Sea State Estimation. IEEE Trans. Intell. Transp. Syst. 2023 , 24 , 15907–15919. [ Google Scholar ] [ CrossRef ]
• Lei, L.; Wen, Z.; Peng, Z. Prediction of Main Engine Speed and Fuel Consumption of Inland Ships Based on Deep Learning. J. Phys. Conf. Ser. 2021 , 2025 , 012012. [ Google Scholar ]
• Ljunggren, H. Using Deep Learning for Classifying Ship Trajectories. In Proceedings of the 21st International Conference on Information Fusion (FUSION), Cambridge, UK, 10–13 July 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 2158–2164. [ Google Scholar ]
• Kulshrestha, A.; Yadav, A.; Sharma, H.; Suman, S. A deep learning-based multivariate decomposition and ensemble framework for container throughput forecasting. J. Forecast. 2024 , in press . [ Google Scholar ] [ CrossRef ]
• Shankar, S.; Ilavarasan, P.V.; Punia, S.; Singh, S.P. Forecasting container throughput with long short-term memory networks. Ind. Manag. Data Syst. 2020 , 120 , 425–441. [ Google Scholar ] [ CrossRef ]
• Lee, E.; Kim, D.; Bae, H. Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models. Appl. Sci. 2021 , 11 , 8995. [ Google Scholar ] [ CrossRef ]
• Cuong, T.N.; You, S.-S.; Long, L.N.B.; Kim, H.-S. Seaport Resilience Analysis and Throughput Forecast Using a Deep Learning Approach: A Case Study of Busan Port. Sustainability 2022 , 14 , 13985. [ Google Scholar ] [ CrossRef ]
• Song, X.; Chen, Z.S. Shipping market time series forecasting via an Ensemble Deep Dual-Projection Echo State Network. Comput. Electr. Eng. 2024 , 117 , 109218. [ Google Scholar ] [ CrossRef ]
• Li, X.; Hu, Y.; Bai, Y.; Gao, X.; Chen, G. DeepDLP: Deep Reinforcement Learning based Framework for Dynamic Liner Trade Pricing. In Proceedings of the Proceedings of the 2023 17th International Conference on Ubiquitous Information Management and Communication, IMCOM, Seoul, Republic of Korea, 3–5 January 2023; pp. 1–8. [ Google Scholar ] [ CrossRef ]
• Alqatawna, A.; Abu-Salih, B.; Obeid, N.; Almiani, M. Incorporating Time-Series Forecasting Techniques to Predict Logistics Companies’ Staffing Needs and Order Volume. Computation 2023 , 11 , 141. [ Google Scholar ] [ CrossRef ]
• Lim, S.; Kim, S.J.; Park, Y.; Kwon, N. A deep learning-based time series model with missing value handling techniques to predict various types of liquid cargo traffic. Expert Syst. Appl. 2021 , 184 , 115532. [ Google Scholar ] [ CrossRef ]
• Cheng, R.; Gao, R.; Yuen, K.F. Ship order book forecasting by an ensemble deep parsimonious random vector functional link network. Eng. Appl. Artif. Intell. 2024 , 133 , 108139. [ Google Scholar ] [ CrossRef ]
• Xiao, Z.; Fu, X.J.; Zhang, L.Y.; Goh, R.S.M. Traffic Pattern Mining and Forecasting Technologies in Maritime Traffic Service Networks: A Comprehensive Survey. IEEE Trans. Intell. Transp. Syst. 2020 , 21 , 1796–1825. [ Google Scholar ] [ CrossRef ]
• Yan, R.; Wang, S.A.; Psaraftis, H.N. Data analytics for fuel consumption management in maritime transportation: Status and perspectives. Transp. Res. Part E Logist. Transp. Rev. 2021 , 155 , 102489. [ Google Scholar ] [ CrossRef ]
• Filom, S.; Amiri, A.M.; Razavi, S. Applications of machine learning methods in port operations—A systematic literature review. Transp. Res. Part E-Logist. Transp. Rev. 2022 , 161 , 102722. [ Google Scholar ] [ CrossRef ]
• Ksciuk, J.; Kuhlemann, S.; Tierney, K.; Koberstein, A. Uncertainty in maritime ship routing and scheduling: A Literature review. Eur. J. Oper. Res. 2023 , 308 , 499–524. [ Google Scholar ] [ CrossRef ]
• Jia, H.; Prakash, V.; Smith, T. Estimating vessel payloads in bulk shipping using AIS data. Int. J. Shipp. Transp. Logist. 2019 , 11 , 25–40. [ Google Scholar ] [ CrossRef ]
• Yang, D.; Wu, L.X.; Wang, S.A.; Jia, H.Y.; Li, K.X. How big data enriches maritime research—A critical review of Automatic Identification System (AIS) data applications. Transp. Rev. 2019 , 39 , 755–773. [ Google Scholar ] [ CrossRef ]
• Liu, M.; Zhao, Y.; Wang, J.; Liu, C.; Li, G. A Deep Learning Framework for Baltic Dry Index Forecasting. Procedia Comput. Sci. 2022 , 199 , 821–828. [ Google Scholar ] [ CrossRef ]
• Wang, Y.C.; Wang, H.; Zou, D.X.; Fu, H.X. Ship roll prediction algorithm based on Bi-LSTM-TPA combined model. J. Mar. Sci. Eng. 2021 , 9 , 387. [ Google Scholar ] [ CrossRef ]
• Xie, H.T.; Jiang, X.Q.; Hu, X.; Wu, Z.T.; Wang, G.Q.; Xie, K. High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR images. Front. Neurorobotics 2022 , 16 , 970832. [ Google Scholar ] [ CrossRef ]
• Muñoz, D.U.; Ruiz-Aguilar, J.J.; González-Enrique, J.; Domínguez, I.J.T. A Deep Ensemble Neural Network Approach to Improve Predictions of Container Inspection Volume. In Proceedings of the 15th International Work-Conference on Artificial Neural Networks (IWANN), Gran Canaria, Spain, 12–14 June 2019; Springer: Berlin/Heidelberg, Germany, 2019; Volume 11506, pp. 806–817. [ Google Scholar ] [ CrossRef ]
• Velasco-Gallego, C.; Lazakis, I. Mar-RUL: A remaining useful life prediction approach for fault prognostics of marine machinery. Appl. Ocean Res. 2023 , 140 , 103735. [ Google Scholar ] [ CrossRef ]
• Zhang, X.; Zheng, K.; Wang, C.; Chen, J.; Qi, H. A novel deep reinforcement learning for POMDP-based autonomous ship collision decision-making. Neural Comput. Appl. 2023 , 1–15. [ Google Scholar ] [ CrossRef ]
• Guo, X.X.; Zhang, X.T.; Lu, W.Y.; Tian, X.L.; Li, X. Real-time prediction of 6-DOF motions of a turret-moored FPSO in harsh sea state. Ocean Eng. 2022 , 265 , 112500. [ Google Scholar ] [ CrossRef ]
• Kim, D.; Kim, T.; An, M.; Cho, Y.; Baek, Y.; IEEE. Edge AI-based early anomaly detection of LNG Carrier Main Engine systems. In Proceedings of the OCEANS Conference, Limerick, Ireland, 5–8 June 2023. [ Google Scholar ] [ CrossRef ]
• Theodoropoulos, P.; Spandonidis, C.C.; Giannopoulos, F.; Fassois, S. A Deep Learning-Based Fault Detection Model for Optimization of Shipping Operations and Enhancement of Maritime Safety. Sensors 2021 , 21 , 5658. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Huang, B.; Wu, B.; Barry, M. Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. Int. J. Geogr. Inf. Sci. 2010 , 24 , 383–401. [ Google Scholar ] [ CrossRef ]
• Zhang, W.; Xu, Y.; Streets, D.G.; Wang, C. How does decarbonization of the central heating industry affect employment? A spatiotemporal analysis from the perspective of urbanization. Energy Build. 2024 , 306 , 113912. [ Google Scholar ] [ CrossRef ]
• Zhang, D.; Li, X.; Wan, C.; Man, J. A novel hybrid deep-learning framework for medium-term container throughput forecasting: An application to China’s Guangzhou, Qingdao and Shanghai hub ports. Marit. Econ. Logist. 2024 , 26 , 44–73. [ Google Scholar ] [ CrossRef ]
• Wang, Y.; Wang, H.; Zhou, B.; Fu, H. Multi-dimensional prediction method based on Bi-LSTMC for ship roll. Ocean Eng. 2021 , 242 , 110106. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

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

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals