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The approach first utilizes a method that combines deep learning and temporal point process for predicting irregularly spaced future demands, and then, it formulates the resource allocation problem faced with randomly arrived demands as a stochastic optimization model. The superiority of this approach over existing resource allocation methods is demonstrated using both real-world data and simulated scenarios. The findings highlight the need for a shift from reactive to proactive strategies. Moreover, the research emphasizes the potential of advanced techniques, such as deep learning and stochastic optimization, in disaster management. These techniques can provide valuable tools for policy makers and practitioners in the field, enabling them to make more informed and effective decisions. Policies that encourage the adoption of such optimized resource allocation strategies could lead to more effective disaster response operations. <\/jats:p>","DOI":"10.1287\/isre.2022.0125","type":"journal-article","created":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T09:59:57Z","timestamp":1693994397000},"page":"528-550","source":"Crossref","is-referenced-by-count":15,"title":["Proactive Resource Request for Disaster Response: A Deep Learning-Based Optimization Model"],"prefix":"10.1287","volume":"35","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-9429-5748","authenticated-orcid":false,"given":"Hongzhe","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Management and Economics and Shenzhen Finance Institute, The Chinese University of Hong Kong, Shenzhen 518172, China;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-7766-6730","authenticated-orcid":false,"given":"Xiaohang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-9429-5748","authenticated-orcid":false,"given":"Xiao","family":"Fang","sequence":"additional","affiliation":[{"name":"Lerner College of Business and Economics, University of Delaware, Newark 19716, Delaware"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bintong","family":"Chen","sequence":"additional","affiliation":[{"name":"Lerner College of Business and Economics, University of Delaware, Newark 19716, Delaware"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"109","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.2307\/41703508"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1287\/isre.2021.1016"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1287\/isre.2019.0847"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2015.01.008"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2005.05.016"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2016.1498"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpe.2015.09.039"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2018.1757"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.1120.1549"},{"key":"B10","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop CM","year":"2006"},{"key":"B11","unstructured":"Chen B, Wang Y, Zhou Y (2023) Optimal policies for dynamic pricing and inventory control with nonparametric censored demands. 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