Proactive Resource Request for Disaster Response: A Deep Learning-Based Optimization Model
References
- (2012) Metafraud: A meta-learning framework for detecting financial fraud. MIS Quart. 36(4):1293–1327.Crossref, Google Scholar
- (2021) Call for papers–Special issue of Information Systems Research—Unleashing the power of information technology for strategic management of disasters. Inform. Systems Res. 32(4):1490–1493.Link, Google Scholar
- (2019) Don’t mention it? Analyzing user-generated content signals for early adverse event warnings. Inform. Systems Res. 30(3):1007–1028.Link, Google Scholar
- (2015) A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district. Transportation Res. Part E Logist. Transportation Rev. 75:145–163.Crossref, Google Scholar
- (2006) OR/MS research in disaster operations management. Eur. J. Oper. Res. 175(1):475–493.Crossref, Google Scholar
- (2016) Repairable stocking and expediting in a fluctuating demand environment: Optimal policy and heuristics. Oper. Res. 64(6):1285–1301.Link, Google Scholar
- (2015) A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting. Internat. J. Production Econom. 170:321–335.Crossref, Google Scholar
- (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.Link, Google Scholar
- (2012) On the efficiency-fairness trade-off. Management Sci. 58(12):2234–2250.Link, Google Scholar
- (2006) Pattern Recognition and Machine Learning (Springer, Berlin).Google Scholar
- (2023) Optimal policies for dynamic pricing and inventory control with nonparametric censored demands. Working Paper, University of Illinois at Chicago, Chicago, IL, USA.Google Scholar
- (2022) Dynamic pricing and inventory control with fixed ordering cost and incomplete demand information. Management Sci. 68(8):5684–5703.Link, Google Scholar
- (2010) Bounds and heuristics for optimal Bayesian inventory control with unobserved lost sales. Oper. Res. 58(2):396–413.Link, Google Scholar
- (2012) Modeling bounded rationality in capacity allocation games with the quantal response equilibrium. Management Sci. 58(10):1952–1962.Link, Google Scholar
- (2021) Long horizon forecasting with temporal point processes. Lewin-Eytan L, Carmel D, Yom-Tov E, Agichtein E, Gabrilovich E, eds. WSDM ‘21 Fourteenth ACM Internat. Conf. Web Search Data Mining (Association for Computing Machinery, New York), 571–579.Google Scholar
- (2016) Recurrent marked temporal point processes: Embedding event history to vector. Krishnapuram B, Shah M, Smola AJ, Aggarwal C, Shen D, Rastogi R, eds. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 1555–1564.Google Scholar
- (2020) Neural temporal point processes for modelling electronic health records. Alsentzer E, McDermott MB, Falck F, Sarkar SK, Roy S, Hyland SL, eds. Proc. Machine Learn. Health NeurIPS Workshop vol. 119 (PMLR, New York), 85–113.Google Scholar
- (2021) A prescriptive analytics method for cost reduction in clinical decision making. MIS Quart. 45(1):83–115.Crossref, Google Scholar
- (2013) Predicting adoption probabilities in social networks. Inform. Systems Res. 24(1):128–145.Link, Google Scholar
- (2000) Optimized resource allocation for emergency response after earthquake disasters. Safety Sci. 35(1–3):41–57.Crossref, Google Scholar
- (2000) Applying a bootstrap approach for setting reorder points in military supply systems. Naval Res. Logist. 47(6):459–478.Crossref, Google Scholar
- (2005) An ARIMA supply chain model. Management Sci. 51(2):305–310.Link, Google Scholar
- (2019) How to increase the impact of disaster relief: A study of transportation rates, framework agreements and product distribution. Eur. J. Oper. Res. 274(1):126–141.Crossref, Google Scholar
- (2012) On the exact calculation of the fill rate in a periodic review inventory policy under discrete demand patterns. Eur. J. Oper. Res. 218(2):442–447.Crossref, Google Scholar
- (2018) Traits of successful research contributions for publication in ISR: Some thoughts for authors and reviewers. Inform. Systems Res. 29(4):779–786.Link, Google Scholar
- (2016) Disaster management from a POM perspective: Mapping a new domain. Production. Oper. Management 25(10):1611–1637.Crossref, Google Scholar
- (2022a) Learning temporal point processes for efficient retrieval of continuous time event sequences. Proc. AAAI Conf. Artificial Intelligence, vol. 36 (AAAI Press, Palo Alto, CA), 4005–4013.Google Scholar
- (2022b) Modeling continuous time sequences with intermittent observations using marked temporal point processes. ACM Trans. Intelligent Systems Tech. 13(6):103.Crossref, Google Scholar
- (2013) On the appropriate objective function for post-disaster humanitarian logistics models. J. Oper. Management 31(5):262–280.Crossref, Google Scholar
- (2016) (s, s) inventory systems with correlated demands. INFORMS J. Comput. 28(4):603–611.Link, Google Scholar
- (2015) Modeling multiple humanitarian objectives in emergency response to large-scale disasters. Transportation Res. Part E Logist. Transportation Rev. 75:1–17.Crossref, Google Scholar
- (2015) A guide to sample average approximation. Fu M, ed. Handbook of Simulation Optimization, International Series in Operations Research & Management Science, vol. 216 (Springer, New York), 207–243.Crossref, Google Scholar
- (2021) First, do no harm: Predictive analytics to reduce in-hospital adverse events. J. Management Inform. Systems 38(4):1122–1149.Crossref, Google Scholar
- (2017) The neural Hawkes process: A neurally self-modulating multivariate point process. Guyon I, Luxburg U, Bengio S, Wallach HM, Fergus R, Vishwanathan SV, Garnett R, eds. Proc. 31st Internat. Conf. Neural Inform. Processing Systems (Neural Information Processing Systems Foundation, Inc., La Jolla, CA), 6757–6767.Google Scholar
- (2022) Transformer embeddings of irregularly spaced events and their participants. Internat. Conf. Learn. Representation (OpenReview.net).Google Scholar
- (2002) Newsvendor networks: Inventory management and capacity investment with discretionary activities. Manufacturing Service Oper. Management 4(4):313–335.Link, Google Scholar
- (2018) Continuous inventory control with stochastic and non-stationary Markovian demand. Eur. J. Oper. Res. 270(1):198–217.Crossref, Google Scholar
- (2014) Inventory management in humanitarian operations: Impact of amount, schedule, and uncertainty in funding. Manufacturing Service Oper. Management 16(4):595–603.Link, Google Scholar
- (2016) A stochastic optimization model for designing last mile relief networks. Transportation Sci. 50(3):1092–1113.Link, Google Scholar
- (2020) Applying deep learning to the newsvendor problem. IISE Trans. 52(4):444–463.Crossref, Google Scholar
- (2015) Disaster experience and hospital information systems. MIS Quart. 39(2):317–344.Crossref, Google Scholar
- (2016) Inventory-allocation distribution models for postdisaster humanitarian logistics with explicit consideration of deprivation costs. Transportation Sci. 50(4):1261–1285.Link, Google Scholar
- (2012) Dynamic resource allocation in disaster response: Tradeoffs in wildfire suppression. PLoS One 7(4):e33285.Crossref, Google Scholar
- (2017) Editor’s comments: Diversity of design science research. MIS Quart. 41(1):iii–xviii.Crossref, Google Scholar
- (2014) A three-stage stochastic facility routing model for disaster response planning. Transportation Res. Part E Logist. Transportation Rev. 62:116–135.Crossref, Google Scholar
- (2016) A dynamic model for disaster response considering prioritized demand points. Socio-Economic Planning Sci. 55:59–75.Crossref, Google Scholar
- (2017) Hawkes processes for events in social media. Chang S-F, ed. Frontiers in Multimedia Research (Association for Computing Machinery and Morgan & Claypool, New York), 191–218.Crossref, Google Scholar
- (2020) Intensity-free learning of temporal point processes. Internat. Conf. Learn. Representations (OpenReview.net).Google Scholar
- (2021) Neural temporal point processes: A review. Zhou ZH, eds. Twenty-Ninth Internat. Joint Conf. Artificial Intelligence, vol. 5 (ijcai.org), 4585–4593.Google Scholar
- (2018) Reinforcement Learning: An Introduction, 2nd ed. (MIT Press, Cambridge, MA).Google Scholar
- (2009) Forecasting for inventory planning: A 50-year review. J. Oper. Res. Soc. 60(sup1):S149–S160.Crossref, Google Scholar
- (2019) Ultra-short-term industrial power demand forecasting using lstm based hybrid ensemble learning. IEEE Trans. Power Systems 35(4):2937–2948.Crossref, Google Scholar
- (2007) Forecasting daily supermarket sales using exponentially weighted quantile regression. Eur. J. Oper. Res. 178(1):154–167.Crossref, Google Scholar
- (2019) Intermittent demand forecasting with deep renewal processes. NeurIPS 2019 Workshop Temporal Point Processes (Neural Information Processing Systems Foundation, Inc., La Jolla, CA).Google Scholar
- (2007) Multi-objective optimal planning for designing relief delivery systems. Transportation Res. Part E Logist. Transportation Rev. 43(6):673–686.Crossref, Google Scholar
- (2016) An integrated logistic model for predictable disasters. Production. Oper. Management 25(5):791–811.Crossref, Google Scholar
- (2017) Attention is all you need. Guyon I, Luxburg U, Bengio S, Wallach HM, Fergus R, Vishwanathan SV, Garnett R, eds. Adv. Neural Inform. Processing Systems (Neural Information Processing Systems Foundation, Inc., La Jolla, CA), 5998–6008.Google Scholar
- (2018) Learning conditional generative models for temporal point processes. McIlraith SA, Weinberger KQ, eds. AAAI’18/IAAI’18/EAAI’18 Proc. Thirty-Second AAAI Conf. Artificial Intelligence Thirtieth Innovative Appl. Artificial Intelligence Conf. Eighth AAAI Sympos. Ed. Adv. Artificial Intelligence (AAAI Press, Palo Alto, CA), 6302–6309.Google Scholar
- (2023) Deep learning-based imputation method to enhance crowdsourced data on online business directory platforms for improved services. J. Management Inform. Systems 40(2):624–654.Crossref, Google Scholar
- (2018) Improving maximum likelihood estimation of temporal point process via discriminative and adversarial learning. Lang J, eds. Proc. Twenty-Seventh Internat. Joint Conf. Artificial Intelligence (ijcai.org), 2948–2954.Google Scholar
- (2021) Coupled layer-wise graph convolution for transportation demand prediction. Proc. AAAI Conf. Artificial Intelligence vol. 35 (AAAI Press, Palo Alto, CA), 4617–4625.Crossref, Google Scholar
- (2022) Electric vehicle charging demand forecasting using deep learning model. J. Intelligent Transportation Systems 26(6):690–703.Crossref, Google Scholar
- (2020) Self-attentive Hawkes process. Proc. 37th Internat. Conf. Machine Learn. vol. 119 (PMLR), 11183–11193.Google Scholar
- (2009) Analysis and evaluation of an assemble-to-order system with batch ordering policy and compound poisson demand. Eur. J. Oper. Res. 198(3):800–809.Crossref, Google Scholar
- (2021) A deep learning approach for recognizing activity of daily living (ADL) for senior care: Exploiting interaction dependency and temporal patterns. MIS Quart. 45(2):859–896.Crossref, Google Scholar
- (2000) Foundations of Inventory Management, Irwin/McGraw-Hill Series in Operations and Decision Sciences (McGraw-Hill, New York).Google Scholar
- (2020) Transformer Hawkes process. Proc. 37th Internat. Conf. Machine Learn. vol. 119 (PMLR, New York), 11692–11702.Google Scholar

