A rapid decision model of disaster relief logistic, based on internet of things (Iot) data analytics and case-based reasoning

M. Dachyar, Yadrifil, Maulana Ihsan Al Ghifari

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The biggest challenge in earthquake emergency logistics lies in determining the demand for emergency logistics support. To forecast the need for emergency logistics support plays a vital role in optimal disaster logistics management. An accurate demand forecasting can prevent an out-of-stock, can save time, and ensure a proper allocation of emergency logistical relief to overcome the long-suffering of victims. This paper aims to design a model for estimating emergency logistical assistance requests after an earthquake. The methodology of Case-based Reasoning (CBR) is applied to build the model. At the same time, the implementation of the Internet of Things (IoT) able to supports retrieving data to the model to produce the forecasting results quickly. The research results show that the error forecast for relief logistics includes blankets, tents, food are respectively 16.78%, 15.99%, and 10.48%. All errors forecast in the range of 10%-20%; thus, the results indicate that the forecast output model is valid to use for predicting emergency logistical assistance requests immediately after an earthquake occurs.

Original languageEnglish
Title of host publicationProceedings of the 2nd African International Conference on Industrial Engineering and Operations Management, 2020
PublisherIEOM Society
Pages473-483
Number of pages11
ISBN (Print)9781792361234
Publication statusPublished - 2020
Event2nd African International Conference on Industrial Engineering and Operations Management, IEOM 2020 - Harare, Zimbabwe
Duration: 7 Dec 202010 Dec 2020

Publication series

NameProceedings of the International Conference on Industrial Engineering and Operations Management
Volume59
ISSN (Electronic)2169-8767

Conference

Conference2nd African International Conference on Industrial Engineering and Operations Management, IEOM 2020
CountryZimbabwe
CityHarare
Period7/12/2010/12/20

Keywords

  • Case-based Reasoning (CBR)
  • Demand forecasting
  • Disaster
  • Internet of Things (IoT)
  • Logistics management

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