TY - GEN
T1 - A rapid decision model of disaster relief logistic, based on internet of things (Iot) data analytics and case-based reasoning
AU - Dachyar, M.
AU - Yadrifil,
AU - Al Ghifari, Maulana Ihsan
N1 - Funding Information:
Research granted by Universitas Indonesia ? PITTA B 2019.
Publisher Copyright:
© IEOM Society International.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Case-based Reasoning (CBR)
KW - Demand forecasting
KW - Disaster
KW - Internet of Things (IoT)
KW - Logistics management
UR - http://www.scopus.com/inward/record.url?scp=85105577421&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85105577421
SN - 9781792361234
T3 - Proceedings of the International Conference on Industrial Engineering and Operations Management
SP - 473
EP - 483
BT - Proceedings of the 2nd African International Conference on Industrial Engineering and Operations Management, 2020
PB - IEOM Society
T2 - 2nd African International Conference on Industrial Engineering and Operations Management, IEOM 2020
Y2 - 7 December 2020 through 10 December 2020
ER -