Towards Building an Optimal Humanitarian Logistics Management System in Oman

Project: Other project

Project Details

Description

This research aims to develop conceptual and analytical models to deal with humanitarian logistics problems occurring during potential disasters in Oman. An extensive investigation of the existing emergency management practices in Oman will be conducted. Subsequently, predictive models will be devised for demand estimation alongside models for humanitarian resource location and allocation during disaster times. The research is motivated by the increasing frequency of natural disasters in the Sultanate over the past years. Therefore, it is essential to investigate robust tools to support effectively proactive pre-disaster decisions and to guarantee an acceptable level of responsiveness for post-disaster reactive decisions. The findings of the research are expected to contribute not only to improving the existing humanitarian relief network in Oman, but also to advancing knowledge on management of humanitarian logistics and fill a gap within the existing body of literature.

Layman's description

This research aims to develop conceptual and analytical models to deal with humanitarian logistics problems occurring during potential disasters in Oman. An extensive investigation of the existing emergency management practices in Oman will be conducted. Subsequently, predictive models will be devised for demand estimation alongside models for humanitarian resource location and allocation during disaster times. The research is motivated by the increasing frequency of natural disasters in the Sultanate over the past years. Therefore, it is essential to investigate robust tools to support effectively proactive pre-disaster decisions and to guarantee an acceptable level of responsiveness for post-disaster reactive decisions. The findings of the research are expected to contribute not only to improving the existing humanitarian relief network in Oman, but also to advancing knowledge on management of humanitarian logistics and fill a gap within the existing body of literature.

Key findings

Demand Predictability during Disaster Times Despite the importance of it, demand prediction hasn't been addressed sufficiently in literature. Tofighi et al. (2015) explained that due to special characteristics of disasters, in most cases there is not enough historical data to model uncertain parameters. Moreover, there is no repetition in the occurrence of disasters. As such, it is hard to estimate probabilistic distributions for uncertain parameters in this context. However, by taking into account a portfolio of both random and fuzziness events will lead to robust solutions. For an opposing view, Holgu?n-Veras and Jaller (2012) and Holgu?n-Veras, et al. (2012) demonstrated the feasibility of estimating time-series models to predict the dynamic patterns of resource requests after a disaster. Unlike Earthquakes, tropical cyclones are different. Although both are classified as sudden onset type of disasters, tropical cyclones are more predictable in terms of timing and location which give an allowance of time for preparedness before reaching the country (Sodhi and Tang, 2014). Location and allocation of critical supplies Location of temporary distribution points and allocation of critical items to these points has received more attention comparably. For example, Manoj et al. (2015) proposed a location model for inventory pre-positioning with last mile distribution and vehicle sizing properties. Tofighi et al. (2015) developed two-echelon location allocation network design model using a novel two-stage scenario-based possibilistic-stochastic programming approach. Moreno et al. (2015) proposed a multi-period facility location allocation model with reuse of vehicles under uncertainty of demand, incoming supply, inventory conditions, and roads availability. Balcik and Beamon (2008) develop a model that determines the number and locations of distribution centers in a relief network and the amount of relief supplies to be stocked at each distribution center to meet the needs of people affected by the disasters using a variant of maximal covering location model. An assumption within these models as stated explicitly in Moreno et al. (2015) is that the evacuation of the affected people living in vulnerable areas has already taken place, and that the victims that need supplies are able to go to the relief centers. However, this case is not representative of the exact situation in Oman. Shelters, were relief supplies are sent to, are not fully utilized by the affected people surrounding it and hence demand estimation and accessibility issues are of concern. Thus, this research will aim to model the humanitarian logistics situation in Oman in a realistic way as well as it will contribute original knowledge to literature of this field.
Short titleLarge disasters and catastrophic events such as the 2004 Indian Ocean Tsunami, 2005 Hurricane Katrina, 2007 Cyclone Gonu, 2010 Cyclone Phet in Oman, and the 2011 earthquake and tsunami in Japan illustrate how challenging humanitarian logistics are in resp
AcronymTTotP
StatusNot started

Keywords

  • Humanitarian Logistics
  • Demand prediction
  • location and allocation model
  • Oman
  • Emergency management

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