Change search

A Multi-Stage Stochastic Facility Routing Model for Humanitarian Logistics Planning
Norwegian University of Science and Technology, Faculty of Social Sciences and Technology Management, Department of Industrial Economics and Technology Management.
Norwegian University of Science and Technology, Faculty of Social Sciences and Technology Management, Department of Industrial Economics and Technology Management.
2012 (English)MasteroppgaveStudent thesis
##### Abstract [en]

This thesis presents a humanitarian logistics decision model to be used in the event of a disaster. The operations under consideration span from opening of local distribution facilities and initial allocation of supplies, to last mile distribution of aid. An introduction of the field of disaster management is given, which forms the basis for the following description of the disaster response problem faced by the decision maker. Two mathematical models are developed aiming to enable efficient decision making. The mathematical models solve the disaster response problem and seek to maximize the utility of distribution of aid amongst beneficiaries. Utility is expressed in terms of amount of satisfied demand and cost-effectiveness. The main mathematical model is formulated as a multi-stage mixed-integer stochastic model to account for the difficulty in predicting the outcome of a disaster. The model will be applied to earthquakes in particular for reasons of concreteness. Accessibility of new information implicates initiation of distinct operations in the humanitarian supply chain, be it facility location and supply allocation, or last mile distribution planning and execution. The realized level of demand, in addition to the transportation resources available to the decision maker for execution of last mile aid distribution, are parameters treated as random due to uncertainty. Complete information regarding these variables is revealed in stage two. As a direct consequence of treating demand as an uncertain parameter, marginal utility will also be subject to stochasticity. Also, the state of the distribution network is treated as a random parameter due to uncertainty arising from the vulnerability of the local infrastructure. Reception of complete information concerning the state of the infrastructure indicates transition from stage~2 to stage~3. The mathematical models are applied to an illustrative example to demonstrate their application as decision-making tools in practice. An assessment of the applicability and validity of the stochastic program is made, based on several test instances generated by the authors. Results show that instances of considerable size are challenging to solve due to the complexity of the stochastic programming model. Still, optimal solutions may be found within a reasonable time frame. Moreover, findings prove the value of the stochastic programming model to be significant as compared with an deterministic expected value approach.

##### Place, publisher, year, edition, pages
Institutt for industriell økonomi og teknologiledelse , 2012. , 137 p.
##### Identifiers
Local ID: ntnudaim:7742OAI: oai:DiVA.org:ntnu-20979DiVA: diva2:626527
##### Supervisors
Available from: 2013-06-09 Created: 2013-06-09 Last updated: 2013-06-22Bibliographically approved

#### Open Access in DiVA

##### File information
File name FULLTEXT01.pdfFile size 5143 kBChecksum SHA-512
a0045ea4bae4810f335810a8346f50670f0cf0fcac982b0a27576e3f7c9a5ebc816de3f6c648d31b14d9fb137cac88bbc73d2d5c3e4b98882418007f101b7682
Type fulltextMimetype application/pdf
##### File information
File name COVER01.pdfFile size 225 kBChecksum SHA-512
928607ff42b9a2a9a6405075a1d2c761483e85e44febc0759352167acec93939175da0ecf32eeab902457b24d45e2a95db3c2d26a4cec8525f707e4ea3c9e9a3
Type coverMimetype application/pdf
##### File information
File name ATTACHMENT01.zipFile size 43881 kBChecksum SHA-512
c0b1dd2d2b534ebc216cfee587d25a9efccf042b45b66f4efbf8292c9745b2366c0eca7874a981f109e32d42ca26b4050acfca71b9b2cbb9e26fd42e58c31b89
Type attachmentMimetype application/zip
##### By organisation
Department of Industrial Economics and Technology Management