ORCID Identifier(s)

0000-0002-3075-4247

Graduation Semester and Year

2019

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Industrial Engineering

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Jamie K Dr. Rogers

Abstract

Disasters, may that be anthropogenic or natural, cause much havoc to vast area and population. Property and infrastructures get destroyed. People are often in need of urgent relief like dry foods and water to survive. In a country which is in the underdeveloped part of the world, relief and evacuation activities are usually carried out by local government run aid agencies. Most of the time, the local decision makers do the coordination or planning of these humanitarian activities largely based on either past experience or sometimes just pure hunch, which is neither efficient nor economic. Proper planning and coordination in the humanitarian activities in the pre and post disaster planning period can save many human lives and property, while saving money to the local relief agencies as well. In an agricultural country like Bangladesh, rivers are usually important assets. But in a District like Sylhet, Bangladesh, these rivers can sometimes cause serious problems. During monsoon season, there are often too much rain in neighboring Indian up-steam hilly region, where most of these rivers are originated. This leads to a deluge of water abruptly surging through, mostly Surma and Kushiara rivers, in the down-steam regions of Sylhet district, which inundates the vast surrounding areas close to the riverbanks. Due to heavy river-bed sedimentation, these narrow rivers can’t always hold this sudden deluge of water and hence the flash flooding occurs. These floods are called “flash” floods as they stay for a short period of time, but cause large economical damage and human suffering to the surrounding areas. Most of the people living on those affected areas are usually farmers. Heavy inundation causes the crops of those agricultural area to get washed away, on which people of those region mostly subsist on. In such situations, people in that area will starve to death, if the relief agencies don’t promptly respond to their needs for relief. These flash flood problems in that area of Bangladesh is recurrent in nature. So, good working pre and post disaster planning models can really help the local relief agencies to be better prepared for the disaster relief activities, in order to minimize casualties. Keeping that in mind, this research has developed three multi-criteria multi-echelon pre and post disaster planning models, which will help the incumbent agencies with various important pre and post disaster decision making activities. In our study, the first pre-disaster planning model tends to minimize the travelling distances from the tentative supplier to tentative regional warehouse location sites as well as distances from the warehouses to the affected locations. The model picks maximum allowed number of best suppliers from a pool of suppliers, based on their total performance ratings on several evaluation criteria for multiple relief items, as well as other issues like their location distances and available capacities. The model also picks the optimum locations for setting up the warehouses, along with their expected capacities. Quantity of necessary relief goods that needs to be transported under different scenarios will be also obtained as output from this model, which has later been used to determine the appropriate level of prepositioned inventories that we can hold at the selected distribution center locations in the third post-disaster model, to reduce the load on the logistics system in the post disaster period. Once the warehouses have been set up at the selected locations, they are then ready to be used as permanent storage infrastructures. We have used scenario-based approach here to make sure that the facilities are built in such a way that it can accommodate moderate fluctuations in demand that might happen in near future. The second pre-disaster planning model is a bi-objective model that finds appropriate routes to be used among different relevant network nodes considering both the actual path distance and the route reliability under each partially observed scenario. The third post-disaster model manages the prepositioning of relief goods, the distribution of relief goods and medical supplies, evacuation of people who needs medical attention, ensuring the equity of the service provided at each affected node and optimizes the use of available transportation facilities by minimizing the number of trips required. These three models are designed to be solved sequentially to provide the users all the necessary information to design the desired efficient aid logistics network. This research has used the recurrent flash flood problem of Sylhet, Bangladesh as the test case to check the effectiveness of the model that intends to assist in the development an effective relief logistics network. Use of this proposed research will mitigate this recurrent problem that is causing misery to a vast population. To solve the developed MILP models, CPLEX version 12.8 has been used, which has utilized a Branch and Cut algorithm to solve the problems. Obtained results has been demonstrated both numerically and graphically in the result and discussion section of this dissertation for the better visual understanding by the decision maker, which can help them to plan an efficient and economic humanitarian logistics network. In summary , the author of this dissertation is hopeful that this research will provide the aid management authorities with necessary decision making models that will help them effectively in disaster mitigation, which will not only reduce human suffering and wastage of relief goods but also will minimize the overall operational cost at the same time.

Keywords

Humanitarian logistics, Optimization

Disciplines

Engineering | Operations Research, Systems Engineering and Industrial Engineering

Comments

Degree granted by The University of Texas at Arlington

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