Abstract
The thesis deals with modeling and analysis of supply planning during or immediately after a natural disaster. In post emergency response planning, the supply of consumable and nonconsumable provisions for both civilians, who evacuate residential areas, and intervention groups at the corresponding shelters, is of immediate importance. In this thesis, provisions supply is modeled and analyzed by introducing the Emergency Supply using Heterogeneous Fleet Problem (ESHFP).
Initially, a Mixed Integer Linear Programming (MILP) mathematical model is introduced for the ESHFP. In order to solve this problem, we have developed a novel heuristic algorithm, which aims in determining the set of routes and the vehicles that can be used to minimize the total supply time, respecting constraints concerning routing, timing, capacity and supply.
Since the corresponding MILP is difficult to be solved to optimality in reasonable time, we have introduced a novel heuristic approach for ESHFP which minimizes the total time needed to collect provisions from available pick up locations and (by using appropriate vehicles among those available) to deliver provisions to a) evacuees at shelters and b) intervention groups at their accommodation sites. The proposed heuristic approach takes into account all necessary constraints described in the MILP model.
To validate the effectiveness this approach, we have applied the proposed algorithm to a series of examples, generated randomly. Furthermore, we have used the proposed algorithm to deal with a real case study involving a significant forest fire in the Province of Teruel in Spain. The results of both the tests and the case study are very encouraging, attesting to the comprehensiveness of the proposed model and the efficiency of the new solution heuristic.