| dc.description.abstract | Natural disasters are inevitable and inflict devastating effects, in terms of human injuries and property damage. These damages can disrupt the traffic and lifeline systems, obstructing the operation of rescue machines, rescue vehicles, ambulances and relief workers. In practice, not only the repair work teams rescue the disaster area, but also supply work teams support the logistic to the repair work teams. If the demand of repair work teams is not supplied in time, the schedule of repair work would be delayed, which will not only affect the rescue efficiency but can also increase human injuries. Most of the logistical models in the past were formulated with the average travel times, meaning that stochastic disturbances arising from variations in vehicle travel times in actual operations were neglected. In the worst case scenario, where vehicle travel times fluctuate wildly during daily operations, the planned schedule could be disturbed enough to lose its optimality.
Hence, we employ network flow techniques, with the objective of minimizing the total system cost, as well as the emergency repair schedule and related operating constraints, to construct a logistical support scheduling model under stochastic travel times. Then, we modified the variable travel time parameters in the stochastic supply work scheduling model as fixed variable to develop a deterministic scheduling model to help the authorities for planning effective logistical support schedules. In addition, we also develop a simulation-based evaluation method to evaluate the schedules obtained from the manual method, the deterministic and the stochastic scheduling models, in simulated real world operations. Our model is formulated as an integer multiple-commodity network flow problem with side constraints which is characterized as NP-hard. To efficiently solve realistically large problems occurring in practice, we use a problem decomposition technique and greedy algorithm, coupled with the use of a mathematical programming solver CPLEX, to develop a heuristic algorithm. Finally, to evaluate the model and the solution algorithm in practice, we perform a case study using real data of the 1999 Chi-Chi earthquake in Taiwan. The test results show that the models and the solution algorithm are better than actual operations and would be useful for logistical support scheduling under stochastic travel times.
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