Rate of Benefit Gain-Based Greedy Algorithms for Finding Near-Optimal Repair Schedules for Recovery of Lifeline Networks

Abstract

This paper presents two greedy scheduling algorithms for recovery of the damaged lifeline network. Unlike genetic algorithm or simulated annealing, the proposed algorithms do not involve random processes. One of the algorithms is for assigning multiple heterogeneous engineers and preparing schedules for repairing independent damaged components, while the other is for finding repair schedule for a single engineer considering network constraint. Both the algorithms use rate of benefit gain as an index in deciding which component should be repaired next, and by whom. The target use of these algorithms is to serve as an initial solution for a multi-agent system which includes fine grain details. With numerical experiments, it is demonstrated that the proposed algorithms can find near-optimal repair schedules and solve problems involving large number of variables.