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This paper presents a stochastic integer program developed to determine how to schedule inspection, damage assessment, and repair tasks so as to optimize the post-earthquake restoration of the electric power system. The objective of the optimization is to minimize the average time each customer is without power, and a genetic algorithm is used to solve it. The effectiveness of the schedules recommended by the optimization are evaluated by running a detailed discrete event simulation model of the restoration process with both the optimization-generated schedules and the power company's original schedules, and comparing the resulting restorations according to three measures-average time each customer is without power, time required to restore 90% of customers, and time required to restore 98% of customers. The optimization and simulation models both consider all the earthquakes that could affect the power system and represent the uncertainty surrounding expected restoration times. The models were developed through an application to the Los Angeles Department of Water and Power (LADWP) electric power system, but the general approach is extendable to other electric power systems, other lifelines, and other hazards. SCHEDULING OF POST-EARTHQUAKE ELECTRIC POWER RESTORATION TASKS 267 the LADWP system, in which the optimization is used to generate schedules and the simulation is used to compare restoration times resulting from the optimization-generated and current LADWP schedules. We conclude with a discussion of the strengths and limitations of the analysis. BACKGROUND Optimization of post-disaster lifeline restorationMany past studies have modelled the post-disaster restoration processes of various lifelines in an effort to estimate expected restoration times (e.g. Reference [7]), and several have compared the performance of a few alternative restoration strategies (e.g. References [6,8,9]). Less research has been done to optimize post-disaster restoration strategies, but several such studies do exist. Considering multiple types of lifelines simultaneously, Kozin and Zhou [10] develop a Markov process to represent lifeline restoration processes, then use dynamic programming to estimate the repair resources (in monetary units) required for each time step and lifeline so as to maximize the expected economic return from lifeline functioning. Nojima and Kameda [11] use tree structures from graph theory to identify a prioritized set of components for repair to achieve network connectivity. They then use Horn's algorithm [12] to determine the optimal repair order for damaged components within the tree structure. Noda [13] uses a neural network to minimize the likelihood of post-earthquake functional loss for a telephone system. Wang et al. [14] develop models to address two problems: (1) how to optimally locate repair depots and determine the amount of each resource to transport from each depot to each damage location so as to minimize transportation time (assumed to represent minimal restoration time), and (2) how to deter...

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