Demand-Oriented Post-Disaster Repair Scheduling for a Power-Grid-Building System

Post-disaster repair priorities can change when building demand and available supply recover at different rates. This study models a power-grid-building system, defines demand loss as cumulative unmet demand divided by cumulative demand, and uses a genetic algorithm (GA) with deterministic feasibility rules to select repair task order and repair mode. The two GA searches used the same settings, 20 runs for each objective, and 36,200 schedules evaluated per run. In the baseline case, the lowest demand loss found was 0.3925 for the demand-targeted search and 0.3995 for the supply-targeted search. The demand-targeted result was 1.7464% lower and reduced cumulative unmet demand by 238 kW-day. Across the same 20 random seeds, the demand-targeted search produced lower demand loss in 16 runs and the supply-targeted search produced lower demand loss in four runs. In a separate comparison with different computational effort, the demand-targeted GA result had 20.3% lower demand loss than one deterministic greedy schedule. Additional five-run analyses show that the observed results depend on GA settings, crew availability, repair duration, and demand timing. The findings apply to the tested deterministic case study and support demand-aware repair scheduling when demand and supply recover at different rates.