Maintenance scheduling for high value assets has been studied for decades and is still a crucial area of research with new technological advancements. The main dilemma of maintenance scheduling is to avoid failures while preventing unnecessary maintenance. The technological advancements in real time monitoring and computational science make tracking asset health and forecasting asset failures possible. The usage and maintenance of assets can be planned more efficiently with the forecasted failure probability and remaining useful life (i.e., prognostic information). The prognostic information is time sensitive. Geographically distributed assets such as off-shore wind farms and railway switches add another complexity to the maintenance scheduling problem with the required time of travel to reach these assets. Thus, the travel time between geographically distributed assets should be incorporated in the maintenance scheduling when one technician (or team) is responsible for the maintenance of multiple assets. This paper presents a methodology to schedule the maintenance of geographically distributed assets using their prognostic information. Genetic Algorithm based solution incorporating the daily work duration of the maintenance team is also presented in the paper. total expected failure cost total maintenance cost total travel cost number of distributed assets maintenance schedule time period cumulative failure probability of asset i on day t expected failure probability progression if the system is left as it is without maintenance failure probability obtained from prognostics at time t expected failure probability progression after a maintenance failure probability obtained from reliability analysis at (t LM) time units after a maintenance expected downtime when a failure occurs in asset i downtime cost per unit time for asset i direct failure cost fixed maintenance cost for asset i travel cost of unit distance cost increase per each time unit in fixed maintenance cost when work duration exceeds Dw city order (asset number visited in the ith order at time t) distance between assets visited π i − 1 t th order and π i t th order travel time of distance d π n , π 0 maintenance duration for the asset i time of last maintenance number of assets to be visited at time unit t working duration for maintenance in a time unit upper limit for the work duration in a time unit 1 if asset i is scheduled for maintenance at time t; 0 otherwise random number for asset i for ranking the visit order string of binary numbers with log 2(T + 1) binary numbers representing the maintenance times for asset i in the given period ( b l o g 2 ( T + 1 ) … b 2 b 1 ) 
Maintenance scheduling of geographically distributed assets with prognostics information