A parallel greedy approach enhanced by genetic algorithm for the stochastic rig routing problem

Scheduling drilling activities for oil and gas exploration involves solving a problem of optimal routing of a fleet of vehicles that represent drilling rigs. Given a set of sites in some geographic area and a certain number of wells to drill in each site, the problem asks to find routes for all the rigs, minimizing the total travel time and respecting the time windows constraints. It is allowed that the same site can be visited by many rigs until all the required wells are drilled. An essential part of the considered problem is the uncertain drilling time in each site due to geological characteristics that cannot be fully predicted. A mixed integer programming model and a parallel greedy algorithm proposed in an earlier study can be used for solving very small-sized instances. In this paper, a graphics processing unit (GPU) accelerated genetic algorithm is developed for using in the greedy algorithm as a subroutine. This approach was implemented and tested on a high-performance computing cluster and the experiments have shown good ability to solve large-scale problems.