Stochastic location optimization in a dynamic environment

Existing location optimization solutions only consider the positioning of resouces/seeds at the best location in a target area by minimizing a certain metric over distance. In reality, what really matters is time. In this paper, the location optimization problem is formulated as the expected response time minimization problem rather than a distance minimization problem. Moreover, we propose an algorithm that takes into consideration various stochastic factors which affect the location optimization problem, such as non-uniform probability distribution of the demands, road congestion level, and vehicles' maximum speed. Our proposed algorithm shows promising performance when the disparity among vehicle capabilities (e.g., maximum speed) are large and the environment constraints (e.g., traffic jam) are taken into consideration.