Sample average approximation method for the chance-constrained stochastic programming in the transportation model of emergency management

This study proposes a stochastic programming model for the transportation of emergency resource during the emergency response. Since it is difficult to predict the timing and magnitude of any disaster and its impact on the urban system, resource mobilisation is treated in a random manner, and the resource requirements are represented as random variables. Randomness is represented by the chance constraints in this paper. To deal with the difficulty in calculating the chance constraint function, we use conditional value at risk (CVaR) to approximate the chance constraint, and solve the approximation problem of the chance‐constrained stochastic programming by using the sample average approximation (SAA) method. For a given sample, the SAA problem is a deterministic nonlinear programming (NLP) and any appropriate NLP code can be applied to solve the problem. The model and method provide a new way for the emergency logistics management engineering.