Joint chance constrained shortest path problem with Copula theory

摘要: In this paper, we investigate the constrained shortest path problem where arc resources of are dependent normally distributed random variables. A model is presented to maximize probability all constraints, while not exceeding a certain amount. We assume that rows constraint matrix dependent, so use marginal distribution Copula functions, instead functions and dependency driven by an appropriate Archimedean Copula. Then, transform joint chance-constrained problems into deterministic second-order cone programming. This new approach considers between resource consumptions connects Copulas stochastic (SRCSPP). The results indicate effect levels considerable. Moreover, linear relaxation SRCSPP generally convex; thus can lower upper bounds programming approximation solve problem. experimental show with theory achieve efficient performance.