The purpose of this article is to model and solve an integrated location, routing and inventory problem (LRIP) in cash-in-transit (CIT) sector. In real operation of cash transportation, to decrease total cost and to reduce risk of robbery of such high-value commodity. There must be substantial variation, making problem difficult to formulate. In this paper, to better fit real life applications ...

In practice, due to the lack of information, imprecise variables which come from experts’ empirical data usually appear. In order to deal with these imprecise variables, uncertainty theory is proposed and has been proved to be an efficient method. This paper introduces uncertainty theory into travelling salesman problem (TSP), in which the link travel times are assumed to be uncertain variables...

In this paper, stochastic programming problems are viewed as parametric programs with respect to the probability distributions of the random coefficients. General results on quantitative stability in parametric optimization are used to study distribution sensitivity of stochastic programs. For recourse and chance constrained models quantitative continuity results for optimal values and optimal ...

The variance of fuzzy random variables often appears in fuzzy random programming problems. Based on the definition of the variance of a fuzzy random variable, this paper attempts to deduce several formulas for the variances of trapezoidal fuzzy random variables, in which the randomness is characterized by uniform distribution. Firstly, we give the moment formulas for trapezoidal fuzzy variables...

Random fuzzy theory offers an appropriate mechanism to model random fuzzy phenomena, with a random fuzzy variable defined as a function from a credibility space to a collection of random variables. Based on this theory, this paper presents the results of an investigation into the representation of properties of alternating renewal processes that are described by sequences of positive random fuz...

We study a class of chance-constrained two-stage stochastic optimization problems where second-stage feasible recourse decisions incur additional cost. In addition, we propose a new model, where “recovery” decisions are made for the infeasible scenarios to obtain feasible solutions to a relaxed second-stage problem. We develop decomposition algorithms with specialized optimality and feasibility...

We consider an n-player finite strategic game. The payoff vector of each player is a random vector whose distribution is not completely known. We assume that the distribution of the random payoff vector of each player belongs to a distributional uncertainty set. Using distributionally robust approach, we define a chance-constrained game with respect to the worst-case chanceconstraint. We call s...

An exact algorithm is developed for the chance-constrained multi-area reserve sizing problem in presence of transmission network constraints. The can be cast as a two-stage stochastic mixed integer linear program using sample approximation. Due to complicated structure problem, existing methods attempt find feasible solution based on heuristics. Existing mixed-integer algorithms that applied di...