We adopt Benders’ decomposition algorithm to solve scenariobased Stochastic Constraint Programs (SCPs) with linear recourse. Rather than attempting to solve SCPs via a monolithic model, we show that one can iteratively solve a collection of smaller sub-problems and arrive at a solution to the entire problem. In this approach, decision variables corresponding to the initial stage and linear reco...

This paper presents a paired-vehicle recourse strategy for the vehicle routing problem with stochastic demands (VRPSD). In the VRPSD, a fleet of homogeneous capacitated vehicles is dispatched from a terminal to serve single-period customer demands which are known in distribution when planning but only revealed with certainty upon vehicle arrival. While most existing research for this problem fo...

We consider a class of convex approximations for totally unimodular (TU) integer recourse models and derive a uniform error bound by exploiting properties of the total variation of the probability density functions involved. For simple integer recourse models this error bound is tight and improves the existing one by a factor 2, whereas for TU integer recourse models this is the first nontrivia...

We introduce a technique called Concave, Adaptive Value Estimation (CAVE) to approximate recourse functions found in stochastic programs. CAVE constructs a sequence of concave (or convex) piecewise-linear approximations using sample gradients of the recourse function at di erent points in the domain. The result is a nonlinear approximation that is more responsive than traditional stochastic qua...

equity in access to and utilization of health services is a common goal of policy-makers in most countries. the fair allocation of human resources is one of the dimensions of equity, which was evaluated in this study.we evaluated the equity of human resources' distribution among iran's medical science universities between 2005 and 2009 by inequality measures including lorenze curve, gini coeffi...

We adopt Benders’ decomposition algorithm to solve scenariobased Stochastic Constraint Programs (SCPs) with linear recourse. Rather than attempting to solve SCPs via a monolithic model, we show that one can iteratively solve a collection of smaller sub-problems and arrive at a solution to the entire problem. In this approach, decision variables corresponding to the initial stage and linear reco...

Mohammad Hossein Hakimi Meibody was born in 1963 in Meibod, Yazd province. He gets his diploma in Meibod and then for higher education, he went to Isfahan university and exceeded direction of plant science for 4 years and graduated in 1989 with excellent grade. Then, he went to Tehran university and passed course of Plant Systematic in Biology department of faculty of science since 1996 with ex...

In this paper, we consider an adaptive approach to address optimization problems with uncertain cost parameters. Here, the decision maker selects an initial decision, observes the realization of the uncertain cost parameters, and then is permitted to modify the initial decision. We treat the uncertainty using the framework of robust optimization in which uncertain parameters lie within a given ...