asimulation-optimization approach is proposed for replenishment planning andshelf space allocation. the customers’ demand is divided into fixed andvariable demand. each customer follows a given path while shopping in thestore. by dividing the store space into several zones and calculating therelated substantial and random utilities, an analytical equation is proposedfor the probability of zone ...

Automated theorem provers use search strategies. Unfortunately, no strategy is uniformly successful on all problems. This motivates us to spend the available resources in terms of processors and time on diierent strategies. In this paper, we develop the basic concept of the complementarity of strategy sets. The problems of the initial strategy selection are discussed in detail. The paper also c...

Histology acquisition often introduces tissue distortions that prevent a smooth 3D reconstruction from being built. In this paper, we propose a method of atlas-guided optimization to the piecewise registration scheme we developed previously. It takes the information of several consecutive slices in a neighbourhood into account. In order to achieve an accurate anatomic presentation, we run the m...

in this paper, we introduce a model  to optimization of milk run system that is one of vrp problem with time window and uncertainty in inventory. this approach led to the routes with minimum cost of transportation while satisfying all inventory in a given bounded set of uncertainty .the problem is formulated as a robust optimization problem. since the resulted problem illustrates that grows up ...

One of the simplest ways of optimizing a problem without knowledge of its gradient, is to iteratively and randomly pick a sequence of candidate solutions that improve on the fitness. This paper shows mathematically that to exhaust the optimum of a problem, the search-range from which the candidate solutions are picked, must be decreased during an optimization run. A simple technique for doing t...

This paper investigates simulation-based optimization problems with a stochastic objective function, stochastic output constraints, and deterministic input constraints. More specifically, it generalizes classic Response Surface Methodology (RSM) to account for these stochastic and deterministic constraints. This extension is obtained through the generalization of the estimated steepest descent—...