A constraint programming approach to the hospitals/residents problem

An instance I of the Hospitals/Residents problem (HR) involves a set of residents (graduating medical students) and a set of hospitals, where each hospital has a given capacity. The residents have preferences for the hospitals, as do hospitals for residents. A solution of I is a <i>stable matching</i>, which is an assignment of residents to hospitals that respects the capacity conditions and preference lists in a precise way. In this paper we present constraint encodings for HR that give rise to important structural properties. We also present a computational study using both randomly-generated and real-world instances. We provide additional motivation for our models by indicating how side constraints can be added easily in order to solve hard variants of HR

Replenishment planning for stochastic inventory systems with shortage cost

One of the most important policies adopted in inventory control is the (R,S) policy (also known as the "replenishment cycle" policy). Under the non-stationary demand assumption the (R,S) policy takes the form (R/sub n/,S/sub n/) where R/sub n/ denotes the length of the n/sup th/ replenishment cycle, and S/sub n/ the corresponding order-up-to-level. Such a policy provides an effective means of damping planning instability and coping with demand uncertainty. In this paper we develop a CP approach able to compute optimal (R/sub n/,S/sub n/) policy parameters under stochastic demand, ordering, holding and shortage costs. The convexity of the cost-function is exploited during the search to compute bounds. We use the optimal solutions to analyze the quality of the solutions provided by an approximate MIP approach that exploits a piecewise linear approximation for the cost function.Anglai

A Parallel Memetic Algorithm for Solving

this paper we examine 10 di#erent functions in order (a) to test specific parameter of a parallel execution of memetic algorithms and (b) to evaluate the general computational behavior of MAs. The available theoretical analysis on memetic algorithms does not o#er a tool which could help in a generalized adjustment of control parameters, leaving the choice of the proper operators, parameters and mechanisms to depend on the problem's demands, and the experience and preferences of the researche

On some applications of Ant Colony Optimization metaheuristic to structural

Ant colony optimization metaheuristic (ACO) represents a new class of algorithms particularly suited to solve real- world combinatorial optimization problems. ACO algorithms, published for the first time in 1991 by M. Dorigo and his co-workers, have been applied, particularly starting from 1999 to several kind of optimization problems as the traveling salesman problem, quadratic assignement problem, vehicle routing, sequential ordering, scheduling, graph coloring, management of communications networks and so on

Distributed Genetic Algorithms with a New Sharing Approach in

In this paper, a new distributed genetic algorithm for multiobjective optimization problems is proposed. In this approach, the island model is used with a distributed genetic algorithm and an operation of sharing for Pareto-optimum solutions is performed with the total population. In multiobjective optimization problems, the Pareto-optimum solutions should be derived for designers. Because the Paretooptimum solutions are the set of optimum solutions that are in the relationship of trade-off, not only the accuracy but also the diversity of the solutions should be high. The effect of the distributed populations leads to the high accuracy and the sharing effect leads to the high diversity of solutions. These effects are examined and discussed through some numerical examples that have more than three objective functions