Catálogo de publicaciones - libros

An original approach, termed Divide-and-Evolve is proposed to hybridize Evolutionary Algorithms (EAs) with Operational Research (OR) methods in the domain of Temporal Planning Problems (TPPs). Whereas standard Memetic Algorithms use local search methods to improve the evolutionary solutions, and thus fail when the local method stops working on the complete problem, the Divide-and-Evolve approach splits the problem at hand into several, hopefully easier, sub-problems, and can thus solve globally problems that are intractable when directly fed into deterministic OR algorithms. But the most prominent advantage of the Divide-and-Evolve approach is that it immediately opens up an avenue for multi-objective optimization, even though the OR method that is used is single-objective. Proof of concept approach on the standard (single-objective) Zeno transportation benchmark is given, and a small original multi-objective benchmark is proposed in the same Zeno framework to assess the multi-objective capabilities of the proposed methodology, a breakthrough in Temporal Planning.

Variable Neighbourhood Search (VNS) is one of the most recent metaheuristics used for solving combinatorial optimization problems in which a systematic change of neighbourhood within a local search is carried out. In this paper, a variable neighbourhood search algorithm is proposed for Job Shop Scheduling (JSS) problem with makespan criterion. The results gained by VNS algorithm are presented and compared with the best known results in literature. It is concluded that the VNS implementation is better than many recently published works with respect to the quality of the solution.

This paper describes a detailed study of a recursive search algorithm for different optimization problems. Although the algorithm has been originally developed for a project scheduling problem with financial objectives, we show that it can be extended to many other application areas and therefore, can serve as a sub-procedure for various optimization problems. The contribution of the paper is threefold. First, we present a hybrid recursive search procedure for the project scheduling problem with net present value maximization and compare it with state-of-the-art procedures by means of computational tests. Second, we show how the procedure can be adapted to two other application areas: project scheduling with work continuity minimization and the open pit mining problem. Last, we highlight some future research areas where this hybrid procedure might bring a promising contribution.

In a Proportional Representation (PR) electoral system it is assumed that seats are apportioned to the different electoral districts/states according to the corresponding voters’ distribution. In a previous paper we proposed a MILP (Mixed Integer Linear Programming) model to apportion the seats in the European Parliament (EP). Since the exact solution to the problem is not computationally efficient, we have designed a hybrid metaheuristic algorithm based on Variable Neighborhood Search (VNS) and Tabu Search (TS). The proposed approach takes into account the existing situation, guaranteeing a minimum number of seats, independently of the population size of each member. The model is illustrated with actual data and its results are compared with the present apportionment. The results show that the proposed approach can significantly improve the proportionality of the present apportionment.