Author
Jordi Pereira
Other affiliations: Polytechnic University of Puerto Rico, Catholic University of the North, Polytechnic University of Catalonia
Bio: Jordi Pereira is an academic researcher from Adolfo Ibáñez University. The author has contributed to research in topics: Heuristics & Metaheuristic. The author has an hindex of 21, co-authored 56 publications receiving 1330 citations. Previous affiliations of Jordi Pereira include Polytechnic University of Puerto Rico & Catholic University of the North.
Papers published on a yearly basis
Papers
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TL;DR: An ant algorithm is presented that incorporates some ideas that have offered good results with simple balancing problems, and the validity of the proposed algorithms is tested by means of a computational experience with reference instances.
175 citations
TL;DR: In this paper, a genetic algorithm and a GRASP heuristic are used to solve the set covering problem and the MAX-SAT problem, respectively, and the quality of the algorithms is tested in a computational experience with real instances from the metropolitan area of Barcelona, as well as a reduced set of set covering instances from literature.
Abstract: Reverse logistics problems arising in municipal waste management are both wide-ranging and varied. The usual collection system in UE countries is composed of two phases. First, citizens leave their refuse at special collection areas where different types of waste (glass, paper, plastic, organic material) are stored in special refuse bins. Subsequently, each type of waste is collected separately and moved to its final destination (a recycling plant or refuse dump). The present study focuses on the problem of locating these collection areas. We establish the relationship between the problem, the set covering problem and the MAX-SAT problem and then go on to develop a genetic algorithm and a GRASP heuristic to, respectively, solve each formulation. Finally, the quality of the algorithms is tested in a computational experience with real instances from the metropolitan area of Barcelona, as well as a reduced set of set covering instances from the literature.
140 citations
TL;DR: This paper describes the methodology that is applied for the solution of an urban waste collection problem in the municipality of Sant Boi de Llobregat, within the metropolitan area of Barcelona (Spain), and presents the ant colonies heuristics that are used to obtain the solutions.
118 citations
TL;DR: This work proposes a new procedure to solve the simple assembly line balancing problem called Bounded Dynamic Programming, capable of obtaining an optimal solution rate of 267 out of 269 instances, thus obtaining the best-known performance for the problem.
108 citations
TL;DR: An exact enumeration algorithm, which makes use of the lower bounds, is developed to solve the assembly line worker assignment and balancing problem, and shows that it improves upon the best-performing methods from the literature in terms of solution quality and verifies more optimal solutions than the other available exact methods.
94 citations
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Book•
01 Jan 2004
TL;DR: Ant colony optimization (ACO) is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals as discussed by the authors In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization.
Abstract: Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony Ant colony optimization exploits a similar mechanism for solving optimization problems From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO The goal of this article is to introduce ant colony optimization and to survey its most notable applications
6,861 citations
21 Apr 2009
TL;DR: Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species as discussed by the authors.
Abstract: Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species [1]. Artificial ants in ACO essentially are randomized construction procedures that generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components. Since the first ACO algorithm has been proposed in 1991, this algorithmic method has attracted a large number of researchers and in the meantime it has reached a significant level of maturity. In fact, ACO is now a well-established search technique for tackling a wide variety of computationally hard problems.
2,424 citations
TL;DR: The introduction of ant colony optimization (ACO) is discussed and all ACO algorithms share the same idea and the ACO is formalized into a meta-heuristics for combinatorial problems.
Abstract: The introduction of ant colony optimization (ACO) and to survey its most notable applications are discussed. Ant colony optimization takes inspiration from the forging behavior of some ant species. These ants deposit Pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. The model proposed by Deneubourg and co-workers for explaining the foraging behavior of ants is the main source of inspiration for the development of ant colony optimization. In ACO a number of artificial ants build solutions to an optimization problem and exchange information on their quality through a communication scheme that is reminiscent of the one adopted by real ants. ACO algorithms is introduced and all ACO algorithms share the same idea and the ACO is formalized into a meta-heuristics for combinatorial problems. It is foreseeable that future research on ACO will focus more strongly on rich optimization problems that include stochasticity.
2,270 citations
TL;DR: This work deals with the biological inspiration of ant colony optimization algorithms and shows how this biological inspiration can be transfered into an algorithm for discrete optimization, and presents some of the nowadays best-performing ant colonies optimization variants.
1,041 citations
TL;DR: The evolution of ECMPRO that has taken place in the last decade is discussed and the new areas that have come into focus during this time are discussed.
911 citations