EU/ME - the metaheuristics community

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EU/ME - the metaheuristics community

EJOR 216(2), 2012

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Metaheuristics for the linear ordering problem with cumulative costs

by Abraham Duarte, Rafael Martí, Ada Álvarez and Francisco Ángel-Bell


The linear ordering problem with cumulative costs (LOPCC) is a variant of the well-known linear ordering problem, in which a cumulative propagation makes the objective function highly non-linear. The LOPCC has been recently introduced in the context of mobile-phone telecommunications. In this paper we propose two metaheuristic methods for this NP-hard problem. The first one is based on the GRASP methodology, while the second one implements an Iterated Greedy-Strategic Oscillation procedure. We also propose a post-processing based on Path Relinking to obtain improved outcomes. We compare our methods with the state-of-the-art procedures on a set of 218 previously reported instances. The comparison favors the Iterated Greedy – Strategic Oscillation with the Path Relinking post-processing, which is able to identify 87 new best objective function values.

Last Updated on Friday, 07 October 2011 06:09

Omega 40(3), 2012

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An investigation into the vehicle routing problem with time windows and link capacity constraints

by  Hong Ma, Brenda Cheang, Andrew Lim, Lei Zhang and  Yi Zhu


In this work, we investigate a new, yet practical, variant of the vehicle routing problem called the vehicle routing problem with time windows and link capacity constraints (VRPTWLC). The problem considers new constraints imposed on road links with regard to vehicle passing tonnage, which is motivated by a business project with a Hong Kong transportation company that transports hazardous materials (hazmats) across the city and between Hong Kong and mainland China. In order to solve this computationally challenging problem, we develop a tabu search heuristic with an adaptive penalty mechanism (TSAP) to help manage the company's vehicle fleet. A new data set and its generation scheme are also presented to help validate our solutions. Extensive computational experiments are conducted, showing the effectiveness of the proposed solution approach.

Last Updated on Friday, 07 October 2011 06:09

CFP - Mini Symposium Meta-heuristic Approaches in Design and Analysis of Structures

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Since designers and managers want to find the “best” solutions, design problems and management decisions are always coupled with optimization. That is why meta-heuristic techniques are being increasingly used for solving structural design and maintenance management problems. Meanwhile, application of meta-heuristic algorithms to analysis problems formed an emerging method in combination with the use of minimum energy principle and the resulting method has proved to be very promising in being an alternative to classical methods. This mini symposium aims to bring together structural designers, managers and structural analists who use meta-heuristic algorithms in their works.

Last Updated on Tuesday, 27 September 2011 13:34

Webminar: Maria soto's PhD defense - 29 September 2011 - 10h (GMT +2)

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Memory allocation in embedded systems is one of the main challenges that electronic designers have to face. This part, rather difficult to handle is often left to the compiler with which automatic rules are applied. Nevertheless, an optimal allocation of data to memory banks may lead to great savings in terms of running time and energy consumption. This thesis addresses various versions of the memory allocation problem. For each studied version, the number of constraints in the problem increases and thus the complexity and difficulty to solve it. The number of memory banks, the bank capacities, the size and number of access to data structures, and the conflicting data structures at each time interval are the main constraints presented in the memory allocation problem. In this work, besides some theoretical properties and results, we present ILP formulations and some metaheuristics implemented for each version of the memory allocation problem. Also, we assess the effectiveness of our metaheuristics with the exact methods and other literature metaheuristics with the aim of highlighting what makes the success of metaheuristics for this problem.

Last Updated on Wednesday, 28 September 2011 12:57

Journal of Heuristics 17(5), 2011

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Biased random-key genetic algorithms for combinatorial optimization

by José Fernando Gonçalves and Mauricio G. C. Resende


Random-key genetic algorithms were introduced by Bean (ORSA J. Comput. 6:154–160, 1994) for solving sequencing problems in combinatorial optimization. Since then, they have been extended to handle a wide class of combinatorial optimization problems. This paper presents a tutorial on the implementation and use of biased random-key genetic algorithms for solving combinatorial optimization problems. Biased random-key genetic algorithms are a variant of random-key genetic algorithms, where one of the parents used for mating is biased to be of higher fitness than the other parent. After introducing the basics of biased random-key genetic algorithms, the paper discusses in some detail implementation issues, illustrating the ease in which sequential and parallel heuristics based on biased random-key genetic algorithms can be developed. A survey of applications that have recently appeared in the literature is also given.

Last Updated on Monday, 26 September 2011 05:59

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