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Sex and Metaheuristic Metaphors

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As metaheuristics continue to play an increasingly important role in the field of Analytics, the connection between metaheuristics and Analytics invites closer examination. This leads, improbably but interestingly, to the topic of metaheuristic metaphors and – as I’ll describe in a moment – to the topic of sex.    

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Last Updated on Thursday, 27 October 2011 07:16

Computers & OR 39(6), 2012

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Iterated local search and very large neighborhoods for the parallel-machines total tardiness problem

by F. Della Croce, T. Garaix and A. Grosso


We present computational results with a heuristic algorithm for the parallel machines total weighted tardiness problem. The algorithm combines generalized pairwise interchange neighborhoods, dynasearch optimization and a new machine-based neighborhood whose size is non-polynomial in the number of machines. The computational results significantly improve over the current state of the art for this problem.

Last Updated on Monday, 10 October 2011 08:10

EJOR 216(1), 2012

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Solving the two-echelon location routing problem by a GRASP reinforced by a learning process and path relinking

by  Viet-Phuong Nguyen, Christian Prins and Caroline Prodhon

Abstract: The two-echelon location-routing problem (LRP-2E) arises from recent transportation applications like city logistics. In this problem, still seldom studied, first-level trips serve from a main depot a set of satellite depots, which must be located, while second-level trips visit customers from these satellites. After a literature review on the LRP-2E, we present four constructive heuristics and a hybrid metaheuristic: A greedy randomized adaptive search procedure (GRASP) complemented by a learning process (LP) and path relinking (PR). The GRASP and learning process involve three greedy randomized heuristics to generate trial solutions and two variable neighbourhood descent (VND) procedures to improve them. The optional path relinking adds a memory mechanism by combining intensification strategy and post-optimization. Numerical tests show that the GRASP with LP and PR outperforms the simple heuristics and an adaptation of a matheuristic initially published for a particular case, the capacitated location-routing problem (CLRP). Additional tests on the CLRP indicate that the best GRASP competes with the best metaheuristics published.

Last Updated on Friday, 07 October 2011 06:08

EJOR 215(2), 2011

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A skyline heuristic for the 2D rectangular packing and strip packing problems

by  Lijun Wei, Wee-Chong Oon and Wenbin Zhu and Andrew Lim

Abstract: In this paper, we propose a greedy heuristic for the 2D rectangular packing problem (2DRP) that represents packings using a skyline; the use of this heuristic in a simple tabu search approach outperforms the best existing approach for the 2DRP on benchmark test cases. We then make use of this 2DRP approach as a subroutine in an “iterative doubling” binary search on the height of the packing to solve the 2D rectangular strip packing problem (2DSP). This approach outperforms all existing approaches on standard benchmark test cases for the 2DSP.

Last Updated on Friday, 07 October 2011 06:09

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

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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