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A greedy randomized adaptive search procedure for the 2partition problem
 Operations Research
, 1994
"... Abstract. Today, a variety of heuristic approaches are available to the operations research practitioner. One methodology that has a strong intuitive appeal, a prominent empirical track record, and is trivial to efficiently implement on parallel processors is GRASP (Greedy Randomized Adaptive Search ..."
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Cited by 478 (75 self)
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Abstract. Today, a variety of heuristic approaches are available to the operations research practitioner. One methodology that has a strong intuitive appeal, a prominent empirical track record, and is trivial to efficiently implement on parallel processors is GRASP (Greedy Randomized Adaptive Search Procedures). GRASP is an iterative randomized sampling technique in which each iteration provides a solution to the problem at hand. The incumbent solution over all GRASP iterations is kept as the final result. There are two phases within each GRASP iteration: the first intelligently constructs an initial solution via an adaptive randomized greedy function; the second applies a local search procedure to the constructed solution in hope of finding an improvement. In this paper, we define the various components comprising a GRASP and demonstrate, step by step, how to develop such heuristics for combinatorial optimization problems. Intuitive justifications for the observed empirical behavior of the methodology are discussed. The paper concludes with a brief literature review of GRASP implementations and mentions two industrial applications.
Reactive GRASP with Path Relinking for Channel Assignment In Mobile Phone Networks
, 2001
"... The Frequency Assignment Prnblem (FAP) arises in wireless networks when the number of available frequency channels is smaller than the number of users. FAP is NPhard and plays an important role in the network planning. Usually, the number of available channels is much smaller than the number of use ..."
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Cited by 6 (1 self)
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The Frequency Assignment Prnblem (FAP) arises in wireless networks when the number of available frequency channels is smaller than the number of users. FAP is NPhard and plays an important role in the network planning. Usually, the number of available channels is much smaller than the number of users accessing the wireless network. In this case, the reuse of frequency channels is mandatory. Consequently, this may cause interference. Nowadays, cellular phone operators use various techniques designed to cope with channel shortage and, as a consequence, to avoid interference. For instance, frequency division by time or code, and local frequency clustering models have been used. These techniques are bounded by the number of users, i.e. as the number of users increases, they tend to become obsolete. In this work, we propose to minimize the total interference of the system, using a netaheuristic based on GRASP (Greedy Random ized Adaptive Search Procedure). A reactive heuristic has been used in order to autonmtically balance GRASP parameters. Furthermore, Path Relinking, which consists of an intensification strategy, has been applied. We report experimental results given by our proposed approach.
An Improved GRASP Interactive Approach for the Vehicle Routing Problem With Backhauls
 ESSAYS AND SURVEYS ON METAHEURISTICS
, 2001
"... ..."
A Bibliography of GRASP
"... This document contains references related to GRASP (greedy randomized adaptive search procedure) that have either appeared in the literature or as technical reports. If you are aware of any uncited reference, incorrectly cited reference, or update to a cited reference, please contact Mauricio G. C. ..."
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Cited by 2 (2 self)
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This document contains references related to GRASP (greedy randomized adaptive search procedure) that have either appeared in the literature or as technical reports. If you are aware of any uncited reference, incorrectly cited reference, or update to a cited reference, please contact Mauricio G. C. Resende at the address given at the end of this document.
Directing the Search of Evolutionary and NeighbourhoodSearch Optimisers for the Flowshop Sequencing Problem with an IdleTime Heuristic
 In The 1997 AISB Workshop on Evolutionary Computing
, 1997
"... . This paper presents a heuristic for directing the neighbourhood (mutation operator) of stochastic optimisers, such as evolutionary algorithms, so to improve performance for the flowshop sequencing problem. Based on idle time, the heuristic works on the assumption that jobs that have to wait a ..."
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Cited by 2 (1 self)
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. This paper presents a heuristic for directing the neighbourhood (mutation operator) of stochastic optimisers, such as evolutionary algorithms, so to improve performance for the flowshop sequencing problem. Based on idle time, the heuristic works on the assumption that jobs that have to wait a relatively long time between machines are in an unsuitable position in the schedule and should be moved. The results presented here show that the heuristic improves performance, especially for problems with a large number of jobs. In addition the effectiveness of the heuristic and search in general was found to depend upon the neighbourhood structure in a consistent fashion across optimisers. 1 Introduction One problem of interest in the artificial intelligence and operations research scheduling communities is the flowshop sequencing problem. The aim of this problem is, quite simply, to find a sequence of n jobs that minimises the makespan  the time for all of the jobs to be proces...
AN APPROACH FOR SOLVING THE INTEGRATIVE FREIGHT MARKET SIMULATION
"... The Integrative Freight Market Simulation (IFMS) is a new modeling framework for urban goods movements that attempts the development of a comprehensive freight transportation demand model that depicts both commodity flows and vehicle trips (HolguínVeras, 2000). This model has a twolevel solution a ..."
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The Integrative Freight Market Simulation (IFMS) is a new modeling framework for urban goods movements that attempts the development of a comprehensive freight transportation demand model that depicts both commodity flows and vehicle trips (HolguínVeras, 2000). This model has a twolevel solution approach. One level deals with the economic problem of estimating the provision of freight service consistent with market equilibrium and profit maximization, while the other level deals with the network problem of constructing tours which are consistent with the economic solution and other system constraints. The network problem is not strictly a traditional routing problem in that competition is incorporated into the process. To capture the effect of imperfect information and to create an environment of economic competition, a specified number of randomly selected nodes are available to two or more freight companies while the rest are available only to one company. This paper describes a method for solving the IFMS which consists of a “cluster first, route second ” approach. The clustering stage consists of a geometric clustering combined with a generalized assignment problem (GAP) to create clusters from which feasible tours can be constructed (Nygard et al. 1988). An initial clustering is performed using a version of the radial sweep method of Gillett and Miller (1974) which provides an estimate of the cost coefficients for the GAP. The GAP is solved to determine a minimum cost clustering with constraints that insure that the resulting node clusters can be turned into
Thorson, HolguínVeras and Mitchell 1 MULTIVEHICLE ROUTING WITH PROFITS AND MARKET COMPETITION
"... This paper deals with a multiple vehicle routing problem in which profit is maximized subject to competition. This problem will be referred to as the multiple vehicle routing problem with profits and competition (MVRPPC). The MVRPPC differs from traditional multivehicle routing problems in three way ..."
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This paper deals with a multiple vehicle routing problem in which profit is maximized subject to competition. This problem will be referred to as the multiple vehicle routing problem with profits and competition (MVRPPC). The MVRPPC differs from traditional multivehicle routing problems in three ways: (1) competition is incorporated into the process, (2) the objective is to maximize profits rather than minimize costs, and (3) it is assumed that trucks leave and return to their home bases empty, thus any freight picked up in a tour must be delivered in that same tour (which represents the case of forhire carriers). The solution method takes a “cluster first, route second ” approach in which the clustering phase combines a geometric clustering with a generalized assignment problem (GAP). The routing is performed using a tabu search. To get an idea of how well the tabu search performs, an alternative method for routing was developed which consisted of a mixed integer program (MIP) based on the flow formulation of the traveling salesman problem. The solution approach was applied to a series of problems of varying size and complexity with the routing performed by both the tabu search and the MIP formulations. A comparison of the tabu search and MIP solutions indicated that the tabu search solutions were practically the same than the corresponding MIP solutions, with tabu search objective function values which were no more than 0.70 % of the MIP values. As an illustration of the potential uses of the methodologies developed, the paper analyzes the role of the degree of market transparency on the geographic segmentation of the market. Thorson, HolguínVeras and Mitchell 3
Proceedings of the TwentySecond International Joint Conference on Artificial Intelligence Coordinating Logistics Operations with Privacy Guarantees
"... Several logistics service providers serve a certain number of customers, geographically spread over an area of operations. They would like to coordinate their operations so as to minimize overall cost. At the same time, they would like to keep information about their costs, constraints and preferenc ..."
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Several logistics service providers serve a certain number of customers, geographically spread over an area of operations. They would like to coordinate their operations so as to minimize overall cost. At the same time, they would like to keep information about their costs, constraints and preferences private, thus precluding conventional negotiation. We show how AI techniques, in particular Distributed Constraint Optimization (DCOP), can be integrated with cryptographic techniques to allow such coordination without revealing agents’ private information. The problem of assigning customers to companies is formulated as a DCOP, for which we propose two novel, privacypreserving algorithms. We compare their performances and privacy properties on a set of Vehicle Routing Problem benchmarks. 1