Reactive GRASP: An Application to a Matrix Decomposition Problem in TDMA Traffic Assignment

Prais, Marcelo; Ribeiro, Celso C.

doi:10.1287/ijoc.12.3.164.12639

Cited by 223 publications

(167 citation statements)

References 14 publications

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“…However, we also examined a second, reportedly better, strategy called reactive GRASP, in which the parameter a is selfadjusted according to the quality of the solutions previously obtained. The algorithm that we propose (Algorithm 5) is an evolution of Algorithm 1 which is inspired by the GRASP algorithm of Prais and Ribeiro [23].…”

Section: The Choice Of the A Parameter: Reactive Graspmentioning

confidence: 99%

GRASP for set packing problems

Delorme

Gandibleux

Rodríguez³

2004

European Journal of Operational Research

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The principles of the Greedy Randomized Adaptative Search Procedure (GRASP) metaheuristic are instantiated for the set packing problem. We investigated several construction phases, and evaluated improvements based on advanced strategies. These improvements include a self-tuning procedure (using reactive GRASP), an intensification procedure (using path relinking) and a procedure involving the diversification of the selection (using a learning process). Two sets of various numerical instances were used to perform the computational experiments. The first set contains randomly generated instances, while the second includes instances relating to real problems in railway planning. No metaheuristic has previously been applied to this combinatorial problem. Consequently, we have discussed GRASPÕs performances both in relation to lower/upper bounds and to the results obtained with Cplex when such results are available. Our analysis, based on the average performances observed, shows the impact of the suggested strategies, and indicates the configuration that produces the best results.

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Section: The Choice Of the A Parameter: Reactive Graspmentioning

confidence: 99%

GRASP for set packing problems

Delorme

Gandibleux

Rodríguez³

2004

European Journal of Operational Research

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“…Each element of the initial population is generated by the constructive phase of the GRASP algorithm (Resende and Ribeiro, 2008) in a reactive procedure (Prais and Ribeiro, 2000). It can be observed that only feasible solutions are generated by the constructive procedure.…”

Section: Proposed Approachmentioning

confidence: 99%

A multiobjective metaheuristic approach for the integrated vehicle and crew scheduling

Prata

2016

J. Transp. Lit.

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Article Info AbstractKeywords: mass transit combinatorial optimization evolutionary algorithmsThe integrated vehicle and crew scheduling problem is a hard Combinatorial Optimization problem widely studied over the years. Taking into consideration the range of variables related to the planning process of vehicles and drivers, there are several practical characteristics of the problem that are not reflected in the solutions generated computationally. Among these characteristics, that were not found in the consulted literature, the most important is the existence of multiple objectives. This paper aims at presenting a multiobjective approach for the integrated vehicle and crew scheduling problem based on Genetic Algorithms. A case study in Portimão (Portugal) is presented and discussed. IntroductionIn what concerns buses, urban transportation planning can be divided in several steps, often considered as follows: network design, timetable creation, vehicle scheduling, crew scheduling and crew rostering. Vehicle scheduling and crew scheduling have been a subject of numerous research activities, due to the significant gains that may result from their optimization (Daduna and Paixão, 1995; Wren and Rousseau, 1999).Several researchers have reported the strong interaction between the vehicle scheduling problem and the crew scheduling problem, emphasizing the gains that can be obtained by considering them together (Freling et al., 2003). The integrated problem is usually known as the Vehicle and Crew Scheduling Problem (VCSP).Traditionally, vehicle scheduling and crew scheduling are done separately and in a sequential manner. The Vehicle Scheduling Problem (VSP) is performed in a first stage, being followed by the Crew Scheduling Problem (CSP). Usually, the CSP is more complex than the VSP, and therefore, in general, it is not a suitable strategy to schedule the vehicles without taking into account the requirements for the crews, since these may form the bottleneck of the whole process.In many public transport companies, particularly in developing countries, the costs of crews and drivers are the most significant. Thus, efforts should be focused in minimizing such costs, even when vehicle scheduling is being performed.For the resolution of the VCSP, several approaches have been reported in the literature. Exact optimization methods, based on branch-and-bound, branch-and-cut, column generation, or lagrangian relaxation are presented by Friberg and Haase (1999), Gaffi and Nonato (1999) Approaches based on heuristics and metaheuristics are presented in Ball et al. (1983), Falkner andRyan (1992), Patrikalakis and Xerocostas (1992), Wren and Gualda (1999), Valouxis and Housos (2002), Rodrigues et al. (2006), Laurent and Hao (2007), Steinzen et al. (2007 and Bartodziej et al. (2009). However, the reported heuristic techniques are not easy to generalize and most of them have been developed for very specific problems, with results reported for a small number of instances.Taking into consideration the range of variables related t...

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“…Several options are possible to solve this problem. In particular, a learning-based strategy termed reactive GRASP has been proposed [21]. In this case, the value of the parameter is chosen in each iteration from a set of discrete values Π = {π 1 , · · · , π m }.…”

Section: Reactive Grasp Vs Hybrid Eamentioning

confidence: 99%

“…Any of the selection mechanisms typically used in EAs can be utilized for this purpose. For example, in [21], a roulette-wheel procedure is proposed. Since we are dealing here with a minimization problem, such a proportional approach would not be possible unless goodness values were appropriately transformed.…”

Section: Reactive Grasp Vs Hybrid Eamentioning

confidence: 99%

A Hybrid GRASP – Evolutionary Algorithm Approach to Golomb Ruler Search

Cotta

Fernández

2004

Lecture Notes in Computer Science

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Abstract. We consider the problem of finding small Golomb rulers, a hard combinatorial optimization task. This problem is here tackled by means of a hybrid evolutionary algorithm (EA). This EA incorporates ideas from greedy randomized adaptive search procedures (GRASP) in order to perform the genotype-to-phenotype mapping. As it will be shown, this hybrid approach can provide high quality results, better than those of reactive GRASP and other EAs.

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Reactive GRASP: An Application to a Matrix Decomposition Problem in TDMA Traffic Assignment

Cited by 223 publications

References 14 publications

GRASP for set packing problems

GRASP for set packing problems

A multiobjective metaheuristic approach for the integrated vehicle and crew scheduling

A Hybrid GRASP – Evolutionary Algorithm Approach to Golomb Ruler Search

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