The Pareto Envelope-Based Selection Algorithm for Multiobjective Optimization

Corne, David; Knowles, Joshua; Oates, Martin J.

doi:10.1007/3-540-45356-3_82

Cited by 624 publications

(295 citation statements)

References 7 publications

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“…The PESA algorithm, proposed for Corne et al (2000), consists in an evolutionary metaheuristic based on the concept of hypergrid. A hypergrid is a grid in hyperplane, which aims to control the density of solutions in a normalized space of objectives.…”

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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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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“…Other improvements include a density estimation technique that is an adaptation of the k-th nearest neighbor method, and a new complex archive truncation procedure. Knowles and Corne 76 presented another algorithm called PAES. This method employs local search and a population archive.…”

Section: Computational Evaluationmentioning

confidence: 99%

“…µ Individuals are selected to update the candidate population while an external archive of non-dominated solutions is maintained. Another genetic algorithm is proposed by Corne et al 76 . This method, called PESA uses an external population EP and an internal one IP to pursuit the goal of finding a well spread Pareto front.…”

Section: Computational Evaluationmentioning

confidence: 99%

Review on Job-Shop and Flow-Shop Scheduling Using

Parveen

Ullah

2011

J. mech. eng.

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“…The evaluation function for an obstacle is shown in Equation (2). In this equation, that evaluation values are worse when a pipe passes through the center of an obstacle and the length of the intersection is long.…”

Section: [The Evaluation Function For Obstacle]mentioning

confidence: 99%

“…A multi-objective optimization problem is an important problem that occurs in a lot of real problems, and a full optimum solution cannot be obtained (Coello [1], Corne [2]). Therefore, solutions are generated using the concept of the Pareto optimum solution, which is a solution that dominates all other solutions for at least one criterion.…”

Section: Introductionmentioning

confidence: 99%

Automatic Design for Pipe Arrangement using Multi-objective Genetic Algorithms

Ikehira¹,

Kimura²,

Ikezaki³

et al. 2005

J.JASNAOE

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This paper presents an automatic design method for pipe arrangement. A pipe arrangement design problem is proposed for a space in which many pipes and objects co-exist. This problem includes large-scale numerical optimization and combinatorial optimization problems, as well as two criteria. For these reasons, it is difficult to optimize the problem using usual optimization techniques such as Random Search. Therefore, a multi-objective genetic algorithm (GAs) suitable for this problem is developed.A pipe is characterized by both a pattern of generation and numerical parameters. The former describes the way the pipe bends and the latter details the length of the straight parts. For this reason, a combination of the pattern of generation and the numerical parameters is used for the solution representation and a new method of crossover is proposed that takes into account interference with obstacles. As the number of pipes increases, it becomes rapidly more difficult to find feasible solutions where pipes do not interfere with each other. Therefore, two modification operators that transform infeasible solution candidates into feasible ones are introduced. One operator modifies the pipe having a lot of interferences with other pipes so that it will not interfere with them, and the other is related with the operation that modifies the pipe that travels through obstacles. Although there are cases in which pipes cannot completely avoid obstacles in practical designs, this situation is taken into consideration by this design process.The proposed method for optimizing a pipe arrangement efficiently is demonstrated through several experiments, and remarks are provided for applying this methodology to a practical pipe arrangement design.

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The Pareto Envelope-Based Selection Algorithm for Multiobjective Optimization

Cited by 624 publications

References 7 publications

A multiobjective metaheuristic approach for the integrated vehicle and crew scheduling

A multiobjective metaheuristic approach for the integrated vehicle and crew scheduling

Review on Job-Shop and Flow-Shop Scheduling Using

Automatic Design for Pipe Arrangement using Multi-objective Genetic Algorithms

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