A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem
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This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem that is based on random keys and tested on a set of standard problems taken from the literature and compared with other approaches.Abstract:
This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions. The chromosomes supplied by the genetic algorithm are adjusted to reflect the solutions obtained by the improvement procedure. The heuristic is tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.read more
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References
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TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Resource-constrained project scheduling: Notation, classification, models, and methods
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Genetic Algorithms and Random Keys for Sequencing and Optimization
TL;DR: A general genetic algorithm to address a wide variety of sequencing and optimization problems including multiple machine scheduling, resource allocation, and the quadratic assignment problem is presented.
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TL;DR: In this article, an extension of deterministic sequencing and scheduling problems, in which the jobs require the use of additional scarce resources during their execution, is considered, and a classification scheme for resource constraints is proposed and the computational complexity of the extended problem class is investigated.
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An overview of genetic algorithms: Part 1, fundamentals
TL;DR: Genetic Algorithms (GAs) are adaptive methods which may be used to solve search and optimisation problems based on the genetic processes of biological organisms, which simulate those processes in natural populations which are essential to evolution.
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