A Dynamic Population Steady-State Genetic Algorithm for the Resource-Constrained Project Scheduling Problem

Cervantes, Mariamar; Lova, Antonio; Tormos, Pilar; Barber, Federico

doi:10.1007/978-3-540-69052-8_64

Cited by 10 publications

(6 citation statements)

References 6 publications

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“…Debels and Vanhoucke [39] state that the larger the number of activities, the smaller the population size. Cervantes et al [40] propose a mathematical expression where the relation between the population and the activities is inverse. Based on these statements, Expression (19) and Expression (20) are proposed.…”

Section: Chromosomementioning

confidence: 99%

A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem

Morillo-Torres

Barber

Salido

2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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SAGE PublicationsMorillo-Torres, D.; Barber, F.; Salido, MA. (2017) instance, through a realistic mathematical model. This extension provides an alternative to the current trend of numerous researches about optimization and the manufacturing field, which require the inclusion of components to reduce the environmental impact in the decision-making process. PSPLIB-ENERGY is available at http://gps.webs.upv.es/psplib-energy/..

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Section: Chromosomementioning

confidence: 99%

A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem

Morillo-Torres

Barber

Salido

2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…There is not a standard method to estimate the best population size in this kind of problems, although it is known that the population should be related to the complexity and the problem size. Some studies state that the population size should decrease with the increasing number of activities [37,38]. However, most authors carry out computational experiments to estimate these parameters in a GA.…”

Section: Basic Elements Of the Genetic Algorithmmentioning

confidence: 99%

Mode‐Based versus Activity‐Based Search for a Nonredundant Resolution of the Multimode Resource‐Constrained Project Scheduling Problem

Morillo

Barber

Salido

2017

Mathematical Problems in Engineering

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This paper addresses an energy-based extension of the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP) called MRCPSP-ENERGY. This extension considers the energy consumption as an additional resource that leads to different execution modes (and durations) of the activities. Consequently, different schedules can be obtained. The objective is to maximize the efficiency of the project, which takes into account the minimization of both makespan and energy consumption. This is a well-known NP-hard problem, such that the application of metaheuristic techniques is necessary to address real-size problems in a reasonable time. This paper shows that the Activity List representation, commonly used in metaheuristics, can lead to obtaining many redundant solutions, that is, solutions that have different representations but are in fact the same. This is a serious disadvantage for a search procedure. We propose a genetic algorithm (GA) for solving the MRCPSP-ENERGY, trying to avoid redundant solutions by focusing the search on the execution modes, by using the Mode List representation. The proposed GA is evaluated on different instances of the PSPLIB-ENERGY library and compared to the results obtained by both exact methods and approximate methods reported in the literature. This library is an extension of the well-known PSPLIB library, which contains MRCPSP-ENERGY test cases.

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“…Toni et al (2008) developed a GA with the following settings: AL representation, randomly generated initial population, two selections: Steady state, tournament size 3 (TS-3), maximum number of generations = 300 or maximum number of consecutive generations without best solution improvement = 50, Uniform crossover (UX) with probability and swap mutation (SWM) with probability Pm=0.05. Cervantes et al (2008) developed a steady-state genetic algorithm that used a dynamic population, i.e. the algorithm started with a determined number of individuals and as the search is progressing, the size of the population grows.…”

Section: Review Of Genetic Algorithm Literature For Rcpspmentioning

confidence: 99%

Solving Resource-Constrained Project Scheduling Problem Using Genetic Algorithm

Elshaer

shawky

Elawady

et al. 2017

Journal of Al-Azhar University Engineering Sector

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Due to the combinatorial nature of the resource-constrained project scheduling problem (RCPSP), there is a lot of artificial intelligence methods proposed to solve it. The Genetic Algorithm (GA), one of these methods, is considered to be a valuable search algorithm capable of finding a reasonable solution in a short computational time. The primary objective of this paper is to build a genetic algorithm for solving RCPSP problem aiming at minimizing project's makespan. Based on a comprehensive review of different GAs and a full factorial experiment, a proposed GA has been presented. The proposed algorithm has been tested on a well-known benchmark (PSPLIB). The computation results show that the proposed GA outperforms many published algorithms and on average performs as well as other algorithms. Also, the performance of the algorithm improves in solving large scale problems.

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A Dynamic Population Steady-State Genetic Algorithm for the Resource-Constrained Project Scheduling Problem

Cited by 10 publications

References 6 publications

A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem

A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem

Mode‐Based versus Activity‐Based Search for a Nonredundant Resolution of the Multimode Resource‐Constrained Project Scheduling Problem

Solving Resource-Constrained Project Scheduling Problem Using Genetic Algorithm

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