The Resource Leveling Problem with multiple resources using an adaptive genetic algorithm

Ponz-Tienda, José Luís; Piqueras, Víctor Yepes; Pellicer, Eugenio; Moreno-Flores, Joaquin

doi:10.1016/j.autcon.2012.10.003

Cited by 84 publications

(59 citation statements)

References 44 publications

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“…This paper addresses this research gap through the formulation of optimization models of resource leveling problems which employ the objective of minimizing the fluctuation of resource profiles while being enabled to explore potential schedules beyond networks' original duration. It is worth mentioning here that it was not until very recently that devising schedules beyond original project duration to enhance resource leveling attracted research efforts in resource levelling (Ponz-Tienda et al 2013). …”

Section: Research Significancementioning

confidence: 99%

“…A knowledge-based GA multi-objective approach for stochastic extended resource investment project scheduling problems (Xiong et al 2014). Adaptive genetic algorithm was used to solve the resource leveling problems with multiple resources allowing the extension of the project deadline with a penalty (Ponz-Tienda et al 2013). An ant colony (AC) resource leveling optimization model was developed by Ding and Wang (2011).…”

Section: Introductionmentioning

confidence: 99%

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Devising extended-duration schedules of enhanced resource leveling

Rahman

Elazouni

2015

Can. J. Civ. Eng.

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The use of resource management techniques is crucial to resolve conflicts and achieve the efficient utilization of resources. Particularly, resource-leveling techniques schedule activities in unconstrained-resource conditions to minimize fluctuations in resource profiles. According to the literature, resource leveling has typically been performed by considering that the original project duration remains fixed. Virtually, some extension in the project duration might be acceptable should a considerable enhancement in resource leveling be achieved. This paper enhances resource leveling through devising schedules of extended duration that exhibit resource profiles of lower fluctuation. Critical path method (CPM) networks of extended total floats are utilized to provide expanded yet definite spaces to search for schedules of lower resource fluctuation. The modified CPM networks accommodate for employing optimization models and searching optimal or near-optimal solutions. For demonstration, a genetic algorithm model was formulated to solve two case-study networks of 30 and 120 activities. The results indicate that schedules of lower fluctuation in resource profiles were obtained beyond the original networks' duration.Résumé : L'utilisation de techniques de gestion des ressources est primordiale si l'on désire résoudre les conflits et utiliser efficacement les ressources. Plus particulièrement, les techniques de répartition des ressources permettent d'établir l'horaire des activités dans des conditions de ressources non contraintes afin de minimiser les fluctuations des ressources. Selon la littérature, la répartition des ressources était normalement réalisée en considérant que la durée initiale du projet était fixe. Pratiquement, une certaine prolongation de la durée du projet pourrait être acceptable si une amélioration considérable de la répartition des ressources était réalisée. Le présent article porte sur la répartition des ressources en mettant au point des horaires à durée prolongée qui montrent une plus faible fluctuation des ressources. Dans la méthode du chemin critique, les réseaux à marge flottante totale sont utilisés pour fournir des espaces agrandis quoique définis pour trouver des horaires comportant une plus faible fluctuation des ressources. Les réseaux modifiés de la méthode du chemin critique s'adaptent à l'utilisation de modèles d'optimisation et à la recherche de solutions optimales -quasi optimales. À des fins de démonstration, un modèle d'algorithme génétique a été formulé pour résoudre deux études de cas de réseaux comportant respectivement 30 et 120 activités. Les résultats indiquent que les horaires ayant des fluctuations de ressources plus faibles ont été obtenus au-delà de la durée originale des réseaux. [Traduit par la Rédaction]

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Section: Research Significancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Devising extended-duration schedules of enhanced resource leveling

Rahman

Elazouni

2015

Can. J. Civ. Eng.

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“…Since 2002, Lalescu has been devoted to studying the application of genetic algorithms in course scheduling systems and developed the software FET. However, the rules and conditions of course scheduling in foreign countries differ from those in China [12] [13].…”

Section: State Of Artmentioning

confidence: 99%

Improved Adaptive Genetic Algorithm for Course Scheduling in Colleges and Universities

Wang¹

2018

Int. J. Emerg. Technol. Learn.

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Abstract-Traditional artificial intelligence and computer-aided course scheduling schemes can no longer meet the increasing demands caused by the informatization of teaching management in colleges and universities. To address this problem, this study designed an improved adaptive genetic algorithm that is based on hard and soft constraints for course scheduling. First, the mathematical model of the genetic algorithm was established. The combination of time, teacher, and course number was regarded as the gene coding. The weekly course schedule of each class was a chromosome, and the course schedule of the entire school was the initial population. The fitness was designed according to the priority of each class, curriculum dispersion, and teacher satisfaction. Local columns between individuals were selected through the roulette principle for a variation of crossover and random columns. Iterative calculation was implemented on the basis of the default mutation and crossover rates to study the optimal course scheduling scheme. Experimental results demonstrate that the improved adaptive genetic algorithm is superior to the original genetic algorithm. When the number of iterations is 150, population evolution is optimal and the fitness does not increase. When the population size is 150 classes, the average scheduling time is the shortest. The basic, adaptive, and improved adaptive genetic algorithms are compared in terms of the number of average iterations required for convergence, maximum individual fitness, and average individual fitness. Comparison results show that the improved adaptive genetic algorithm is superior to the two other algorithms. This study provides references for the model building and evaluation of course scheduling in colleges and universities.

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“…In construction management, natureinspired algorithms have been used to solve problems such as project site layout [8], time-cost trade-off [9], and resource leveling [10]. In structural engineering, examples of nature-inspired applications include: truss design [11,12] and frame design [13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Implementation of Nature-inspired Optimization for Civil Engineering: A Comparative Study of Symbiotic Organisms Search

Prayogo

Cheng

Prayogo

2017

ced

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Abstract:The increasing numbers of design variables and constraints have made many civil engineering problems significantly more complex and difficult for engineers to resolve in a timely manner. Various optimization models have been developed to address this problem. The present paper introduces Symbiotic Organisms Search (SOS), a new nature-inspired algorithm for solving civil engineering problems. SOS simulates mutualism, commensalism, and parasitism, which are the symbiotic interaction mechanisms that organisms often adopt for survival in the ecosystem. The proposed algorithm is compared with other algorithms recently developed with regard to their respective effectiveness in solving benchmark problems and three civil engineering problems. Simulation results demonstrate that the proposed SOS algorithm is significantly more effective and efficient than the other algorithms tested. The proposed model is a promising tool for assisting civil engineers to make decisions to minimize the expenditure of material and financial resources.

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The Resource Leveling Problem with multiple resources using an adaptive genetic algorithm

Cited by 84 publications

References 44 publications

Devising extended-duration schedules of enhanced resource leveling

Devising extended-duration schedules of enhanced resource leveling

Improved Adaptive Genetic Algorithm for Course Scheduling in Colleges and Universities

A Novel Implementation of Nature-inspired Optimization for Civil Engineering: A Comparative Study of Symbiotic Organisms Search

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