A model for resource-constrained project scheduling using adaptive PSO

Kumar, Neetesh; Vidyarthi, Deo Prakash

doi:10.1007/s00500-015-1606-8

Cited by 38 publications

(9 citation statements)

References 44 publications

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“…An improved method for determining the position of the particles and the velocities was proposed. Moreover, the objective of PSO was defined to obtain the optimized makespan (Kumar & Vidyarthi, 2016). A hybrid particle swarm optimization was developed against the RCPSP, where a maximum of one interruption per activity is allowed.…”

Section: Particle Swarm Optimization (Pso) and Rcpspmentioning

confidence: 99%

Review of conventional metaheuristic techniques for resource-constrained project scheduling problem

Golab¹,

Gooya²,

Falou³

et al. 2022

10.5267/j.jpm

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This paper is concerned with an overview of the Resource-Constrained Project Scheduling Problem (RCPSP) and the conventional meta-heuristic solution techniques that have attracted the attention of many researchers in the field. Therefore, researchers have developed algorithms and methods to solve the problem. This paper addresses the single-mode RCPSP where the objective is to optimize and minimize the project duration while the quantities of resources are constrained during the project execution. In this problem, resource constraints and precedence relationships between activities are known to be the most important constraints for project scheduling. In this context, the standard RCPSP is presented. Then, the classifications of the collected papers according to the year of publication and the different meta-heuristic approaches applied are presented. Five weighted articles and their meta-heuristic techniques developed for RCPSP are described in detail and their results are summarized in the corresponding tables. In addition, researchers have developed various conventional meta-heuristic algorithms such as genetic algorithms, particle swarm optimization, ant colony optimization, bee colony optimization, simulated annealing, evolutionary algorithms, and so on. It is stated that genetic algorithms are more popular among researchers than other meta-heuristics. For this reason, the various conventional meta-heuristics and their corresponding articles are also presented to give an overview of the conventional meta-heuristic optimizing techniques. Finally, the challenges of the conventional meta-heuristics are explored, which may be helpful for future studies to apply new suitable techniques to solve the Resource-Constrained Project Scheduling Problem (RCPSP).

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Section: Particle Swarm Optimization (Pso) and Rcpspmentioning

confidence: 99%

Review of conventional metaheuristic techniques for resource-constrained project scheduling problem

Golab¹,

Gooya²,

Falou³

et al. 2022

10.5267/j.jpm

View full text Add to dashboard Cite

show abstract

“…Bu sonuçlar, öncelik kuralının başarısının veri kümesine bağlı olarak değiştiğini doğrulmakla birlikte, melez kuralların daha başarılı sonuçlar verebileceğini göstermektedir. Algoritmanın başarısını ölçmek için Kumar ve Vidyarthi'nin [20] çalışmasında olduğu gibi Eş. 1'de verilen formülle ortalama sapma değerleri hesaplanmıştır.…”

Section: öNcelik Kuralları (Priority Rules)unclassified

Priority rule for resource constrained project planning problem with predetermined work package durations

Ercan

Tanrıöver

2020

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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 Comparison of priority rules for the RCPSP  Examination of hybrid rules for the RCPSP  Proposition of a new priority rule for the RCPSP Figure A. A project schedule example for a single resourcePurpose: Project planning has a significant role in project management to progress regularly and complete a project on time. The studies for Resource Constrained Project Scheduling Problem, where the available resources are limited, indicate that the choice of priority rule affects the success of planning and there is not a single priority rule for reaching the optimum solution for all problems. In this study, a priority rule that can be used to solve Resource Constrained Project Scheduling Problem with predetermined work packages is proposed. Theory and Methods:In order to explore the most common 8 priority rules PSPLib J30, J60, and J120 datasets are used. Since the success of the rules changed according to the dataset, Maximum Area and Longest Progressing Time rules are selected from the priority rules, which give the most successful results, and examined. Two priority rules, which are Area and Resource Usage and Area and Process Time, has been proposed and tested on the PSPLib J30, J60, J120 dataset. The success of the rules is measured with the number of shortest schedule generation and average deviation for both serial and parallel scheduling. Results:In general, it is observed that the proposed Area and Resource Usage rule gives successful results in both serial and parallel scheduling, and it produces better results than the rules; Maximum Area, Highest Resource Usage, and Longest Progressing Time which are used separately. Also, the proposed rule was one of the two most successful one among the examined rules. It gave 57% better results than the First Eligible Activity rule, and produced the best results in the j120 dataset, where both the sample and the number of activities were higher.The proposed rule benefits from the shortest time generation feature of Maximum Area rule and the advantage of Highest Resource Usage rule that produces the closest to the optimum. In this way, it is able to produce more numbers of schedule which are close to optimum. In addition, the proposed Area and Resource Usage rule produces solutions below the average execution time of the other tested rules. Conclusion:The increase in the number of activities and samples leads to a decrease in the number of shortest-time schedule production for all priority rules and to an increase in the mean deviation. However, it was found that Maximum Area rule was successful in producing the shortest schedule and Highest Resource Usage rule was successful resulting in the lowest average deviation value. Therefore, a hybrid priority rule that takes these two rules into consideration is proposed. The proposed rule is successful when both the number of shortest-term schedule generation and average deviation are taken into consideration. It also performed in a time period below average execution time when compared with others.

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“…Al aplicar este método es posible que algunas soluciones resulten infactibles, puesto que violan las restricciones de precedencia. Para evitar esto, se utiliza el método Valid Particle Generator (VPG) [21] para corregir este problema y obtener siempre soluciones factibles. Se puede detallar en la Fig.…”

Section: Ssa Aplicado Al Problema Jssopunclassified

Un Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificados

2019

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Introducción: El problema Job Shop Con Operadores Calificados o Job Shop With Skilled Operators (JSSO) es una extensión del problema clásico Job Shop en el cual, una operación debe ser ejecutada por un conjunto limitados de trabajadores, con el objetivo de minimizar el tiempo de terminación total de los trabajos o Makespan, situación que puede representar distintas aplicaciones en la vida cotidiana. Es un problema complejo y es catalogado como NP-HARD. Objetivo: En este artículo, se aborda el problema JSSO desde la adaptación de un algoritmo conocido como Squirrel Search Algorithm (SSA). Metodología: Se propone un esquema de codificación discreto para el algoritmo SSA utilizando el método Smallest Position Value (SPV). Además, para evitar soluciones que violen las relaciones de precedencia; se corrigen con el método Valid Particle Generator (VPG), el cual garantiza soluciones factibles. Dos versiones del algoritmo se colocan a prueba en 28 instancias propuesta en la literatura para validar su rendimiento. Resultados: Los experimentos computacionales realizados muestran que los dos algoritmos propuestos alcanzan soluciones óptimas en 25 de las 28 instancias analizadas. Además, para las instancias en donde no se logró soluciones óptimas, el gap promedio no supera el 2% para ambas versiones de los algoritmos propuestos. Conclusiones: El esquema de codificación propuesto garantiza la discretización del algoritmo, generando soluciones que convergen hacia el óptimo. Además, la codificación propuesta, permite utilizar de manera natural los operadores de movimiento propuestos originalmente para el algoritmo utilizado. El rendimiento obtenido por los algoritmos es adecuado y de alta calidad.

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A model for resource-constrained project scheduling using adaptive PSO

Cited by 38 publications

References 44 publications

Review of conventional metaheuristic techniques for resource-constrained project scheduling problem

Review of conventional metaheuristic techniques for resource-constrained project scheduling problem

Priority rule for resource constrained project planning problem with predetermined work package durations

Un Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificados

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