Ant Colony Optimization for Software Project Scheduling and Staffing with an Event-Based Scheduler

Chen, Wei-Neng; Zhang, Mengjie

doi:10.1109/tse.2012.17

Cited by 149 publications

(79 citation statements)

References 41 publications

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“…Moreover, by building the solutions step-by-step within ACO algorithms, it is possible to design useful heuristics to direct the ants to avoid critical tasks as early as possible (Chen and Zhang, 2013). This characteristic is a key concept in our work.…”

Section: Related Workmentioning

confidence: 99%

A risk-based approach applied to system engineering projects: A new learning based multi-criteria decision support tool based on an Ant Colony Algorithm

Lachhab

Béler

Coudert

2018

Engineering Applications of Artificial Intelligence

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This is an author-deposited version published in: http://oatao.univ-toulouse.fr/ Eprints ID: 19945 A B S T R A C TThis article proposes a multi-criteria decision support tool fully integrated within system engineering and project management processes that allows decision makers to select an optimal scenario of a project. A model based on an oriented graph includes all the alternative choices of a new system's conception and realization. These choices take into account the risks inherent to perform project tasks in terms of cost and duration. The model of the graph is constructed by considering all the collaborative decisions of the different actors involved in the project. This decision support tool is based on an Ant Colony Algorithm (ACO) for its ability to provide optimal solutions in a reasonable amount of time. The model developed is a multi-objective new ant colony algorithm based on an innovative learning mechanism (named MONACO) that allows ants to learn from their previous choices in order to influence the future ones. The objectives to be minimized are the total cost of the project, its global duration and the risk associated with these criteria. The risk is modeled as an uncertainty related to the increase of the nominal values of cost and duration. The optimization tool is a part of an integrated and more global process, based on industrial standards (the System Engineering process and the Project Management one) that are widely known and used in companies.

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Section: Related Workmentioning

confidence: 99%

A risk-based approach applied to system engineering projects: A new learning based multi-criteria decision support tool based on an Ant Colony Algorithm

Lachhab

Béler

Coudert

2018

Engineering Applications of Artificial Intelligence

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show abstract

“…In any case, as both of these two issues are troublesome, existing models either experience from the effects of an extensive inquiry space or need to confine the adaptability of human asset assignment to improve the model. To build up an adaptable and powerful model for software project planning [16].…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Survey Paper for Software Project Team, Staffing, Scheduling and Budgeting Problem

Akram¹,

Ihsan²,

Zafar³

et al. 2018

ijacsa

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“…We must create the pheromone update rule suitable for HC and establish the heuristic information that will be used to guide the construction of the solutions [22,23].…”

Section: Mmas-hc For Spspmentioning

confidence: 99%

Software Project Scheduling using the Hyper-Cube Ant Colony Optimization algorithm

et al. 2015

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Original scientific paper This paper introduces a proposal of design of Ant Colony Optimization algorithm paradigm using Hyper-Cube framework to solve the Software Project Scheduling Problem. This NP-hard problem consists in assigning tasks to employees in order to minimize the project duration and its overall cost. This assignment must satisfy the problem constraints and precedence between tasks. The approach presented here employs the Hyper-Cube framework in order to establish an explicitly multidimensional space to control the ant behaviour. This allows us to autonomously handle the exploration of the search space with the aim of reaching encouraging solutions. Keywords: Ant Colony Optimization; Hyper-Cube; scheduling; Software Project Management Programiranje računarskog projekta primjenom Hyper-Cube algoritma za optimizaciju kolonije mravaIzvorni znanstveni članak U radu se daje prijedlog dizajna paradigme algoritma za optimizaciju kolonije mrava primjenom Hyper-Cube sustava za rješenje problema programiranja računarskog projekta (Software Project Scheduling Problem). Taj se NP-hard problem sastoji od davanja zaduženja zaposlenicima u svrhu smanjenja trajanja projekta i njegovih ukupnih troškova. To zaduženje mora zadovoljiti ograničenja problema i pitanje prvenstva među zadacima. Pristup prikazan ovdje koristi Hyper-Cube sustav za uspostavljanje eksplicitno multidimenzionalnog prostora za kontrolu ponašanja mravi. Time nam se omogućava autonomno vođenje istraživanja u cilju pronalaženja ohrabrujućih rješenja.

show abstract

Ant Colony Optimization for Software Project Scheduling and Staffing with an Event-Based Scheduler

Cited by 149 publications

References 41 publications

A risk-based approach applied to system engineering projects: A new learning based multi-criteria decision support tool based on an Ant Colony Algorithm

A risk-based approach applied to system engineering projects: A new learning based multi-criteria decision support tool based on an Ant Colony Algorithm

Survey Paper for Software Project Team, Staffing, Scheduling and Budgeting Problem

Software Project Scheduling using the Hyper-Cube Ant Colony Optimization algorithm

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