scholarly journals Project Scheduling using Event Based Scheduler with ABC

Scheduling is the first and foremost step for every project implementation. Project scheduling could be a mechanism to communicate what tasks has to be compelled to get done and which resources are going to be allotted to finish those tasks in what timeframe. Project scheduling occurs during the planning phase of the project. The problem comprises the correct assignment of employees to the various tasks that frame a software project, casing in time and cost limitations. To accomplish this objective, this paper presents and discusses the EBS with ACO, FCM clustering and EBS with ABC and the conclusions are drawn from it. First, to schedule human resources to tasks we implement Event based scheduler with the Ant colony optimization algorithm (ACO) for probabilistic optimization, second, for fast scheduling we implemented Fuzzy c means clustering to assign similar data points of employees to clusters so that the searching space will be reduced. Third, for optimum scheduling we apply Artificial Bee Colony algorithm with Event Based Scheduler. Artificial bee colony (ABC) is an optimization algorithm based on stochastic calculation which has demonstrated good search capacities on numerous advancement issues. Based on these findings we briefly describe the scheduling with FCM-EBS with ABC prompt optimum values

2012 ◽  
Vol 3 (2) ◽  
pp. 86-106 ◽  
Author(s):  
Tarun Kumar Sharma ◽  
Millie Pant

Artificial Bee Colony (ABC) is an optimization algorithm that simulates the foraging behavior of honey bees. It is a population based search technique whose performance depends largely on the distribution of initial population. Generally, uniform distributions are preferred since they best reflect the lack of knowledge about the optimum’s location. Moreover, these are easy to generate as most of the programming languages have an inbuilt function for generating uniformly distributed random numbers. However, in case of a population dependent optimization algorithm like that of ABC, random numbers having uniform probability distribution may not be a good choice as they may not be able exploit the search space fully. This paper uses quasi random numbers based on Halton sequence for the initial distribution and have compared the simulation results with initial population generated using uniform distribution. The proposed variant, termed as Halton based ABC (H-ABC), is validated on a set of 15 standard benchmark problems, 6 nontraditional shifted benchmark functions proposed at the special session of CEC2008, and has been used for solving the real life problem of estimating the cost model parameters. Numerical results indicate the competence of the proposed algorithm.


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