Title: A new optimisation method for scheduling with ACO and GA

Authors: Seyed Nima Mirabedini; Hassan Mina; Seyed Hossein Iranmanesh

Addresses: Department of Industrial and Systems Engineering, University of Tehran, P.O. Box: 11155-4563, Kargar Shomali street, Tehran, Iran ' Department of Industrial and Systems Engineering, University of Tehran, P.O. Box: 11155-4563, Kargar Shomali street, Tehran, Iran ' Department of Industrial and Systems Engineering, University of Tehran, P.O. Box: 11155-4563, Kargar Shomali street, Tehran, Iran

Abstract: In this paper we consider project scheduling in critical condition and encountered project delays and defeat. Under such circumstances the project manager should make the best decision for the project to decrease incoming risks, costs, time and maintain the organisation reputation. So in order to deliver the project on time, the manager has to find the critical jobs among the tasks which are not scheduled yet. We have presented two new mathematical models for achieving minimum time and cost of the project and is implemented by ant colony optimisation (ACO) and genetic algorithm (GA). Project task duration is considered as fuzzy-stochastic variable under uncertainty model. We test these models with an information technology (IT) project in real world and illustrate our how model reduce completion time and cost of the project and they can be implemented as a good trade-off for the main goal of the project.

Keywords: project scheduling; fuzzy theory; elected tasks; time windows; ant colony optimisation; ACO; genetic algorithms; GAs; project time; project costs; mathematical modelling; uncertainty modelling; project completion time.

DOI: 10.1504/IJFCM.2014.067125

International Journal of Fuzzy Computation and Modelling, 2014 Vol.1 No.2, pp.169 - 193

Received: 13 Feb 2014
Accepted: 02 Jun 2014
Published online: 07 Feb 2015 *

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