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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.8 No.3 pp.171-180
DOI :

A Looping Population Learning Algorithm for the Makespan/Resource Trade-offs Project Scheduling

Chiuh-Cheng Chyu, 1Ying-Chieh Fang
Department of Industrial Engineering and Management Yuan-Ze University, 135 Yuan-Tung Road, Chung-Li, 320, Taiwan, R.O.C.
1Department of Industrial Engineering and Management Yuan-Ze University, 135 Yuan-Tung Road, Chung-Li, 320, Taiwan, R.O.C.
Received Date, March 25, 2009; Revised Date, July 31, 2009; Accepted Date, August 3, 2009 (Selected from APIEMS 2008)

Abstract

Population learning algorithm (PLA) is a population-based method that was inspired by the similarities to the phenomenon of social education process in which a diminishing number of individuals enter an increasing number of learning stages. The study aims to develop a framework that repeatedly applying the PLA to solve the discrete resource constrained project scheduling problem with two objectives: minimizing project makespan and renewable resource availability, which are two most common concerns of management when a project is being executed. The PLA looping framework will provide a number of near Pareto optimal schedules for the management to make a choice. Different improvement schemes and learning procedures are applied at different stages of the process. The process gradually becomes more and more sophisticated and time consuming as there are less and less individuals to be taught. An experiment with ProGen generated instances was conducted, and the results demonstrated that the looping framework using PLA outperforms those using genetic local search, particle swarm optimization with local search, scatter search, as well as biased sampling multi-pass algorithm, in terms of several performance measures of proximity. However, the diversity using spread metric does not reveal any significant difference between these five looping algorithms.

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