A MULTI-OBJECTIVE RESOURCE-CONSTRAINED PROJECT SCHEDULING PROBLEM USING GENETIC ALGORITHM

Authors

  • LADI OGUNWOLU

    Department of Systems Engineering, Faculty of Engineering, University of Lagos, Nigeria.

  • Adeyanju Sosimi

    Department of Systems Engineering, Faculty of Engineering, University of Lagos, Nigeria

  • Toheeb Salahudeen

    Department of Systems Engineering, Faculty of Engineering, University of Lagos, Nigeria

DOI:
https://doi.org/10.51459/futajeet.2018.12.1.73
Keywords:
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Abstract
Resource-Constrained Project Scheduling Problem (RCPSP) has been modeled as a single or multi-objective, using minimization of project make-span, lateness, total weighted start time, total project cost and maximization of project net present value. In this paper, a multi-objective RCPSP incorporated resource idleness into the list of RCPSP objectives. Here, the RCPSP is modeled as a Mixed Integer Non-Linear Programme to depict the various objective factors namely cost, time and resource idleness. Genetic algorithm (GA) meta-heuristic solution technique is used to promote solution diversity and determine the Pareto optimal for the multi-objective problem. The performance of the proposed RCPSP model was evaluated using a standard test problem that consist of 5 activities, 3 reusable resource types and a network diagram; a comprehensive computational experiment was performed and the results were analyzed with precedence relations considering the objectives as single objectives, bi-objectives and in combined form as multi-objectives simultaneously. The integration of resources idleness into the multi-objective policy gives more realistic result.
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Futajeet Volume 12 Issue 1
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Published
2019-04-29
Issue
Vol. 12 No. 1 (2018): FUTA Journal of Engineering and Engineering Technology
Section
Articles
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How to Cite

A MULTI-OBJECTIVE RESOURCE-CONSTRAINED PROJECT SCHEDULING PROBLEM USING GENETIC ALGORITHM. (2019). FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY, 12(1), 90-101. https://doi.org/10.51459/futajeet.2018.12.1.73

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