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Comparing optimization modeling approaches for the multi-mode resource-constrained multi-project scheduling problem

Marimuthu Kannimuthu (Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India) (Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, Australia)
Benny Raphael (Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India)
Palaneeswaran Ekambaram (Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, Australia)
Ananthanarayanan Kuppuswamy (Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India)

Engineering, Construction and Architectural Management

ISSN: 0969-9988

Article publication date: 13 November 2019

Issue publication date: 21 April 2020

Abstract

Purpose

Construction firms keep minimal resources to maintain productive working capital. Hence, resources are constrained and have to be shared among multiple projects in an organization. Optimal allocation of resources is a key challenge in such situations. Several approaches and heuristics have been proposed for this task. The purpose of this paper is to compare two approaches for multi-mode resource-constrained project scheduling in a multi-project environment. These are the single-project approach (portfolio optimization) and the multi-project approach (each project is optimized individually, and then heuristic rules are used to satisfy the portfolio constraint).

Design/methodology/approach

A direct search algorithm called Probabilistic Global Search Lausanne is used for schedule optimization. Multiple solutions are generated that achieve different trade-offs among the three criteria, namely, time, cost and quality. Good compromise solutions among these are identified using a multi-criteria decision making method, Relaxed Restricted Pareto Version 4. The solutions obtained using the single-project and multi-project approaches are compared in order to evaluate their advantages and disadvantages. Data from two sources are used for the evaluation: modified multi-mode resource-constrained project scheduling problem data sets from the project scheduling problem library (PSPLIB) and three real case study projects in India.

Findings

Computational results prove the superiority of the single-project approach over heuristic priority rules (multi-project approach). The single-project approach identifies better solutions compared to the multi-project approach. However, the multi-project approach involves fewer optimization variables and is faster in execution.

Research limitations/implications

It is feasible to adopt the single-project approach in practice; realistic resource constraints can be incorporated in a multi-objective optimization formulation; and good compromise solutions that achieve acceptable trade-offs among the conflicting objectives can be identified.

Originality/value

An integer programming model was developed in this research to optimize the multiple objectives in a multi-project environment considering explicit resource constraints and maximum daily costs constraints. This model was used to compare the performance of the two multi-project environment approaches. Unlike existing work in this area, the model used to predict the quality of activity execution modes is based on data collected from real construction projects.

Keywords

Acknowledgements

The doctoral research study of the first author was supported by the Ministry of Human Resource Development (MHRD), India and scholarship from Swinburne University Postgraduate Research Award (SUPRA), Australia. The authors would like to thank reviewers for their invaluable comments.

Citation

Kannimuthu, M., Raphael, B., Ekambaram, P. and Kuppuswamy, A. (2020), "Comparing optimization modeling approaches for the multi-mode resource-constrained multi-project scheduling problem", Engineering, Construction and Architectural Management, Vol. 27 No. 4, pp. 893-916. https://doi.org/10.1108/ECAM-03-2019-0156

Publisher

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Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited