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Optimizing the scheduling of crew deployments in repetitive construction projects under uncertainty

Abbas Hassan (Military Technical College, Cairo, Egypt)
Khaled El-Rayes (University of Illinois at Urbana-Champaign, Champaign, Illinois, USA)
Mohamed Attalla (University of Illinois at Urbana-Champaign, Champaign, Illinois, USA)

Engineering, Construction and Architectural Management

ISSN: 0969-9988

Article publication date: 28 August 2020

Issue publication date: 25 June 2021

307

Abstract

Purpose

This paper presents the development of a novel model for optimizing the scheduling of crew deployments in repetitive construction projects while considering uncertainty in crew production rates.

Design/methodology/approach

The model computations are performed in two modules: (1) simulation module that integrates Monte Carlo simulation and a resource-driven scheduling technique to calculate the earliest crew deployment dates for all activities that fully comply with crew work continuity while considering uncertainty; and (2) optimization module that utilizes genetic algorithms to search for and identify optimal crew deployment plans that provide optimal trade-offs between project duration and crew deployment plan cost.

Findings

A real-life example of street renovation is analyzed to illustrate the use of the model and demonstrate its capabilities in optimizing the stochastic scheduling of crew deployments in repetitive construction projects.

Originality/value

The original contribution of this research is creating a novel multiobjective stochastic scheduling optimization model for both serial and nonserial repetitive construction projects that is capable of identifying an optimal crew deployment plan that simultaneously minimizes project duration and crew deployment cost.

Keywords

Citation

Hassan, A., El-Rayes, K. and Attalla, M. (2021), "Optimizing the scheduling of crew deployments in repetitive construction projects under uncertainty", Engineering, Construction and Architectural Management, Vol. 28 No. 6, pp. 1615-1634. https://doi.org/10.1108/ECAM-05-2020-0304

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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