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Using the generalized maximum covering location model to control a project’s progress

Using the generalized maximum covering location model to control a project’s progress Project control consists of monitoring a project’s progress at so called control points, finding possible deviations from the baseline schedule and if necessary, making adjustments to the deviated schedule subject to the available control budget, the adjusting strategies and also other technical and environmental possibilities in order to bring the schedule back on the right track. In this study, we adapt for the first time the generalized maximum covering location model to determine the adjusting strategies such that the maximum control coverage is achieved, i.e. under the given constraints, a schedule that is globally as close to the baseline schedule as possible is obtained. Numerical examples are given to illustrate the intricacies of the proposed method and also to demonstrate its applicability. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computational Management Science Springer Journals

Using the generalized maximum covering location model to control a project’s progress

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References (21)

Publisher
Springer Journals
Copyright
Copyright © Springer-Verlag GmbH Germany, part of Springer Nature 2018
Subject
Business and Management; Operations Research/Decision Theory; Optimization
ISSN
1619-697X
eISSN
1619-6988
DOI
10.1007/s10287-018-0301-5
Publisher site
See Article on Publisher Site

Abstract

Project control consists of monitoring a project’s progress at so called control points, finding possible deviations from the baseline schedule and if necessary, making adjustments to the deviated schedule subject to the available control budget, the adjusting strategies and also other technical and environmental possibilities in order to bring the schedule back on the right track. In this study, we adapt for the first time the generalized maximum covering location model to determine the adjusting strategies such that the maximum control coverage is achieved, i.e. under the given constraints, a schedule that is globally as close to the baseline schedule as possible is obtained. Numerical examples are given to illustrate the intricacies of the proposed method and also to demonstrate its applicability.

Journal

Computational Management ScienceSpringer Journals

Published: Jan 22, 2020

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