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Infrastructures
Volume 10
Issue 9
10.3390/infrastructures10090225
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Towards Smarter Infrastructure Investment: A Comprehensive Data-Driven Decision Support Model for Asset Lifecycle Optimisation Using Stochastic Dynamic Programming

by
Neda Gorjian Jolfaei
1,*,
Leon van der Linden
2,
Christopher W. K. Chow
3,
Nima Gorjian
3,
Bo Jin
1 and
Indra Gunawan
4
1
School of Chemical Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
2
South Australian Water Corporation, Adelaide, SA 5000, Australia
3
Sustainable Infrastructure and Resource Management (SIRM), UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia
4
Adelaide Business School, The University of Adelaide, Adelaide, SA 5005, Australia
*
Author to whom correspondence should be addressed.
Infrastructures 2025, 10(9), 225; https://doi.org/10.3390/infrastructures10090225 (registering DOI)
Submission received: 27 May 2025 / Revised: 27 July 2025 / Accepted: 18 August 2025 / Published: 23 August 2025
(This article belongs to the Section Smart Infrastructures)
Versions Notes

Abstract

Equipment renewal and replacement strategy as well as smart capital investment is a vital focus in engineering asset management, particularly for water utilities aiming to improve asset reliability, water quality, service continuity and affordability. This study presents a novel decision support model that integrates whole-life costing principles across all asset lifecycle phases—from capital delivery and daily operations to long-term maintenance. The proposed model uniquely combines asset degradation and failure patterns, operating and maintenance costs, and the impact of technological advancements to provide a holistic and comprehensive asset management decision-making tool. These dimensions are jointly analysed using a hybrid approach that combines optimisation with stochastic dynamic programming, allowing for the determination of optimal asset renewal and replacement timing. The model’s performance was validated using historical data from eight critical wastewater pump stations within a township’s sewerage network. This was performed by comparing the model’s cost-saving results to those achieved by the water utility’s current strategy. Results revealed that the proposed model achieved an average cost saving of 12%, demonstrating its effectiveness in supporting sustainable and cost-efficient asset renewal decisions.
Keywords: asset lifecycle; optimisation; stochastic dynamic programming; tactical asset management; smart infrastructure asset lifecycle; optimisation; stochastic dynamic programming; tactical asset management; smart infrastructure

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MDPI and ACS Style

Gorjian Jolfaei, N.; van der Linden, L.; Chow, C.W.K.; Gorjian, N.; Jin, B.; Gunawan, I. Towards Smarter Infrastructure Investment: A Comprehensive Data-Driven Decision Support Model for Asset Lifecycle Optimisation Using Stochastic Dynamic Programming. Infrastructures 2025, 10, 225. https://doi.org/10.3390/infrastructures10090225

AMA Style

Gorjian Jolfaei N, van der Linden L, Chow CWK, Gorjian N, Jin B, Gunawan I. Towards Smarter Infrastructure Investment: A Comprehensive Data-Driven Decision Support Model for Asset Lifecycle Optimisation Using Stochastic Dynamic Programming. Infrastructures. 2025; 10(9):225. https://doi.org/10.3390/infrastructures10090225

Chicago/Turabian Style

Gorjian Jolfaei, Neda, Leon van der Linden, Christopher W. K. Chow, Nima Gorjian, Bo Jin, and Indra Gunawan. 2025. "Towards Smarter Infrastructure Investment: A Comprehensive Data-Driven Decision Support Model for Asset Lifecycle Optimisation Using Stochastic Dynamic Programming" Infrastructures 10, no. 9: 225. https://doi.org/10.3390/infrastructures10090225

APA Style

Gorjian Jolfaei, N., van der Linden, L., Chow, C. W. K., Gorjian, N., Jin, B., & Gunawan, I. (2025). Towards Smarter Infrastructure Investment: A Comprehensive Data-Driven Decision Support Model for Asset Lifecycle Optimisation Using Stochastic Dynamic Programming. Infrastructures, 10(9), 225. https://doi.org/10.3390/infrastructures10090225

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