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ORCID

0009-0002-0750-9582

Abstract

A Decision Support Tool (DST) represents a transformative way of modernising maintenance practice on the permanent-way infrastructure of Meter Gauge Railway (MGR). Maintenance practice remains largely reactive, as the MGR plays a strategic role in transporting both freight and passengers, resulting in inefficient resource allocation, high operational risk, and growing Lifecycle expenses. This concept paper explores the potential and the design challenges for a DST that combines fuzzy analytic hierarchy process (Fuzzy-AHP) modelling, multi-criteria decision analysis (MCDA), Geographic Information Systems (GIS) and predictive machine learning (ML) analytics for evidence-based, future-oriented maintenance management. The paper draws on peer-reviewed studies from similar railway contexts around the world, such as the United Kingdom, India, South Africa, Sweden, Thailand and East Africa, to consolidate methodological progress in the design of DSTs, reports measurable performance results and correlates these to the gaps identified in Tanzania's railway maintenance practice. The conceptual framework introduces a five-block architecture comprising: input parameters, the Fuzzy AHP analysis, the ranking of maintenance strategies, the decision logic modelling, and implementation planning, supported by a centralized GIS asset register and by streams of information coming from IoT sensors. Expected benefits when compared to the literature are a 35-45 per cent reduction in unforeseen maintenance events, a 30 per cent increase in resource utilization, and a 22 per cent decrease in long-term lifecycle cost over five years.

Publisher Name

University of Dar es Salaam

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