Authors : MD Monasur Rahman; Md Jahedul Islam Arif; Mohammad Mostafijur Rahman; Safaul Islam Rohan

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/3kch5m9u

DOI : https://doi.org/10.38124/ijisrt/26sep461

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Industrial and infrastructure asset portfolios require investment decisions under limited budgets, operational risks, resource constraints, and competing sustainability priorities. This study proposes a decision intelligence framework for portfolio level asset intervention prioritization. The framework integrates Fuzzy Delphi based criterion validation, Pareto screening, Group AHP weighting, Utility Value Analysis, performance-gap assessment, and binary performancegain knapsack optimization. Eight criteria are considered: asset condition, failure exposure, lifecycle cost, operational importance, resilience contribution, environmental performance, modernization potential, and implementation feasibility. The optimization model selects intervention portfolios according to expected performance gains while considering budget limits, performance requirements, resource constraints, and logical dependencies. Scenario analysis examines portfolio changes under cost, reliability, resilience, sustainability, and digital-modernization priorities. VIKOR, TOPSIS, PROMETHEE, and COPRAS provide alternative rankings, while Spearman correlation assesses ranking stability. A synthetic asset portfolio is used to demonstrate the computational workflow without relying on proprietary organizational data.

Keywords : Decision Intelligence; Asset Portfolio Prioritization; Multi-Criteria Decision Analysis; Group AHP; Goal Programming; Knapsack Optimization; Sustainable Infrastructure; Industrial Asset Management.

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