Two-stage multi-objective technology portfolio planning under resource constraints (case study: Iranian technology development fund)

Document Type : Research Article

Authors

1 Department of STI Financing and Economics, National Research Institution for Science Policy (NRISP), Tehran, Iran.

2 Mosaheb Institute of Mathematics

Abstract

This study proposes a novel two-stage multi-objective framework for optimal technology portfolio planning and resource allocation under constraints, specifically for Technology Development Funds (TDFs). The integrated methodology combines the Analytic Network Process (ANP) for prioritizing strategic technology fields with a multi-period Mixed-Integer Linear Programming (MILP) model, solved using a Revised Multi-Choice Goal Programming (RMCGP) approach. The model’s objectives are to maximize technological export potential, maximize international technological cooperation, and minimize financial risk, while incorporating critical real-world mechanisms such as staged financing contingent on Technology Readiness Level (TRL) progress, loan moratorium, and repayment periods.
The framework was validated through a real-world case study of an Iranian Technology Development Fund (ITDF), involving eight technology fields and up to 30 projects per field. Key quantitative results demonstrate the model’s efficacy: by reducing the risk objective’s weight from 0.3 to 0.1, the number of approved projects increased over fivefold (from 12 to 65), and the total allocated resources surged nearly tenfold (from $22.2$ million to $217.5$ million). Sensitivity analysis revealed that fields with high export potential and collaboration capacity (e.g., Advanced Machinery) received the highest funding, while the staged financing mechanism successfully identified and terminated 25% of projects for insufficient technical progress after the first stage. The proposed model provides a robust decision-support tool for policymakers to enhance the strategic impact and financial efficiency of national technology investments.

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References

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Journal of Mathematical Modeling
Volume 14, Issue 2
May 2026
Pages 645-671

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