Validating a Hybrid Multi-Criteria Decision-Making Model for Private Partner Selection in PPP Using Monte Carlo Simulation

Abstract

Effective private partner selection is fundamental to the success of Public-Private Partnership, particularly in environments characterised by uncertainty and complexity. This study develops and validates a Hybrid Multi-Criteria Decision-Making framework that integrates Fuzzy Analytical Hierarchy Process, Best-Worst Method, Analytic Network Process and the Technique for Order of Preference by Similarity to Ideal Solution. The integrated approach accommodates judgmental vagueness, ensures internal consistency of criteria weights, addresses interdependencies and facilitates comprehensive prioritisation of private partners. To test the reliability and sensitivity of the proposed model under variable conditions, Monte Carlo simulation was employed. This simulation introduced stochastic variation to the input parameters. Java was adopted to implement the simulation process due to its computational efficiency and object-oriented modularity. A total of 10,000 iterations were run, with each criterion weight treated as a normally distributed variable, assuming a standard deviation of 5% of its mean. The stability of rankings observed across all simulation iterations affirms the model’s structural predictive consistency. By quantifying the impact of input uncertainty, the model offers decision-makers enhanced confidence in the selection process. The findings advocate for the adoption of the framework, validated through simulation, as a critical tool for advancing transparency, defensibility and resilience in PPP procurement.