A SIMULATION-OPTIMIZATION APPROACH FOR EVALUATING AND SELECTING MUNICIPAL PROJECTS IN DEVELOPING COUNTRIES

Abstract

Municipalities in developing countries often face constrained budgets, institutional limitations, infrastructure instability, and uncertainty when selecting among competing projects. Palestinian municipalities provide a relevant applied context in which these pressures are particularly visible. Conventional project-prioritisation approaches may not adequately differentiate alternatives or represent uncertainty in expert judgement. This thesis proposes and evaluates a three-stage simulation-optimization decision-support approach for evaluating and selecting municipal projects. The approach integrates the Analytic Hierarchy Process (AHP), a Monte Carlo-based Modified Analytic Hierarchy Process (MAHP), and non-parametric bootstrap inference to produce a deterministic baseline ranking, assess ranking robustness under uncertainty, and evaluate pairwise statistical differences between project scores. A panel of 20 experts performed pairwise comparisons of criteria and performance ratings for three municipal infrastructure projects against four criteria: technological feasibility (C1), economic viability (C2), social impact (C3), and resilience to external constraints (C4). AHP produced deterministic criterion weights and a baseline project ranking. MAHP used K = 10,000 Monte Carlo iterations sampled from the empirical distribution of expert judgements. Bootstrap inference used B = 5,000 replicates and 95% confidence intervals to assess pairwise score differences. The highest-ranking criterion under AHP was C1 (w = 0.514), with an acceptable consistency ratio (CR = 0.047), producing the baseline ranking P3 > P1 > P2. Under MAHP, P3 (Southern Industrial Zone Transfer Station) remained first with a mean score of 0.799, while P2 ranked above P1 under uncertainty. Bootstrap inference showed positive pairwise differences between P3 and P1 (Δ = +0.097) and between P3 and P2 (Δ = +0.079), supporting P3 as the highest-priority project within the analysed case set.