An Adaptive Simulation–Optimisation Framework for Multi-Echelon Humanitarian Relief Networks

Humanitarian supply chains (HSCs) operate under extreme uncertainty, where rapid and informed decision-making is essential to ensure efficient relief delivery. This paper presents a simulation–optimisation framework that integrates a discrete-event simulation (DES) model with a multi-objective evolutionary algorithm (NSGA-II) to jointly optimise workforce allocation across distribution nodes and routing decisions within a multi-echelon relief network. The framework aims to maximise demand satisfaction while minimising total operating cost. It was validated through two experimental configurations: (i) a pre-defined routing experiment, where node-to-node routes were fixed, and (ii) an adaptive routing experiment, where routes were treated as decision variables within the optimisation process. Results indicate that allowing adaptive route selection substantially enlarged the non-dominated solution set, yielding a 132.1% increase in Pareto-optimal solutions and improving mean demand satisfaction by 57.1% relative to the fixed-route baseline, at the expense of higher but operationally justified cost. These findings highlight the framework’s efficiency and adaptability in generating diverse, high-quality non-dominated solutions that effectively balance operational costs and demand satisfaction, demonstrating its value as a robust decision-support tool for humanitarian logistics under uncertainty.