An integrated framework for evaluation and selection of disaster response roads network

Purpose The confirmation and implementation of disaster response roads network (DRRN) often require input from diverse stakeholders, including municipal authorities, police and military forces. This leads to frequent modifications in disaster response routes. Furthermore, decision-makers (DMs) may face difficulties in interpreting complex model structures and identifying appropriate solutions. To address these challenges, this study aims to propose an integrated framework designed to improve the evaluation and selection processes of disaster response networks (DRNs). Design/methodology/approach The proposed integrated framework comprises three main steps: (1) identification of suggested DRNs (Pareto optimal solutions), (2) prioritization of parameters affecting the DRNs (ranking the pruning functions) and (3) evaluation and selection of DRNs (pruning the solution set). Network, route and link parameters are used as the pruning functions. In this paper, an integrated framework for evaluation and selection of the DRNs is presented. In addition, a constraint satisfaction problem (CSP)-based hierarchical Pareto-pruning mechanism is presented to evaluate and select the DRRN. So, a basic CSP is proposed that can be applied to various types of disasters. Findings Sensitivity analyses conducted on the Sioux Falls network and the Isfahan case study demonstrate the effectiveness of the proposed framework in identifying preferred DRNs under three-level road segments conflicting objectives. The CSP-based hierarchical Pareto-pruning process reduced the candidate solution set by 95.4% and decreased the number of model executions by 72.3%, resulting in a total CPU time of 242.52 s for the Isfahan network (80 origin-destination pairs and 2,679 links). Practical implications The proposed framework supports disaster management authorities by enabling efficient DRN selection without prior preference information. It improves transparency and consistency in disaster preparedness phase planning by reducing the Pareto set to a single implementable solution while preserving efficiency. Consequently, it facilitates evidence-based infrastructure prioritization and resilient transportation policy development. Originality/value The primary value of this study lies in the development of an integrated framework that combines DRN identification, pruning function prioritization, evaluation and final selection within a unified decision-support process, thereby reducing decision ambiguity and DM intervention. In addition, a CSP-based hierarchical Pareto-pruning mechanism is proposed to integrate three-level road segment performance indicators for DRN selection.