The development of a sustainable road network in Northeast India is challenging due to the rough terrain, inclement weather, budgetary constraints, and lack of skilled workforce, among other factors. Since the sustainable development of roads involves balancing multiple goals and limitations, in this work, a hybrid multi-objective optimization model was developed by integrating the multi-objective particle swarm optimization with the crowd distance technique and the K-means clustering strategy. The application of the proposed approach was demonstrated by designing a road suitable for Northeast India. The proposed model generated sustainable designs by optimizing the budgetary and environmental limits and workforce competence. The results indicated that multiple optimization techniques can be incorporated in the design of sustainable highways in Northeast India. The use of stabilized layers instead of granular layers helped reduce transportation costs and environmental impacts. This study can assist highway engineers in the prompt evaluation of multiple options and decision making.
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