Research Projects

India's logistics sector, accounting for 13–14% of GDP, has long faced inefficiencies due to high costs, modal imbalance, and inadequate infrastructure. The National Logistics Policy (NLP) 2022 was introduced to address these challenges, with a key focus on the development of Multi-Modal Logistics Parks (MMLPs). These parks aim to integrate various modes of transport—road, rail, air, and waterways—alongside warehousing and value-added services, creating large-scale, efficient, and sustainable logistics hubs. MMLPs are expected to reduce logistics costs, encourage modal shift, enhance throughput, and lower carbon emissions. Despite growing investments from bodies like the National Highways Logistics Management Limited (NHLML), empirical data on their operational, economic, and environmental performance remains unaddressed. This research seeks to fill that gap by undertaking a comprehensive impact assessment of MMLPs. It proposes a novel methodology that combines empirical statistical techniques and multi-objective optimization models to evaluate MMLPs across different regions and operational scenarios. The study's four main objectives are: (1) to identify key success factors and implementation barriers; (2) to evaluate environmental benefits such as emission reductions and sustainability through intermodal integration; (3) to assess economic impacts including cost efficiency and modal shift; and (4) to build a multi-objective optimization framework to compare MMLP configurations with the aim of minimizing costs and emissions while maximizing throughput and modal efficiency. To achieve the first two objectives, the study adopts a quantitative approach using structured surveys, expert interviews, and statistical techniques such as Exploratory Factor Analysis (EFA), regression modeling, T-tests, and ANOVA. Objective 1 will assess implementation challenges such as infrastructure readiness, land acquisition, stakeholder engagement, and technology integration. Objective 2 will measure environmental outcomes using emission data, transport mode share, and adoption of smart logistics technologies. . For Objectives 3 and 4, the study adopts optimization-based methods to simulate various MMLP models. Using Linear Programming (LP), Mixed Integer Programming (MIP), and evolutionary algorithms like NSGA-II, it will develop a decision-support tool capable of analyzing trade-offs among cost, emissions, and efficiency. These simulations will model alternative configurations and dynamic scenarios (e.g., fuel price volatility, policy changes, demand fluctuations) to support strategic planning. The significance of this research lies in its holistic and practical approach. By integrating real-time sensor data, multi-objective optimization, and empirical analysis, it offers a robust evaluation framework tailored specifically for the Indian logistics context. Prior studies have addressed elements such as emission reduction or facility layout in isolation, but none have brought these components together within the unique MMLP framework under India’s NLP 2022. Moreover, the inclusion of Public-Private Partnership (PPP) models, digital monitoring dashboards, and stakeholder dynamics makes this research not only academically rigorous but also highly relevant to policymakers and industry practitioners. In summary, this study provides a much-needed, data-driven evaluation of MMLPs, helping to bridge the gap between policy intent and on-ground impact. It aims to enhance the design, implementation, and governance of MMLPs, ultimately contributing to India's goal of reducing logistics costs to below 10% of GDP while promoting sustainable and efficient freight movement.