Projects
List of ongoing and future projects.
2027
- PostDocOptimizing Crowdsourced Delivery Systems for Perishable GoodsAnmol PahwaJul 2027
Perishable goods delivery presents unique logistical challenges where product quality deteriorates rapidly during transport, requiring precise timing and temperature control to maintain consumer satisfaction. These complexities have been amplified by quick-commerce platforms delivering groceries and meals within 15–30 minute windows, creating demands for algorithmic solutions that handle workforce volatility and product degradation constraints. Platforms face inefficiencies due to unpredictable agent availability and inadequate routing optimization that fails to account for time-sensitive quality requirements. This research develops an integrated framework combining machine learning (ML) and microeconomic modeling to optimize crowdsourced delivery systems for perishable goods, addressing gaps where traditional logistics models prove inadequate in dynamic marketplace environments. The approach represents a shift from isolated optimization domains toward comprehensive algorithmic integration that addresses supply uncertainty and demand variability in crowdsourced networks. The supply prediction module employs ensemble ML algorithms to forecast delivery agent participation patterns by analyzing behavioral economics factors, including incentive structures, opportunity costs, and external market conditions. The demand forecasting component utilizes recurrent neural networks with attention mechanisms to capture temporal dependencies in consumer purchasing patterns, incorporating lead time preferences, price sensitivity, and quality degradation expectations for perishable goods categories. The framework operates through integrated data pipelines where real-time demand forecasting triggers supply prediction algorithms, which feed into the routing optimization engine. When orders are placed, demand updates inform supply availability assessments within delivery time windows, converging into route assignments that minimize spoilage while maximizing stakeholder satisfaction. The core innovation integrates economic constraints into vehicle routing optimization algorithms, ensuring route planning optimizes both operational efficiency and market-driven factors, including dynamic pricing and agent compensation. The routing engine considers product shelf life, temperature requirements, and delivery urgency to minimize spoilage while maximizing satisfaction across all stakeholders. This interdisciplinary approach bridges economic theory, machine learning, and operations research to advance knowledge in crowdsourced delivery optimization while demonstrating measurable improvements in delivery efficiency, cost effectiveness, and stakeholder satisfaction for perishable goods logistics.
- PostDocModelling the Spatial Structure of Service Catchment Areas in Q-Commerce Last-Mile Delivery SystemsAnmol PahwaJul 2027
Rapid growth of Q-commerce has produced a dense network of small last-mile delivery facilities (“dark stores”) operating under strict time and distance constraints. Unlike conventional logistics systems, Q-commerce facilities serve compact, highly time-sensitive urban catchments, where even small changes in facility location can significantly alter service coverage, overlap, and operational efficiency. However, existing location-planning approaches largely assume highly regular service regions, typically circular catchments, which do not adequately capture how the shape, structure, and fragmentation of service catchments emerge in dense urban environments. This study aims to develop a spatial modelling framework to analyse how service catchment geometry impacts the configuration (number and location) of Q-commerce dark stores. Instead of committing to one idealised geometry, the framework will test a small set of shape families that span the main mechanisms by which real catchments emerge: (i) distance-limited regions (as a baseline), (ii) tessellating, repeatable partitions that approximate dense facility layouts (grid- or honeycomb-like), (iii) proximity-based partitions (Voronoi-type) that arise naturally when customers choose the nearest facility and when facilities are repositioned toward the centre of their served region, and (iv) non-periodic partitions (aperiodic tiling) with “irregular but structured” layouts. These families cover both Euclidean and Rectilinear travel environments and allow the model to represent elongation, fragmentation, and boundary effects without over-specifying a single polygon template. The framework will incorporate constrained or truncated regions with administrative boundaries, barriers, or corridor-like service structures, while remaining consistent with key urban planning constraints. Building on these ideas, the study will quantify how catchment geometry influences overlap, coverage gaps, effective travel distances, and the robustness of promised delivery times. An optimisation layer will generate candidate facility configurations, while a multi-criteria decision module will prioritise configurations that balance operational efficiency and service reliability against city-planning constraints (land-use compatibility, residential exposure, and neighbourhood accessibility). The expected outcome is a transferable decision-support framework linking catchment geometry, location optimisation, and urban planning considerations for regulating Q-commerce last-mile systems in dense cities.
2026
- Graph Neural Network-Based Tour Length Estimation for the Travelling Salesman ProblemAbshishek Rai , and Anmol PahwaJun 2026
Tour length estimation plays an important role in logistics planning, where fast approximations are needed across large numbers of scenarios without running a full optimization each time. Classical Continuous Approximation (CA) methods, such as the Beardwood-Halton-Hammersley (BHH) formula, provide analytical estimates but assume customers are uniformly distributed, an assumption that often breaks down in real delivery networks where demand clusters spatially around dense areas. This project explores whether Graph Neural Networks can learn to predict TSP and VRP tour lengths more accurately than classical formulas, particularly on non-uniform and clustered customer distributions. By representing each problem instance as a graph, where nodes are customer locations and edges capture spatial relationships, a GNN can potentially learn structural patterns that fixed analytical formulas cannot express. The model will be trained on synthetically generated instances using optimal tour lengths from an exact solver as ground truth. Prediction accuracy will be evaluated against the BHH formula and a standard feedforward neural network baseline, across both uniform and clustered distributions, to assess where and by how much the graph-based approach improves on existing methods.
- An Economic Analysis of Wage Elasticity and Labor Mobility among Gig WorkersAkshith Nemalipuri , and Anmol PahwaJun 2026
This study explores the impact of platform remuneration on the labor supply of Gig Workers in the Quick Commerce sector of Chennai. The analysis shifts its focus from traditional qualitative evaluations to include an economic perspective, where changes in dynamic compensation, which includes base salary, premium rates during peak hours, and milestone rewards, either directly or indirectly determine the level of driver loyalty and working hours on the platform and inter-industry labor mobility, both internal and external. Through a detailed survey conducted in important commercial districts of Chennai, the project will gather gross incentives provided by the platforms along with the operating costs to derive net salaries. The collected information will help in estimating econometric models for labor supply, allowing us to determine the precise wage elasticity of the crowdsourced delivery workers. This project is expected to reveal the economic boundaries of gig workers’ behavior.
- Developing battery-aware operations for autonomous and unmanned last-mile delivery systemsDhruv Goel , and Anmol PahwaJun 2026
The economically viable, environmentally efficient, and socially equitable growth potential of e-commerce has drawn a growing number of retailers that compete for the market through increasingly consumer-focused services, particularly just-in-time delivery. However, such consumer-focused services necessitate frequent less-than-truckload last-mile deliveries that endanger sustainability of urban freight. To this end, emerging technologies such as autonomous delivery robots (robots) and unmanned aerial vehicles (drones) can help leverage the opportunities and cope with the challenges associated with last-mile distribution. However, unexpected battery drainage leading to failed delivery, loss of control, drone/robot damage, etc., is one of the significant barriers to adoption. Yet, there is a severe dearth of knowledge on real-world implementation of autonomous and unmanned systems for last-mile distribution. Therefore, the objective of this work is to 1) develop empirically validated microscopic energy models for drones and robots to consequently 2) formulate battery-aware drone and robot strategies, and thereby 3) assess sustainability-related opportunities and challenges associated with deploying such battery-aware drones and robots in last-mile distribution. To do so, this work will 1) conduct empirical tests collecting real-time data on drone/robot telemetry, battery state-of-charge, and energy-use, 2) employ analytical procedures modelling efficient payload management plans, speed selection policies, route choice logic, etc. for drones/robots, and 3) assess case studies comparing economic, environmental, and equity-related implications of battery-aware drone/robot-based distribution strategies with conventional delivery methods. In doing so, this work supports large-scale adoption of emerging technologies in urban freight. Specifically, by developing empirically validated microscopic energy models, this work will facilitate energy demand prediction in autonomous and unmanned delivery systems. Further, through battery-aware operational strategies, this work will enable optimal deployment of drones and robots in last-mile distribution with extended autonomy and improved operational reliability. And finally, by comprehensively assessing sustainability of autonomous and unmanned last-mile distribution strategies, this work will equip stakeholders to make informed policies towards sustainable urban freight management. Collectively, these contributions advance Government of India’s commitment towards improvement in logistic performance as outlined in the National Logistics Policy. Nonetheless, beyond logistics, this work also supports use of autonomous and unmanned systems for humanitarian logistics such as in disaster relief, healthcare, and emergency response. These contributions, therefore, underscore the broader societal impact of this work towards reinforcing sustainability and resilience through autonomous and unmanned systems.
- Assessing Wider Economic Benefits of Transportation Infrastructure Investments (WEB of TransIT)Anmol PahwaJul 2026
India’s growing economy and consumption demand are placing increasing pressure on its freight system, which remains heavily dependent on long-haul trucking. While road freight is central to economic growth, it also increases crude oil dependence, greenhouse gas emissions, and local air pollution. Although zero-emission freight technologies offer long-term mitigation potential, their adoption is constrained by technological, infrastructural, and economic barriers. In this context, India’s emerging Dedicated Freight Corridor (DFC) network offers a strategic opportunity to shift freight from road to rail, reduce logistics costs, improve supply-chain reliability, and support sustainable growth. Beyond these direct benefits, freight rail infrastructure can generate wider economic benefits through productivity gains, improved interregional trade linkages, changes in commodity prices, factor returns, household income, tax revenues, public expenditure, and investment decisions. This study aims to estimate the Wider Economic Benefits (WEB) of Transportation Infrastructure (TRANSIT) in India by assessing how the proposed DFC network affects regional household income, factor returns, and fiscal outcomes through changes in logistics costs. It will further develop an optimal DFC network design that maximizes national welfare under infrastructure and fiscal constraints, and compare its aggregate and distributional impacts with the proposed network. Methodologically, the study will develop a nationwide Computable General Equilibrium model incorporating sectoral input-output relationships, commodity consumption behavior, and domestic trade margins to inform strategic freight infrastructure planning in India.
- Arriving within Time and Energy Budget: A Dynamic Routing Framework for Electric Vehicles in Stochastic NetworksAnmol PahwaJul 2026
The transportation sector is one of the most resource intensive industry, contributing 30% to global energy consumption (REN Secretariat, 2024) thereby reinforcing continued fossil fuel dependence, and 23% to global greenhouse gas emissions (Shukla et al., 2022) thus accelerating climate change. Further, it accounts for a significant share of criteria pollutant emissions – worsening local community health (World Health Organization, 2021). To this end, electric vehicles present a sustainable pathway towards reducing these negative externalities, however, their wide scale adoption is hindered by several practical constraints, with range anxiety posing one of the most significant barriers (Pevec et al., 2019). These challenges are exacerbated by prevailing navigation (route planning) systems. Designed predominantly for internal combustion engine vehicles, conventional routing frameworks optimize for travel time under deterministic assumptions. Consequently, they may propose routes that are energy-infeasible, leading to an electric vehicle getting stranded (Artmeier et al., 2010) – a risk further amplified in stochastic environments where fluctuations in network conditions can significantly undermine route feasibility (Bruni et al., 2020). Therefore, to ensure reliable mobility, this study aims to develop a dynamic (adaptive) routing framework for electric vehicles within stochastic (uncertain) network conditions, incorporating characteristics of the electric powertrain and associated charging infrastructure. To do so, this work proposes Time-Energy Stochastic in-Budget Arrival (TE-SIBA) problem – a finite-horizon Markov Decision Process grounded in the Bellman principle of optimality. To implement this routing framework, the study proposes recursive dynamic programming solution, rendering state-dependent routing policies that maximize the probability of arrival at the destination within prescribed time-energy budgets. Finally, this work will prototype an intelligent navigation system, embedding the TE-SIBA routing framework. With these contributions, this work aims to advance route planning of electric vehicles to help alleviate range anxiety and improve user confidence, thereby supporting broader adoption of electric mobility. Further, this work will form a deployable foundation for next generation of electric vehicle navigation platforms, supporting broader adoption of an economically viable, environmentally efficient, and socially equitable electric mobility. In doing so, the proposed work supports the long-term transition towards sustainable transportation, thus addressing fossil fuel dependence, global climate change, and air-quality impacts of the transportation sector.
2025
- Modelling energy consumption of Unmanned Aerival Vehicles (UAVs) for last-mile deliveryPreetham Palle , and Anmol PahwaOct 2025
Unmanned Aerial Vehicles (UAVs), commonly known as drones, are emerging as a transformative technology in transportation and logistics. In India, the growth of Drone-as-a-Service (DaaS) platforms across sectors such as logistics, healthcare, and agriculture highlights the increasing operational maturity of UAV systems. Drones offer significant advantages for delivery applications due to their high speeds and ability to bypass ground congestion, thereby reducing delivery times. However, their deployment is constrained by limited payload capacity, high energy requirements at elevated speeds, and strong sensitivity to environmental conditions such as wind and precipitation. This study focuses on the application of UAVs in last-mile delivery, the final stage of the supply chain connecting distribution hubs to end customers. Drone-based delivery systems can operate through multiple configurations, including direct warehouse-to-customer delivery, hybrid truck-drone operations, and larger multi-parcel UAV systems. Existing literature presents mixed conclusions regarding the energy efficiency of drones relative to electric and conventional ground vehicles, largely due to limitations in current modelling approaches. Studies employing detailed UAV energy models often oversimplify logistics operations, while those incorporating realistic delivery systems frequently rely on simplified energy estimations based primarily on travel distance. To address this gap, this study proposes a mesoscopic modelling framework that integrates detailed UAV energy consumption models with realistic representations of last-mile delivery operations. The framework enables a more comprehensive evaluation of drone efficiency, operational feasibility, and sustainability within urban logistics systems.
- Exploring Anthropological, Meteorological, and Temporal Determinants of Air QualityOm Ramawtar Yadav , and Anmol PahwaJun 2025
Air pollution has emerged as one of the most severe environmental and public health crises facing the world. India, bears an extraordinary burden with the entire population of 1.4 billion people living in areas with PM2.5 (Particulate Matter with a diameter of 2.5 micrometers or less) concentrations above that recommended by the WHO guidelines (exceeding the 5 μg/m3 annual mean) and 81.9% of the people lived in areas above the Indian National Ambient Air Quality Standards (Annual mean PM2·5 not exceeding 40 μg/m3). Air pollution increases the chances of heart issues, skin infections, eye diseases, throat infections, lung cancer, asthma, bronchitis diseases, etc., Long-term exposure of air pollutions may increase the chances of premature mortalities. Children might face development issues which includes impaired lung function and cognitive developments. Pregnant women might face issues in their pregnancy journey which includes low birth weight, premature births, etc. Air pollution also introduces serious impact on the economy, a report from Dalberg Advisors (Berg, n.d.) calculated the total cost of weak environmental regulations and poor emissions practices: USD $95 billion every year, or 40% of the cost of tackling the Covid-19 pandemic in the country - year after year. Per their research, the costs of poor air quality manifest in six ways: lower labor productivity, lower consumer footfall, premature mortality, lower asset productivity, increased health expenses and welfare losses. At the onset of the industrial revolution fossil fuels like coal and petroleum were deemed as the major sources of energy. Humans widely used them to meet the needs of the modern life and developed increasingly robust ways to drill more fossil fuels from the earth’s crust. The combustion of said fuels numerous gases like Carbon Dioxide (CO2), Sulfur Dioxide (SO2), etc., emerge. These emissions contribute to a host of other environmental problems like acid rain, act as greenhouse gases. Thus, the need to measure the quality of air emerged; to communicate the level of pollution in a given area and assess the impact it could have on public health, national economy and global sustenance. Given the rising levels of air pollution levels and policy directive implications, AQI prediction has been an ongoing research topic with several papers authored that utilise statistical and machine learning models to forecast future AQI values for a variety of geographies. This work attempts to use various machine learning models to predict AQI for Indian cities, being trained on various parameters for each city of selected cities i.e., Delhi, Mumbai, Chennai, Kolkata, Bhopal, and Guwahati were chosen to represent all major geographic and climatic zones of India.
- Simulating Daily Urban Mobility in India: Unravelling the travel patternsArvind Vidhyashankar , and Anmol PahwaOct 2025
Travel is an induced demand, characterised by individual’s economic and lifestyle choices. As these travel patterns evolve, urban and transportation planning must adapt to meet these changes. Understanding and forecasting travel demand is crucial for effective infrastructure development and policy planning, ensuring that transportation systems align with the needs of the population. Thus, to identify typical travel demand patterns, this study will develop an activity-based model and simulate a day in the life of Indians. In particular, the authors will utilise the 2019 Indian Time Use Survey to model an individual’s daily activity plan as a Markov Process and consequently simulate transitions between daily activities such as work, education, leisure, domestic chores, etc., across different times of the day, while also accounting for individual, communal, and regional factors. In doing so, this study will inform stakeholders for better urban and transportation planning tailored to unique demographic and economic landscapes of different Indian cities.
- Simulating a day in the life of Indians: A Markov Chain Approach using Time Use Survey DataAditya Aggarwal , and Anmol PahwaJun 2025
Travel is an induced demand, characterised by individual’s economic and lifestyle choices. As these travel patterns evolve, urban and transportation planning must adapt to meet these changes. Understanding and forecasting travel demand is crucial for effective infrastructure development and policy planning, ensuring that transportation systems align with the needs of the population. Thus, to identify typical travel demand patterns, this study will develop an activity-based model and simulate a day in the life of Indians. In particular, the authors will utilise the 2019 Indian Time Use Survey to model an individual’s daily activity plan as a Markov Process and consequently simulate transitions between daily activities such as work, education, leisure, domestic chores, etc., across different times of the day, while also accounting for individual, communal, and regional factors. In doing so, this study will inform stakeholders for better urban and transportation planning tailored to unique demographic and economic landscapes of different Indian cities.
- modular Adaptive Large Neighborhood Search (mALNS) for Capacitated Vehicle Routing Problem (CVRP)Harshvardhan Ket , and Anmol PahwaJun 2025
As urban environments grow increasingly complex, the Vehicle Routing Problem (VRP) continues to challenge researchers and practitioners. A typical VRP aims to configure routes for a fleet of vehicles serving a set of customers, rendering minimum operational costs. To address this computational challenge, Adaptive Large Neighborhood Search (ALNS) has emerged as an effective tool, leveraging its flexible paradigm of iterative destroy and rebuild. Specifically, the ALNS metaheuristic searches through the neighborhood by iteratively destroying and consequently rebuilding the solution thereby reconfiguring large portions of the solution using specific operators that are chosen adaptively in each iteration of the algorithm based on the performance of operators in the previous iterations, hence the name adaptive large neighborhood search. However, this flexibility introduces complexities in ALNS development and deployment stemming from operator selection and heuristic organization. To this end, this work introduces a modular ALNS (mALNS) framework to efficiently solve the different variants of the VRP. Unlike traditional ALNS implementations, mALNS emphasizes modular architecture, allowing seamless integration of destroy and repair operators, acceptance criteria, and adaptive mechanisms. Specifically, the ALNS implementation in this work breaks down operator and heuristic mechanisms into individual modular pieces, that are combined adaptively in each iteration of the algorithm based on the performance of the modules in the previous iterations. This modularity enhances scalability and adaptability, enabling tailored solutions for complex real-world routing problems that include resource-related constraints, customer-specific time-windows, multi-echelon distribution structures, as well as dynamic and stochastic elements from the delivery environment.
- Construction Consolidation Centres for Urban Construction LogisticsKrishnapriya Mohanan , and Anmol PahwaJan 2025
The Indian construction industry faces significant material supply chain challenges due to its highly fragmented and decentralized delivery model, resulting in a high number of daily deliveries, coordination inefficiencies, traffic congestion, limited onsite storage, etc., while construction consolidation centres have demonstrated remarkable success in streamlining logistics and improving supply chain performances in developed countries, their applicability and potential adoption remain underexplored within India’s rapidly growing construction industry. This study investigates the fundamental research question of exploring the applicability of the “Construction Consolidation Centres (CCC)” in India, by assessing its alignment with Indian Construction supply chain challenges, industry support and operational feasibility. It includes identifying material categories and regions, along with a case study application that compares the logistic costs of the existing direct delivery model to the CCC model to provide strategic recommendations for its implementation. The study follows a two-phase mixed-methods approach, where Phase 1 includes a questionnaire survey analysis using descriptive and inferential statistics to understand the industry alignment and perspective, Phase 2 explores CCC implementation through a case study application and uses vehicle routing optimization to do a comparative cost analysis between the traditional direct delivery model and the CCC model, using purchase order data. The findings confirm the potential applicability of CCC in India and recommend its role as a control tower and a consolidation hub. As a first step, this study lays the foundation for future research in implementing CCC in India, and offers a potential solution to construction firms, policy makers and logistic providers seeking innovative solutions to achieve a more resilient construction supply chain.
- Eco-driving freight on Indian highways: Assessing opportunities and challenges towards reducing transportation-related externalitiesStephen Babu , Anmol Pahwa, and Atriya BiswasJul 2025
While freight is crucial for continued economic development, the trucks that haul this cargo consume substantial amounts of fuel – increasing India’s dependence on oil imports, as well as significantly contribute towards greenhouse gas emissions – advancing global climate change, and criteria pollutant emissions – affecting local community health. To this end, alternate fuel technologies – such as battery electric, plug-in hybrid, hydrogen fuel cell, solar-power, etc. – can help mitigate these transportation-related externalities. However, the adoption of zero-emission vehicles (ZEVs) in the long-haul freight sector has been sluggish, primarily due to the technological nascency, insufficient infrastructure, and lengthy breakeven periods associated with these technologies. Thus, a significant change in the operator’s fleet can take a considerable amount of time, while there is an urgent need for operational improvement initiatives that could provide efficient alternatives to address crude oil dependence, global climate change, and local pollution impacts of freight. Hence, the objective of this work is to explore opportunities and challenges associated with eco-driving freight on Indian highways for reducing transportation-related externalities. To comprehensively explore the potential for reduction in transportation-related externalities from eco-driving freight on Indian highways, this study will develop microscopic vehicle fuel consumption and tailpipe emissions in the first phase of this work, and then study long-term sustainability of truck eco-driving in the second phase. In doing so, this research aims to equip logistics operators with the tools to effectively manage long-haul freight and provide government agencies with insights for informed policy and decision-making. In addition, this study aims to support strategic planning of the transportation sector consistent with Government of India’s a) ambition to improve the ease of doing business by reducing logistics costs to 10% of the GDP, as targeted by the National Logistics Policy, b) commitment at The Paris Agreement towards reducing emission intensity of its GDP by 34% by 2030 relative to 2005, and c) objective to reduce traffic accident-related fatalities with engineering solutions particularly focusing on heavy commercial vehicles consistent with Vision Zero target for 2030 and beyond.
- Assessing sustainability of last-mile distribution strategies for e-commerce deliveries across different Indian citiesBlessy K , and Anmol PahwaJul 2025
The surge in internet access in the past decade has paved the way for retailers to expand their market horizon with e-commerce platforms. This rise of e-commerce has bridged the gap between the consumer and the retailer, thus improving economic viability in urban freight – first pillar of sustainability. Further, e-commerce has enabled demand consolidation and delivery route optimization, thereby enhancing environmental efficiency in urban freight – second pillar of sustainability. Moreover, e-commerce has facilitated access to vital products for otherwise disadvantaged communities, thus advancing social equity in urban freight – third pillar of sustainability. This sustainable growth potential of e-commerce has drawn a growing number of retailers to compete for market through increasingly consumer-focused services. Often such consumer-focused services necessitate frequent less-than-truckload last-mile deliveries. As a consequence, urban freight witnesses a substantial increase in distribution cost as well as transportation-related negative externalities including greenhouse gas emissions advancing climate change, and criteria pollutant emissions affecting health of communities located close to logistics clusters. Hence, consumer-focused services significantly worsen the economic viability, environmental efficiency, and social equity in urban freight. Thus, to leverage the opportunities and cope with the challenges associated with last-mile deliveries, e-retailers establish alternate last-mile distribution strategies. Unlike the conventional strategy that includes use of diesel vehicles for home deliveries, these alternate strategies could include use of electric delivery vehicles for last-mile operations, a fleet of crowdsourced delivery vehicles for last-mile delivery, consolidation facilities coupled with light-duty delivery vehicles for a multi-echelon distribution, collection-points for customer pickup, or even drones and robots for autonomous operations. Thus, the objective of this study is to assess sustainability of different last-mile distribution strategies across different Indian cities. To model the diverse distribution environments, this work will employ a machine learning-enhanced continuous approximation-based framework (to be developed by the team). With this framework, the authors will assess a) economic viability by evaluating the total cost of distribution stemming from fixed assets and associated operations; b) environmental efficiency by estimating the distribution-related emissions; and c) social equity by establishing the social cost of these emissions, for each of the different distribution strategies.
2024
- Assessing sustainability of last-mile distribution strategies for e-commerce deliveries in ChennaiVarun A , and Anmol PahwaJun 2024
The surge in internet access in the past decade has paved the way for retailers to expand their market horizon with e-commerce platforms. This rise of e-commerce has bridged the gap between the consumer and the retailer, thus improving economic viability in urban freight – first pillar of sustainability. Further, e-commerce has enabled demand consolidation and delivery route optimization, thereby enhancing environmental efficiency in urban freight – second pillar of sustainability. Moreover, e-commerce has facilitated access to vital products for otherwise disadvantaged communities, thus advancing social equity in urban freight – third pillar of sustainability. This sustainable growth potential of e-commerce has drawn a growing number of retailers to compete for market through increasingly consumer-focused services. Often such consumer-focused services necessitate frequent less-than-truckload last-mile deliveries. As a consequence, urban freight witnesses a substantial increase in distribution cost as well as transportation-related negative externalities including greenhouse gas emissions advancing climate change, and criteria pollutant emissions affecting health of communities located close to logistics clusters. Hence, consumer-focused services significantly worsen the economic viability, environmental efficiency, and social equity in urban freight. Thus, to leverage the opportunities and cope with the challenges associated with last-mile deliveries, e-retailers establish alternate last-mile distribution strategies. Unlike the conventional strategy that includes use of diesel vehicles for home deliveries, these alternate strategies could include use of electric delivery vehicles for last-mile operations, a fleet of crowdsourced delivery vehicles for last-mile delivery, consolidation facilities coupled with light-duty delivery vehicles for a multi-echelon distribution, collection-points for customer pickup, or even drones and robots for autonomous operations. Thus, the objective of this study is to assess sustainability of different last-mile distribution strategies for an e-retailer offering delivery service in Chennai. With a Continuous Approximation (CA) based last-mile distribution model developed previously, this work will assess a) economic viability by evaluating the total cost of distribution stemming from fixed assets and associated operations; b) environmental efficiency by estimating the distribution-related emissions; and c) social equity by establishing the social cost of these emissions, for each of the different distribution strategies.In particular, this study will a) estimate the efficacy of conventional distribution strategy; b) confirm the competitiveness of electric delivery vehicles; c) evaluate the effectiveness of crowdsourced delivery services; d) advance the case for consolidation-based multi-echelon distribution strategies; e) establish the rationale for customer pickups; and f) develop the use case for drones and robots, thereby enabling informed decision-making for the e-retailer.
- A Holistic Continuous Approximation Framework for Strategic Last-Mile Distribution PlanningBlessy Kirubakaran , and Anmol PahwaJul 2024
The conventional approach to modelling last-mile distribution operations involves use of the discrete formulation method which renders a representative mathematical model necessitating use of sophisticated solution techniques. While such an intensive approach is justified when decision-makers require a precise plan to support operational planning, this level of precision is redundant for strategic planning, wherein available information is representative but not necessarily exact. To this end, continuous approximation (CA) method offers a practical alternative with use of continuous density functions that estimate parameters approximately, thus striking a balance between estimation accuracy and computational effort. However, typical CA-based routing frameworks assume a simplified distribution environment that significantly limits the capability of such frameworks to accurately model last-mile operations in real-world distribution environments, such as postal service, e-commerce, q-commerce, waste collection, emergency service etc. Thus, the objective of this work is to develop a holistic CA framework for the Vehicle Routing Problem (VRP) to facilitate strategic last-mile distribution planning within diverse logistic settings. Specifically, this study synthesises a range of distribution environments, each with peculiar distribution structure and distinctive customer characteristics. Subsequently, it optimizes last-mile distribution for distribution environment instance using an Adaptive Large Neighbourhood Search (ALNS) metaheuristic. Finally, this work develops a robust CA-based functional form through Symbolic Regression to precisely estimate the total distribution tour length across the various distribution environments. In doing so, this work supports strategic decision-making for a wide scope of logistic operators that may have representative but not necessarily exact information.
- Exploring determinants of consumer shopping behaviour in IndiaBlessy Kirubakaran , and Anmol PahwaJul 2024
The retail sector, conventionally dominated by physical marketplaces with brick-and-mortar stores (organised) and itinerant merchants (unorganised), has witnessed an increasing presence of digital marketplaces in the past few years. The ease of shopping afforded by this surge in e-commerce has significantly reshaped individuals’ shopping behaviours and consequently, urban goods flow. What would previously be a trip to the bazaar is now a delivery to the home. This shift in urban goods flow from aggregate commercial to disaggregate residential locations raises concerns pertaining to sustainability of freight operations. Thus, the objective of this study is to understand the changing patterns of urban goods flow in Indian cities by exploring determinants of consumer shopping behaviour. In particular, using the 2019 Indian Time Use Survey, this work will develop a multinomial logit model to identify individual, communal, and regional factors that influence consumers’ choice of shopping channel. In doing so, this study empowers stakeholders with comprehensive consumer demand characteristics to develop targeted interventions that optimize freight operations and enhance sustainability of urban goods flow.