Analytical model for large-scale design of sidewalk delivery robot systems

With the rise in demand for local deliveries and e-commerce, robotic deliveries are being considered as efficient and sustainable solutions. However, the deployment of such systems can be highly complex due to numerous factors involving stochastic demand, stochastic charging and maintenance needs, complex routing, etc. We propose a model that uses continuous approximation methods for evaluating service trade-offs that consider the unique characteristics of large-scale sidewalk delivery robot systems used to serve online food deliveries. The model captures both the initial cost and the operation cost of the delivery system and evaluates the impact of constraints and operation strategies on the deployment. By minimizing the system cost, variables related to the system design can be determined. First, the minimization problem is formulated based on a homogeneous area, and the optimal system cost can be derived as a closed-form expression. By evaluating the expression, relationships between variables and the system cost can be directly obtained. We then apply the model in neighborhoods in New York City to evaluate the cost of deploying the sidewalk delivery robot system in a real-world scenario. The results shed light on the potential of deploying such a system in the future

Operational Strategies for On-demand Personal Shopper Services

Inspired by several recent startups, we study an on-demand delivery service that lets customers shop online for products from a number of brick and mortar stores. The customer orders are fulfilled by a fleet of personal shoppers who are responsible for both the shopping of orders at the stores and the delivery of these to customer locations. The operation of such a service requires to dynamically manage new requests, coordinate a fleet of shoppers, schedule shopping operations at stores, and execute deliveries to customers on time. Our work presents three operational strategies, each requiring different levels of shopper flexibility and implementation complexity. We quantify the performance of each strategy in a vast family of computational experiments. Also, the performance of this on-demand shopping service is compared to a setting in which customers travel to stores to shop themselves. Our numerical experiments show that there are significant savings in resources spent in shopping (up to 55.2%) when this activity is outsourced

Big data applications in food supply chains

International Conference On Engineering And Computer Science (ICECS) 2022: The use of innovative technology in accelerating problems sustainable development. 13 December 2022, Bandar Lampung City, IndonesiaFood supply chains are characterized by innovation, not only in products but also in processes. This paper aims to identify big data applications in the food and drink sector and present its findings as a state-of-the-art literature review. Academic databases were searched using ‘food’ or ‘drink’ and ‘big data’ keywords. Scholarly publications from 2015 onward are identified and presented in broad categories of demand prediction and retail operations optimization. The review recognized big data applications as a great opportunity for food supply chains. The applications aimed 1) to understand the customer base and inform marketing communications strategy, 2) to predict demand and organize retail operations to meet this demand, and 3) to optimize prices, assortment, and inventories based on demand patterns. Applications in this review focused more on descriptive and predictive analytics than prescriptive analytics, possibly due to the emergent nature of these applications. Descriptive analytics applications focused on capturing data, summarizing the status quo, and developing customer segments which can then be managed using varying marketing strategies. Predictive analytics applications focused on demand prediction with novel approaches proposed by the machine learning community. Prescriptive analytics applications aimed at promotion optimization and pricing for profit maximization. Cognitive analytics applications extracted customer reviews from online stores to inform which products should be marketed in what way. The review offers managerial insights on circumstances where big data analytics could prove beneficial. Managerial implications suggest that data integrators enable big data applications by ensuring the data collected are accurate, timely, and complete to inform descriptive, predictive, and prescriptive analytical models

The Sustainability of the Gig Economy Food Delivery System (Deliveroo, UberEATS and Just-Eat):Histories and Futures of Rebound, Lock-in and Path Dependency

Online food delivery has transformed the last-mile of food and grocery delivery, with unnoticed yet often significant impacts upon the transport and logistics network. This new model of food delivery is not just increasing congestion in urban centers though, it is also changing the contours and qualities of those doing delivery – namely through gig economy work. This new system of food consumption and provision is rapidly gaining traction, but assessments around its current and future sustainability tend to hold separate the notions of social, environmental and economic sustainability – with few to date working to understand how these can interact, influence and be in conflict with one another. This paper seeks to work with this broader understanding of sustainability, whilst also foregrounding the perspectives of gig economy couriers who are often marginalized in such assessments of the online food delivery system. We make use of systems thinking and Campbell’s (1996) conflict model of sustainability to do this. In assessing the online food delivery in this way, we seek to not only provide a counternarrative to some of these previous assessments, but to also challenge those proposing the use of gig economy couriers as an environmentally sustainable logistics intervention in other areas of last-mile logistics to consider how this might impact the broader sustainability of their system, now and in the future

Developing Environmentally Friendly Solutions for On-Demand Food Delivery Service

USDOT Grant 69A3551747114Goods movement accounts for a significant and growing share of urban traffic, energy use and greenhouse gas emissions (GHGs). This project investigated the vehicle miles travelled (VMT) and emissions impact of on-demand food delivery under different COVID-19 pandemic periods and multiple operational strategies, with real-world scenarios set up in the city of Riverside, California. The evaluation results showed that during COVID-19 the total VMT and pollutant emissions (CO2, CO, HC, NOx) incurred by eat out demand all decreased by 25% compared with the before-COVID-19 period. The system can achieve substantial reductions in vehicle trips and emissions with higher penetration of on-demand delivery. From the dynamic operation perspective, the multi-restaurant strategy (allow food orders to be bundled from multiple restaurants in one driver\u2019s tour) can bring 28% of VMT and and emissions reductions while avoiding introducing additional delay compared to the one-restaurant policy (only allow food orders from the same restaurant to be bundled in one driver\u2019s tour). The research results indicate that the delivery platform should provide more reliable service with lower cost to increase the food delivery penetration level, which needs improvement in driver capacity management, eco-friendly delivery strategy, and efficient order allocation system. Meanwhile, to achieve maximum VMT and emissions reduction, the platform should encourage order bundling and employ a multi-restaurant policy to provide higher flexibility to group food orders, especially from restaurants located densely in one shopping plaza or commercial zone