Analytical model for large-scale design of sidewalk delivery robot
systems
- URL: http://arxiv.org/abs/2310.17475v1
- Date: Thu, 26 Oct 2023 15:26:12 GMT
- Title: Analytical model for large-scale design of sidewalk delivery robot
systems
- Authors: Hai Yang, Yuchen Du, Tho V. Le, Joseph Y. J. Chow
- Abstract summary: We propose a model that 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.
We then apply the model in neighborhoods in New York City to evaluate deploying the sidewalk delivery robot system in a real-world scenario.
- Score: 4.510000677649468
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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.
Related papers
- Power Hungry Processing: Watts Driving the Cost of AI Deployment? [74.19749699665216]
generative, multi-purpose AI systems promise a unified approach to building machine learning (ML) models into technology.
This ambition of generality'' comes at a steep cost to the environment, given the amount of energy these systems require and the amount of carbon that they emit.
We measure deployment cost as the amount of energy and carbon required to perform 1,000 inferences on representative benchmark dataset using these models.
We conclude with a discussion around the current trend of deploying multi-purpose generative ML systems, and caution that their utility should be more intentionally weighed against increased costs in terms of energy and emissions
arXiv Detail & Related papers (2023-11-28T15:09:36Z) - Refined Mechanism Design for Approximately Structured Priors via Active
Regression [50.71772232237571]
We consider the problem of a revenue-maximizing seller with a large number of items for sale to $n$ strategic bidders.
It is well-known that optimal and even approximately-optimal mechanisms for this setting are notoriously difficult to characterize or compute.
arXiv Detail & Related papers (2023-10-11T20:34:17Z) - Viewpoint Generation using Feature-Based Constrained Spaces for Robot
Vision Systems [63.942632088208505]
This publication outlines the generation of viewpoints as a geometrical problem and introduces a generalized theoretical framework for solving it.
A $mathcalC$-space can be understood as the topological space that a viewpoint constraint spans, where the sensor can be positioned for acquiring a feature while fulfilling the regarded constraint.
The introduced $mathcalC$-spaces are characterized based on generic domain and viewpoint constraints models to ease the transferability of the present framework to different applications and robot vision systems.
arXiv Detail & Related papers (2023-06-12T08:57:15Z) - TransPath: Learning Heuristics For Grid-Based Pathfinding via
Transformers [64.88759709443819]
We suggest learning the instance-dependent proxies that are supposed to notably increase the efficiency of the search.
The first proxy we suggest to learn is the correction factor, i.e. the ratio between the instance independent cost-to-go estimate and the perfect one.
The second proxy is the path probability, which indicates how likely the grid cell is lying on the shortest path.
arXiv Detail & Related papers (2022-12-22T14:26:11Z) - A Data-driven Pricing Scheme for Optimal Routing through Artificial
Currencies [1.3419982985275638]
Mobility systems often suffer from a high price of anarchy due to the uncontrolled behavior of selfish users.
This paper presents a data-driven approach to automatically adapt artificial-currency tolls within repetitive-game settings.
arXiv Detail & Related papers (2022-11-27T11:23:29Z) - Concepts and Algorithms for Agent-based Decentralized and Integrated
Scheduling of Production and Auxiliary Processes [78.120734120667]
This paper describes an agent-based decentralized and integrated scheduling approach.
Part of the requirements is to develop a linearly scaling communication architecture.
The approach is explained using an example based on industrial requirements.
arXiv Detail & Related papers (2022-05-06T18:44:29Z) - Learning Optimization Proxies for Large-Scale Security-Constrained
Economic Dispatch [11.475805963049808]
Security-Constrained Economic Dispatch (SCED) is a fundamental optimization model for Transmission System Operators (TSO)
This paper proposes to learn an optimization proxy for SCED, i.e., a Machine Learning (ML) model that can predict an optimal solution for SCED in milliseconds.
Numerical experiments are reported on the French transmission system, and demonstrate the approach's ability to produce, within a time frame that is compatible with real-time operations.
arXiv Detail & Related papers (2021-12-27T00:44:06Z) - Estimating the Robustness of Public Transport Systems Using Machine
Learning [62.997667081978825]
Planning public transport systems is a highly complex process involving many steps.
Integrating robustness from a passenger's point of view makes the task even more challenging.
In this paper, we explore a new way of such a scenario-based robustness approximation by using methods from machine learning.
arXiv Detail & Related papers (2021-06-10T05:52:56Z) - Bayesian Inference for Optimal Transport with Stochastic Cost [22.600086666266243]
In machine learning and computer vision, optimal transport has had significant success in learning generative models.
We introduce a framework for inferring the optimal transport plan distribution by induced cost.
We also tailor an HMC method to sample from the resulting transport plan distribution.
arXiv Detail & Related papers (2020-10-19T09:07:57Z) - A Multi-Agent System for Solving the Dynamic Capacitated Vehicle Routing
Problem with Stochastic Customers using Trajectory Data Mining [0.0]
E-commerce has created new challenges for logistics companies, one of which is being able to deliver products quickly and at low cost.
Our work presents a multi-agent system that uses trajectory data mining techniques to extract territorial patterns and use them in the dynamic creation of last-mile routes.
arXiv Detail & Related papers (2020-09-26T21:36:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.