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Crowdsourced Delivery: A Modern Solution for Fast Parcels

Discover how crowdsourced delivery is changing the way we receive packages.

Yuki Oyama, Takashi Akamatsu

― 6 min read


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Table of Contents

Crowdsourced delivery is gaining traction as more people shop online and expect their parcels to appear at their doorsteps in no time. The concept is simple: everyday people, like commuters and travelers, take on delivery tasks while they go about their daily routines. Think of it as a way to turn a mundane trip to the store into a side gig.

This approach uses the extra space in cars and the time people already spend on the road, which helps lighten the load on traditional delivery services. Not only does it save cash for customers, but it can also be kinder to the environment by reducing the number of delivery trucks clogging the streets.

The Challenge of Matching Shippers and Drivers

The crux of the crowdsourced delivery system lies in matching those who need stuff delivered (shippers) with those who are willing to deliver it (drivers). Sounds easy, right? Well, not quite. There are many variables at play, like who is available to drive, what kind of tasks they’re willing to take, and how much they want to be paid.

Usually, shippers want to get their packages delivered as cheaply and quickly as possible, while drivers are looking for a decent reward for their trouble. Balancing these interests can be a tricky endeavor!

Demand and Supply Elasticity: What Are They?

To tackle this issue, it's essential to consider something called demand and supply elasticity. Essentially, this refers to how sensitive shippers and drivers are to changes in price. If prices go up, will shippers still want to use the service? Will drivers be less willing to deliver for lower pay? Understanding these behaviors can help create a better matching system.

Dealing with Complexity: Task Bundling

Another layer of complexity is task bundling. Instead of handling one delivery at a time, drivers can take on multiple tasks during a single trip. Imagine a driver picking up a pizza on their way to drop off a package. This can save time and money for both drivers and shippers, but it complicates the matching process even more!

The Role of Technology

Thanks to modern technology, we can leverage smart mobile devices and communication systems to make this matching process easier. By using apps, drivers can get notifications about nearby delivery tasks while they manage their daily activities. It’s like having a personal assistant telling you when to pick up a package while you're already out and about!

Proposed Solutions for CSD Challenges

Researchers have been hard at work figuring out how to solve these challenges. They’ve created new methods to gather information about shippers' and drivers' preferences through auctions. This helps ensure that everyone plays fair and gets the best possible outcome.

By breaking down the overall problem into smaller parts, they can handle the complexity better. Think of it like assembling a puzzle: it’s much easier to see how the pieces fit together if you look at a few at a time instead of trying to tackle the whole thing at once!

Transforming the Problem into a Traffic Assignment Model

One clever approach is to reframe the matching issue as a traffic assignment problem. This means imagining the delivery system like a network of roads, where drivers and shippers are connected by various paths. In doing so, researchers can find the most efficient routes for deliveries and optimize how tasks are assigned.

The Auction Mechanism

A key part of this solution involves using an auction mechanism. Shippers can place bids for drivers to accept delivery tasks, allowing price competition to drive a better match. This approach encourages efficiency, as drivers are incentivized to declare their true costs and preferences to maximize social benefits.

Computational Efficiency: Speeding Things Up

One of the significant breakthroughs in this study is how to make the matching process much quicker. Traditional methods often took far too long to yield usable results. However, with the new approaches, the time needed to solve the matching problem can be cut down significantly—sometimes as much as 700 times faster! That’s like going from a slow, old dial-up Internet connection to lightning-fast fiber optics.

How Does This Work in Practice?

In practical terms, this means that even during a busy delivery period, the system can quickly match shippers and drivers, making the entire process more efficient for everyone involved.

Let’s say you want to send Grandma a birthday gift (a lovely knitted sweater), and you’re hoping to have it delivered the same day. Thanks to this new system, you can place your request in the app, and the platform will analyze which nearby drivers are available to help. If one of them also happens to be heading in Grandma’s direction, bingo! A match is made.

Tackling Individual Preferences

Different people have different desires and constraints. Shippers may have certain time windows in which they need packages delivered, while drivers may prefer to work in specific areas or at particular times. The proposed approach takes these individual preferences into account, so everyone can find a suitable match without compromising their personal needs.

Real-World Application in Urban Areas

Urban settings, which typically have higher demands for crowded delivery options due to many online orders, can especially benefit from these systems. The combination of technology, smart design, and individual preferences creates a robust framework for improving the efficiency of parcel delivery.

Reducing Environmental Impact

Fewer dedicated delivery trucks mean less traffic, fewer emissions, and reduced energy consumption. We can reduce the carbon footprint of delivery services by tapping into existing travel paths and using everyday vehicles. It’s a win-win scenario for everyone involved!

Final Remarks

In summary, crowdsourced delivery is a remarkable way to utilize everyday drivers and turn routine trips into delivery opportunities. With modern technology, innovative matching methods, and the consideration of individual preferences, this system not only offers quick and efficient solutions but also fosters a more sustainable approach to delivery.

Who knew that sending a gift could actually help the planet? So, next time you order a package online, just remember: It might be your neighbor playing the role of Santa Claus, making your life easier while running their errands!

Let’s keep our fingers crossed for more innovations in this exciting field. The future of delivery might just be at our doorstep!

Original Source

Title: A market-based efficient matching mechanism for crowdsourced delivery systems with demand/supply elasticities

Abstract: Crowdsourced delivery (CSD) is an emerging business model that leverages the underutilized or excess capacity of individual drivers to fulfill delivery tasks. This paper presents a general formulation of a larege-scale two-sided CSD matching problem, considering demand/supply elasticity, heterogeneous preferences of both shippers and drivers, and task-bundling. We propose a set of methodologies to solve this problem. First, we reveal that the fluid-particle decomposition approach of Akamatsu and Oyama (2024) can be extended to our general formulation. This approach decomposes the original large-scale matching problem into a fluidly-approximated task partition problem (master problem) and small-scale particle matching problems (sub-problems). We propose to introduce a truthful auction mechanism to sub-problems, which enables the observation of privately perceived costs for each shipper/driver. Furthermore, by finding a theoretical link between auction problems and parturbed utility theory, we succeed in accurately reflecting the information collected from auctions to the master problem. This reduces the master problem to a smooth convex optimization problem, theoretically guaranteeing the computational efficiency and solution accuracy of the fluid approximation. Second, we transform the master problem into a traffic assignment problem (TAP) based on a task-chain network. This transformation overcomes the difficulty in enumerating task bundles. Finally, we formulate the dual problem of the TAP, whose decision variable is only a price/reward pattern at market equilibrium, and develop an efficient accelerated gradient descent method. The numerical experiments clarify that our approach drastically reduces the computational cost of the matching problem (~700 times faster than a naive method) without sacrificing accuracy of the optimal solution (mostly within 0.5% errors).

Authors: Yuki Oyama, Takashi Akamatsu

Last Update: 2024-12-29 00:00:00

Language: English

Source URL: https://arxiv.org/abs/2412.20395

Source PDF: https://arxiv.org/pdf/2412.20395

Licence: https://creativecommons.org/licenses/by-nc-sa/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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