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2354. Last-mile Delivery With Heterogeneous Crowdshipping and Service Levels
Invited abstract in session WA-64: Last-Mile Delivery, stream VeRoLog - Vehicle Routing and Logistics.
Wednesday, 8:30-10:00Room: S16 (building: 101)
Authors (first author is the speaker)
1. | Tiago Monteiro
|
INESC TEC & FEUP | |
2. | João Pedro Pedroso
|
Campus da FEUP, INESC TEC and Faculdade de Ciencias, Universidade do Porto | |
3. | Ana Viana
|
INESC TEC and ISEP -- School of Engineering, Polytechnic of Porto |
Abstract
Innovative strategies are emerging to handle the growing challenges of last-mile delivery, such as high operational costs and increasing customer expectations. Crowdshipping is a novel solution for last-mile delivery that offers a quick and cheaper way to provide same-day delivery and the flexibility to scale up or down the delivery capacity as needed: retailers, in addition to professionals, employ ordinary citizens (a.k.a. occasional couriers - OCs) to perform deliveries (this is the case of companies such as Amazon Flex and Roadie). The OCs can be categorized into two classes depending on their commitment to the company: dedicated OCs, who actively register, provide a schedule with their availability, and can accept some proposed deliveries for compensation; and en-route OCs, who are in-store customers who make deliveries on their commute, also for a compensation.
This work proposes a decision-support tool to empower decision-makers with insights into the trade-off between customer service levels and operational costs across different workforce compositions and compensation values. To achieve this, we conduct simulations where different compositions handle deliveries: exclusively the retailer’s fleet, or the en-route OCs, or the dedicated OCs, or a hybrid combination of those workforces. As a collateral result, the traveled distance of different workforce compositions and compensation strategies is measured, offering insights into their environmental impact.
Keywords
- Supply Chain Management
- Vehicle Routing
- Decision Support Systems
Status: accepted
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