Distributed Fleet Return Routing With Combined Delivery Orders
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Solution Overview
Problem
Existing delivery systems face inefficiencies in managing a large volume of delivery and return orders, particularly the frustration of consumers in routing package returns, which often require manual handling and lack optimization in combining delivery and return routes.
Innovation Solution
A central server computer system that optimizes routes by combining delivery and return orders, considering factors like time windows and bundling, and uses a scoring algorithm to select transporters based on historical performance and order type, leveraging existing transporter fleets for efficient item transportation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If separate delivery and return routes are managed independently, then route planning is simpler, but total travel time and cost increase
Solution Approach 1:
The patent combines delivery orders and return orders into unified route plans. The server creates optimal routes that integrate both delivery and return shipments, allowing transporters to complete both types of deliveries in a single trip. This merging approach reduces total travel time and costs while maintaining manageable planning complexity through automated optimization algorithms.
Solution Approach 2:
The server performs preliminary route planning and optimization before actual delivery execution. By pre-calculating optimal routes that combine delivery and return orders, and assigning them to appropriate transporters in advance, the system minimizes travel time without requiring complex real-time decision-making during delivery operations.
2Productivity
If return orders are manually handled separately, then handling procedures are simpler, but delivery efficiency decreases
Solution Approach 1:
The system automatically handles return order integration without requiring manual intervention. The server autonomously receives return order data, optimizes routes, selects transporters, and assigns deliveries. This self-service approach increases delivery efficiency by eliminating manual coordination while the automated nature of the system keeps complexity manageable.
Solution Approach 2:
The server acts as an intermediary between return order generators (consumers) and transporters. It receives return order data from consumers, processes the routing optimization, and automatically assigns deliveries to transporters. This intermediary function increases efficiency by centralizing coordination while managing system complexity through standardized processing protocols.
3Adaptability or versatility
If existing transporter fleets are leveraged, then resource utilization increases, but matching complexity increases
Solution Approach 1:
The server changes key parameters for route optimization including time windows, geographic locations, order priorities, and transporter capabilities. By adjusting these parameters dynamically, the system matches return orders to appropriate transporters from existing fleets. This parameter-based approach increases fleet utilization adaptability while the automated parameter processing keeps selection complexity manageable.
Data Source
AI summary
A method includes a server computer receiving data relating to a plurality of delivery orders from service providers to end users, and data relating to a plurality of delivery orders from end users to service providers. The server computer determines a plurality of routes corresponding to the plurality of delivery orders. The server computer can then determine a set of optimal route plans by combining delivery order routes. The server computer can then receive acceptances from a plurality of transporters that will execute the set of optimal route plans. The server computer can then facilitate execution of the optimal route plans.