Delivery Route Assignment Using Historical Tour Templates
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Solution Overview
Problem
Existing delivery systems face inefficiencies in load and route assignments, leading to increased costs and time due to empty miles driven by delivery trucks, and challenges in determining optimal delivery routes and schedules.
Innovation Solution
A computing device aggregates historical load data to determine optimal paths and nodes, constructing directed edges and paths based on time windows, and assigns live loads to vehicles in real-time using tour templates generated from historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If delivery systems increase the number of delivery orders and vehicles, then delivery capacity increases, but delivery costs and empty miles increase
Solution Approach 1:
The system performs preliminary aggregation of historical load data to identify patterns and determine optimal paths before actual delivery operations. Tour templates are pre-generated based on historical data analysis, allowing the system to plan routes in advance and reduce empty miles before deliveries occur
Solution Approach 2:
The system uses historical load data as feedback to continuously improve route optimization. By analyzing past delivery patterns, the system refines tour templates and path determination algorithms to minimize empty miles in subsequent operations
2Loss of time
If delivery systems use traditional route determination methods, then implementation is simple, but delivery time and costs increase
Solution Approach 1:
The system pre-determines optimal paths by aggregating historical load data and constructing tour templates before actual deliveries. This preliminary route planning based on historical patterns enables faster execution with reduced delivery times
Solution Approach 2:
The system dynamically determines paths by constructing directed edges based on time window attributes and travel times. The path determination adapts to specific delivery constraints and conditions, optimizing routes in real-time based on current requirements
3Productivity
If delivery systems aggregate historical data to determine optimal paths, then delivery efficiency improves, but data processing complexity increases
Solution Approach 1:
The system segments historical load data by attributes and groups similar loads together to identify patterns. This segmentation approach simplifies complex data processing by organizing historical data into manageable categories that reveal optimal routing patterns
Solution Approach 2:
The system creates tour templates as simplified representations of optimal routes derived from historical data. These templates serve as reusable copies of proven effective paths, reducing the need to reprocess historical data for each new delivery while maintaining high efficiency
Data Source
AI summary
A load and route assignment system is provided and generally includes a computing device and a database. The database may store historical inbound load data and historical outbound load data related to previous inbound and outbound loads. The computing device can obtain and aggregate the historical inbound and outbound load data from the database, and determine an optimal path based on the aggregated historical data. The optimal path, along with load attribute data, may be stored in the database as a tour template for future load executions. The computing device may use the tour templates to determine future load assignments to vehicles. The computing device may also obtain real-time load requests, and match them to one or more of a plurality of tour templates. The computing device may assign the matched real-time load requests to a vehicle for execution in accordance with the corresponding load and tour template.