Logistical Management System Graph-Based Task Automation
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Solution Overview
Problem
Current logistical management systems for transporting goods rely heavily on manual processes, leading to errors, inconsistency, and increased workload for operators, as they manually manage tasks such as route determination and task execution, which can result in forgotten or incorrectly entered tasks and inconsistent task management.
Innovation Solution
A system with a user interface, processor, and pre-generated graph data structure to automate task management by identifying nodes and edges representing locations and transit costs, generating preferred routes, and creating task icons for handlers, thereby centralizing and standardizing task creation and assignment, reducing human error and improving consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual management of orders and tasks is used, then operators can handle complex routing decisions, but errors and inconsistencies increase and operator burden increases
Solution Approach 1:
The system enables self-service automation where the logistical management system automatically generates tasks, determines routes, and manages transportation without requiring manual operator intervention for each task. The system serves itself by using pre-generated graph data structures to automatically compute optimal routes and generate task icons, eliminating the need for operators to manually manage routine transportation tasks while maintaining complex decision-making capabilities.
2Reliability
If automated task generation is implemented, then human error is reduced, but system complexity increases
Solution Approach 1:
The system performs preliminary action by pre-generating graph data structures that contain all possible locations, routes, and transportation edges before actual order processing occurs. This pre-computation stores routing information, transit costs, and connectivity data in advance, so that when an order is placed, the system can quickly generate tasks by simply querying the pre-existing graph structure rather than computing routes from scratch, thereby reducing errors while managing complexity through advance preparation.
3Productivity
If manual route determination is used, then flexibility in handling special cases is maintained, but time consumption increases
Solution Approach 1:
The system applies dynamics by making the graph data structure adaptable and flexible rather than rigid. The pre-generated graph allows for dynamic querying and route determination based on specific order requirements, pickup locations, and destination nodes. The system can dynamically select different paths through the graph based on real-time conditions, transit costs, and transportation constraints, enabling fast automated route determination that maintains flexibility for special cases through its configurable and adaptable graph-based architecture.
Data Source
AI summary
A system and methods to facilitate transporting goods is disclosed. In aspects, the system can have a user interface comprising a display, a non-transitory computer-readable medium comprising instructions, and a processor in communication with the non-transitory computer-readable medium and with the display. The instructions can be executed by the processor to process a user request for transporting the good from a pickup location to a destination location. Based on the user request, a preferred route from the pickup location to the destination location can be generated. The transporting can be controlled using a display by modifying a task icon.