Order Allocation System for Mobile Agents
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Solution Overview
Problem
Traditional methods for assigning location-based orders to mobile agents, such as taxis or repair services, are limited by human error and do not maximize resource utilization, as they rely on dispatcher knowledge and do not account for agents' real-time availability and movement.
Innovation Solution
A method that involves tracking agents' availability by maintaining current order details and prioritizing locations based on when agents will be free to service orders, allowing for efficient allocation of orders to the most suitable agent without the need for continuous querying, using a system that updates dynamically with GPS information and journey time calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional dispatcher-based methods are used to assign orders to mobile agents, then the system is simple to operate, but it is limited by human error and does not maximize resource utilization
Solution Approach 1:
The patent replaces the mechanical dispatcher-based system with an automated computer-based system. The computer automatically queries mobile agents for their current locations and destinations, processes this information, and assigns orders without human intervention. This substitution eliminates human error while managing system complexity through automated algorithms.
Solution Approach 2:
The mobile agents themselves provide the information needed for assignment by querying their own current location and destination. The system receives this self-provided data from agents, processes it, and makes automated assignment decisions. This self-service approach reduces the burden on dispatchers and improves assignment accuracy.
2Extent of automation
If GPS information is used to determine the closest available agent, then automation is increased, but the system does not account for agents moving away from callers or becoming unavailable
Solution Approach 1:
The system performs preliminary actions by querying mobile agents for their current locations and destinations before making assignment decisions. It also predicts when agents will become available by considering their current tasks and travel times. This preliminary information gathering enables the system to make more reliable assignments that account for agents moving away from callers or becoming unavailable.
Solution Approach 2:
The system continuously receives feedback from mobile agents about their current status, location, and destination. This feedback loop allows the system to update its knowledge of agent availability in real-time, adjusting assignments based on the latest information about agent movements and availability status.
3Reliability
If detailed local knowledge is required for dispatching positions, then assignment accuracy improves, but the system becomes more complex and harder to operate
Solution Approach 1:
The patent replaces the need for detailed local knowledge in dispatchers with an automated computer system that handles the complexity of location-based assignments. The computer processes GPS coordinates, calculates distances, and determines optimal assignments without requiring human expertise in local geography or traffic conditions.
Solution Approach 2:
The system uses standardized data structures and protocols to copy and transmit location information, agent status, and assignment details. This standardization eliminates the need for detailed local knowledge by using universal digital representations that can be processed automatically across different locations and contexts.
Data Source
AI summary
A method of allocating a location-related order to one of a number of mobile agents, such as taxicabs, delivery or repair vehicles. The method involves the following steps:a) holding current order details which identify at least the location and time at which each agent is expected to become free to fulfill new orders;b) keeping a listing of locations, where each location in the listing is prioritised for an agent according to the availability of the agent to reach that location after the agent becomes free;c) receiving a new order and recording the location and time at which this order is to be fulfilled;d) deciding, based on the prioritised listing of locations, which agent(s) are most suitable to take the new order; ande) allocating the new order to the identified agent(s).