Fleet Service Interface for Real-Time Task Prioritization
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Solution Overview
Problem
Conventional fleet management systems lack the capability to utilize real-time data for dynamic service scheduling, making it labor-intensive and inefficient to manage and service large fleets of shareable/rentable vehicles like scooters and bicycles in urban environments.
Innovation Solution
An automated platform that determines the status of each fleet vehicle, generates service tasks, assigns locations, and allocates users to perform these tasks based on proximity and user profiles, utilizing machine learning models to predict demand and optimize navigation routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional fleet management systems are used, then service scheduling is simple to implement, but productivity and efficiency are low due to labor-intensive manual management
Solution Approach 1:
The system enables self-service through automated service scheduling where the platform autonomously determines service tasks, assigns them to appropriate users, and optimizes routing without manual intervention. The system monitors fleet vehicle status automatically and generates service schedules based on real-time data, eliminating the need for labor-intensive manual management while improving productivity.
Solution Approach 2:
The patent replaces manual mechanical scheduling processes with an automated digital platform that uses algorithms to determine service tasks, assign users, and optimize routes. The system substitutes human labor and manual decision-making with automated computing systems that process real-time fleet data to generate optimized service schedules.
2Loss of time
If manual service scheduling is used, then system implementation is straightforward, but time consumption increases due to labor-intensive management
Solution Approach 1:
The system performs preliminary actions by pre-determining service tasks and optimizing routes in advance before service personnel need to act. The platform proactively monitors fleet vehicle status and generates service schedules ahead of time, allowing service personnel to prepare and execute tasks efficiently without time-consuming manual scheduling decisions.
Solution Approach 2:
The patent replaces manual time-consuming scheduling operations with automated computational systems that instantly process fleet data and generate optimized service schedules. The automation eliminates the time required for manual task assignment and route planning, significantly reducing the loss of time in service scheduling operations.
3Measurement precision
If real-time data collection is implemented, then service scheduling accuracy improves, but device complexity increases
Solution Approach 1:
The system achieves multi-functionality by using a single automated platform that performs multiple functions: collecting real-time fleet vehicle data, determining service tasks, assigning users, optimizing routes, and monitoring progress. This universal system handles diverse data collection needs through integrated functionality, improving measurement precision without proportionally increasing overall system complexity.
Solution Approach 2:
The automated platform acts as an intermediary between fleet vehicles and service personnel, collecting and processing real-time data from vehicles and translating it into actionable service schedules. The intermediary system manages the complexity of real-time data collection by providing a centralized interface that processes information and generates optimized service assignments.
4Productivity
If automated service scheduling is implemented, then productivity increases, but user allocation complexity increases
Solution Approach 1:
The system implements dynamic user allocation that adapts to changing conditions in real-time. The platform dynamically assigns service tasks to users based on current fleet vehicle locations, user availability, and service requirements, optimizing productivity through flexible, real-time adjustments rather than static predetermined assignments.
Solution Approach 2:
The system uses feedback mechanisms to monitor service task progress and adjust user allocations dynamically. The platform receives feedback from service personnel about task completion status and uses this information to optimize subsequent assignments, improving productivity through continuous refinement of user allocation based on actual performance data.
Data Source
AI summary
In one embodiment, a method for managing servicing of fleet vehicles includes: determining, based on data received from the fleet vehicles, current locations and statuses of the fleet vehicles in a region; determining service locations for servicing the fleet vehicles in the region, each service location being associated with a subset of the fleet vehicles; for each service location, determining a fleet vehicle demand and service task, the service task being determined at least based on the statuses of the subset of the fleet vehicles; for each service location, determining a priority score based on the service task, the demand, and the current vehicle locations; generating a service task list based on the service locations, service tasks, and priority scores; and providing for display the service task list to the user for instructing the user to service one or more of the fleet vehicles.