Vehicle Scheduling Algorithm for Transportation Demand
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Solution Overview
Problem
Transportation agencies face challenges in manually scheduling vehicles along routes to meet varying public demands, which can be complex due to factors like time of day, events, or festivals, requiring a robust method to manage routes and schedule vehicles effectively.
Innovation Solution
A method and system that utilize processors to determine demands, constraints, and schedule vehicles based on parameters such as the count of vehicles, capacity, and performance metrics, employing Integer Linear Programming (ILP) or Greedy algorithms to optimize vehicle scheduling along routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual scheduling method is used to determine vehicle schedules, then flexibility in adjusting to various parameters (time of day, events, festivals) can be achieved, but the complexity and difficulty of managing routes and scheduling vehicles increases significantly
Solution Approach 1:
The patent replaces the manual mechanical scheduling process with an automated computer-based system that uses algorithms to determine vehicle schedules. The system processes multiple parameters (time of day, events, festivals, route demands) automatically, eliminating the complexity of manual management while maintaining adaptability to varying public demands.
Solution Approach 2:
The scheduling system performs self-service by automatically determining vehicle schedules based on input parameters without requiring manual intervention. The system independently processes route demands, constraints, and multiple parameters to generate optimized schedules, reducing the burden on transport agencies while adapting to varying conditions.
2Reliability
If manual scheduling is used considering all parameters (time, events, festivals), then public demands can be met, but the time and resources required for scheduling increase
Solution Approach 1:
The system performs preliminary action by pre-processing route demands and constraints, and by using algorithms to quickly generate schedules that consider all relevant parameters. This allows the system to meet public demands reliably while minimizing scheduling time, as the automated process can evaluate multiple scenarios and determine optimal schedules much faster than manual methods.
3Productivity
If more vehicles are scheduled along routes to meet varying public demands, then service coverage is improved, but the operational cost and resource allocation complexity increase
Solution Approach 1:
The system uses parameter changes by dynamically adjusting vehicle schedules based on varying demands, time of day, events, and festivals. The algorithm processes multiple parameters simultaneously to determine optimal vehicle allocation, improving service coverage while managing resource allocation complexity through automated optimization rather than manual planning.
Data Source
AI summary
The disclosed embodiments illustrate methods and systems for scheduling one or more first vehicles along a route in a transportation system. The method includes determining one or more demands pertaining to a commutation along a route, where the route comprises at least one pair of stations such that there is a unique path between the pair of stations. The method further includes determining a set of constraints associated with the transportation system. The set of constraints are based on at least a count of second vehicles plying on one or more routes in the transportation system at a time instance, a capacity of a first vehicle, and a performance metric of the transportation system. Further, the method includes determining a count of first vehicles, for plying along the route at the time instance, based on at least the set of constraints.