Replanned Plan Output Device for Public Transport Scheduling
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Solution Overview
Problem
Public transportation systems face challenges in automatically replanning operation schedules due to unpredictable events, relying heavily on manual input and resulting in numerous changes that are not suitable for actual operations.
Innovation Solution
A replanned plan output device that uses a vehicle-in-charge candidate determination unit and a vehicle-in-charge selection unit to identify and select alternative vehicles for difficult-to-perform schedules, considering the number of changes and additional costs, employing an interaction model and optimization calculation technology like CMOS annealing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual replanning by operators is used, then flexibility in handling accidents is improved, but productivity and consistency deteriorate due to reliance on individual expertise
Solution Approach 1:
The system performs automatic replanning without human intervention by using the interaction model to generate optimized schedules. The computer system serves itself by autonomously determining vehicle assignments and schedule adjustments based on accident information and operational constraints.
Solution Approach 2:
Manual mechanical replanning by operators is replaced with an automated computational system using interaction models and optimization algorithms. The mechanical process of manual schedule adjustment is substituted with electronic calculation and automatic generation of replanned schedules.
2Productivity
If complete automation of replanning is implemented, then productivity is improved, but ease of operation deteriorates due to complexity of optimization algorithms
Solution Approach 1:
The interaction model acts as an intermediary between the accident input and the replanned schedule output. It transforms the complex optimization problem into a structured format that can be efficiently solved, mediating between the raw data and the final optimized schedule.
Solution Approach 2:
The system changes parameters such as vehicle capacity, travel time, and cost coefficients dynamically based on the accident scenario. By adjusting these parameters in the interaction model, the system adapts to different accident conditions and generates optimized schedules without requiring complex manual intervention.
3Device complexity
If traditional optimization methods are used, then computational simplicity is maintained, but reliability deteriorates due to inability to handle large-scale problems efficiently
Solution Approach 1:
The replanning problem is segmented into smaller sub-problems by using the interaction model framework. The overall optimization is divided into determining vehicle assignments, schedule adjustments, and cost calculations as separate but coordinated components, making the large-scale problem manageable and reliable.
Solution Approach 2:
The system adds a new dimension to the optimization by introducing the interaction model as an additional layer between the input data and the optimization algorithm. This dimensional addition allows the system to handle complex constraints and objectives that would be intractable with traditional single-layer optimization methods.
4Adaptability or versatility
If frequent schedule changes are made to accommodate accidents, then adaptability is improved, but loss of time and resources increases due to numerous plan modifications
Solution Approach 1:
The system incorporates feedback mechanisms by evaluating the impact of each potential schedule change on the overall operation. The interaction model calculates costs and constraints for different replanning options, providing feedback that guides the selection of changes that minimize time loss while maintaining necessary adaptability.
Solution Approach 2:
Instead of completely redesigning the entire schedule in response to an accident, the system applies partial action by making targeted adjustments only to the affected portions of the plan. The interaction model identifies minimal changes needed to accommodate the accident while preserving the majority of the original schedule, thereby reducing time loss.
Data Source
AI summary
A replanned plan output device that outputs a replanned plan for an operation plan of performing planned schedules in order by an operation of a vehicle in charge, includes: a replanned candidate determination unit configured to, when a difficult-to-perform schedule that becomes difficult to be performed by the vehicle in charge among the planned schedules occurs, determine another vehicle as a candidate for a substitute vehicle for performing the difficult-to-perform schedule; and a substitute vehicle selection unit configured to, when a plurality of candidates for the substitute vehicle are set, select the substitute vehicle for performing the difficult-to-perform schedule from the plurality of candidates for the substitute vehicle in consideration of the number of changes of the vehicle in charge and an additional cost of each of the schedules specified in the operation plan.