Elevator call allocation with adaptive multi-objective optimization
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Solution Overview
Problem
Conventional elevator control systems struggle to optimize waiting times and transit times effectively, especially during varying traffic conditions, as they often prioritize one objective over the others, leading to inefficiencies and potential saturation during heavy traffic.
Innovation Solution
Implementing an adaptive multi-objective optimization system that dynamically adjusts weights for waiting time and transit time objectives based on current passenger traffic indicators, using sigmoid functions and exponential smoothing to optimize elevator call allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a pure waiting time objective is used to minimize average waiting time, then waiting time is reduced, but handling capacity decreases and the system saturates sooner during heavy traffic
Solution Approach 1:
The patent applies dynamics by making the objective function adaptive rather than static. The controller dynamically adjusts the weighting between waiting time minimization and handling capacity based on real-time traffic conditions. During light traffic, the system prioritizes waiting time reduction, while during heavy traffic, it shifts focus to maximizing handling capacity, thus resolving the contradiction between these two opposing goals.
Solution Approach 2:
The patent changes the parameters of the objective function based on traffic conditions. By monitoring traffic indicators and adjusting the weight coefficients in the multi-objective function, the system transitions between different optimization priorities. This parameter adaptation allows the system to achieve both short waiting times and high handling capacity under different operational conditions.
2Productivity
If a pure time to destination objective is used to maximize handling capacity, then handling capacity increases, but waiting time optimization deteriorates
Solution Approach 1:
The system dynamically switches optimization priorities based on traffic intensity. When traffic is light, the controller emphasizes waiting time minimization. When traffic becomes heavy, it transitions to prioritizing time to destination and handling capacity, thus adaptively resolving the contradiction between these two objectives.
Solution Approach 2:
The controller adjusts the weight parameters in the objective function according to traffic conditions. By changing these parameters dynamically, the system can shift focus between waiting time optimization and handling capacity maximization, achieving both goals under different operational scenarios.
3Device complexity
If fixed weight optimization is used, then the objective function is simple, but it cannot adapt to varying traffic conditions
Solution Approach 1:
The patent implements feedback by continuously monitoring traffic indicators and using this information to adjust the objective function weights. The controller receives feedback about current traffic conditions and adapts its optimization strategy accordingly, enabling the system to respond dynamically to changing operational environments while maintaining a relatively simple overall structure.
Data Source
AI summary
Apparatuses, methods and computer programs for elevator call allocation with adaptive multi-objective optimization are disclosed. At least some of the disclosed embodiments may allow adaptively and smoothly changing an objective function according to passenger traffic. This in turn may allow minimizing waiting times in all traffic situations compared to using a fixed objective function. Furthermore, at least some of the disclosed embodiments may allow adaptively and smoothly changing the objective function according to the traffic while taking user preferences into consideration via a single transit time target parameter.