Predictive Node Allocation for Queue Demand and Adjacency Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in efficiently allocating check-in resources such as desks or processor cores while considering adjacency constraints and quality of service, leading to long waiting times and operational inefficiencies.
Innovation Solution
A robust optimization model that determines optimal node allocation by considering adjacency constraints and quality of service measures, using a simulation process to predictively allocate resources based on dynamic passenger demand and social distancing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the number of check-in desks is increased to reduce waiting times, then passenger service quality is improved, but operating costs and resource allocation complexity increase
Solution Approach 1:
The system performs preliminary actions by predicting future passenger demand for each time interval and proactively allocating desks before peak periods occur. The optimization model pre-calculates the optimal number of desks needed for each time slot based on forecasted arrivals, ensuring desks are available when needed without requiring permanent expansion of check-in capacity.
2Productivity
If check-in capacity is expanded to handle peak demand, then service quality during peak hours is improved, but operating costs increase
Solution Approach 1:
The system implements dynamic resource allocation where the number of active check-in desks changes according to real-time and predicted demand patterns. During low-demand periods, fewer desks are activated to reduce operating costs, while during predicted peak periods, the system automatically allocates additional desks to maintain service quality, creating a flexible capacity that adapts to varying operational requirements.
3Reliability
If more resources are allocated to handle adjacent node constraints, then constraint satisfaction is improved, but resource allocation efficiency decreases
Solution Approach 1:
The optimization model incorporates adjacency constraints as parameters in the mathematical formulation, where binary variables determine whether adjacent desks are assigned to the same agent. By integrating these constraints directly into the optimization parameters rather than treating them as separate restrictions, the system efficiently finds solutions that satisfy adjacency requirements while maintaining overall resource allocation efficiency.
Data Source
Figure 1
Figure 2~3
Figure 4
AI summary
Methods, methods, systems, and computer program products for implementing an optimization process and simulation process to predictively allocate nodes in a queue. A first number of tasks to be processed in a task queue within a current time interval is determined. A second number of tasks from the task queue that are unprocessed after a time interval that immediately precedes the current time interval is determined. A resource allocation constraint for each resource is determined. A subset of the number of nodes to be assigned to each resource in the time interval is determined based on the first number of tasks, the second number of tasks, and the resource allocation constraint for each resource. A resource allocation schedule, which includes the determined subset of the number of nodes assigned to each resource, is provided to a resource allocation demand server.