Non-linear Destination Selection for Event Cost Optimization
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Solution Overview
Problem
Current event planning tools are constrained by availability and temporal factors, such as holidays, which limit the optimization of event costs, particularly in destination and travel costs, leading to suboptimal solutions for event organizers.
Innovation Solution
The development of systems and methods that use logistics models and historical data to determine an optimal event destination selection, independent of availability, by simulating travel costs and attendee dispersion, and providing a budgeting and cost estimation tool for event planning, allowing for theoretical planning solutions that consider long-term trends and live data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If tactical resource allocation tools use availability-based constraints for event planning, then meeting time and place can be determined based on attendee availability, but the overall optimal destination cannot be determined and costs are not minimized
Solution Approach 1:
The system performs preliminary actions by collecting historical pricing data and simulating travel costs for multiple destinations before the actual event planning decision. This allows the system to pre-calculate optimal destinations based on cost patterns, which are then used to guide the final destination selection independent of temporary availability constraints.
Solution Approach 2:
The system introduces an intermediary optimization algorithm that acts as a mediator between availability-based tools and optimal cost determination. This algorithm processes attendee dispersion patterns and travel cost simulations to generate recommended destinations, bridging the gap between simple availability checking and complex optimal selection.
2Loss of time
If event planning is constrained by momentary temporal factors such as holidays and flight availability, then planning can be done within given time windows, but optimal event costs cannot be achieved due to flight cost variability
Solution Approach 1:
The system performs cost simulations and historical analysis in advance, building a knowledge base of optimal destinations and cost patterns before the actual planning deadline. This preliminary work allows planners to make informed decisions quickly within tight time windows without sacrificing cost optimization.
Solution Approach 2:
The system changes the planning approach by shifting from availability-based parameters to cost-optimization parameters. By using historical pricing data and simulation results, the system transforms the planning problem from one constrained by temporal availability to one optimized by cost parameters, enabling better cost efficiency even within fixed time windows.
3Productivity
If traditional tools provide destination options based on current availability, then immediate planning decisions can be made, but planners lack knowledge of the optimal solution compared to available solutions
Solution Approach 1:
The system performs preliminary simulations and historical analysis to establish what the optimal solution would be, then uses this information to evaluate and compare against currently available options. This allows planners to quickly assess whether available destinations are truly optimal or if better options exist, maintaining decision speed while improving information quality.
Solution Approach 2:
The system implements feedback by comparing currently available destination options against the theoretically optimal solution derived from historical data and simulations. This feedback mechanism informs planners whether to accept available options or adjust plans to achieve better cost optimization, preventing information loss about the true optimal solution.
Data Source
AI summary
Apparatus and methods for machine-based planning. The apparatus may cluster a plurality of geographically different resources into a plurality of clusters of proximate resources. The apparatus may calculate a cost of transporting each resource to each of a plurality of destinations. The apparatus may map each cluster to one of the destinations to determine a sum of costs of transporting all of the resources to the destinations. The apparatus may assign to each of the plurality of destinations only resources: that are mapped to the destination; for which the destination has sufficient capacity to accommodate the resources; and whose assignment to the destination does not exclude from the destination, by filling the capacity, a different resource that is: mapped to the destination; and has a net cost that is higher than a net cost of the resource of the assignment.