134. Optimization of Dynamic Service Systems with Adaptive Resource Allocation and Pricing Strategies Based on Random Environments and Customer Feedback
Invited abstract in session WC-12: Optimisation under uncertainty for sustainability, stream Applications: AI, uncertainty management and sustainability.
Wednesday, 14:00-16:00Room: B100/8009
Authors (first author is the speaker)
| 1. | CHESOONG KIM
|
| Department of Business Administration, Sangji University |
Abstract
This paper models and optimizes a dynamic service system that adaptively adjusts resource allocation and service pricing in real-time, considering both random environmental changes and customer feedback. The system integrates features of multi-server and retrial queuing systems, modeling scenarios where the number of available servers fluctuates due to random environmental changes, such as in communication networks, and arrival rates dynamically change based on customer ratings. Service prices are also adjusted in real-time based on queue length and customer feedback to maximize system efficiency. A multi-dimensional Markov chain model is used to analyze the system's stability and performance. Optimal resource allocation and pricing policies are derived to maximize service provider revenue and enhance customer satisfaction. The efficiency of the proposed model is validated through numerical experiments under various scenarios, demonstrating its applicability in diverse fields such as communication networks, online platforms, and transportation systems.
Keywords
- Applications of continuous optimization
- Stochastic optimization
- Optimization under uncertainty
Status: accepted
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