scholarly journals Real-Time Dynamic Pricing for Revenue Management with Reusable Resources and Deterministic Service Time Requirements

2016 ◽  
Author(s):  
Yanzhe Lei ◽  
Stefanus Jasin
Keyword(s):  
Revenue Management ◽  
Real Time ◽  
Service Time ◽  
Dynamic Pricing ◽  
Time Dynamic ◽  
Time Requirements ◽  
Deterministic Service

Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time Requirements

2020 ◽  
Vol 68 (3) ◽  
pp. 676-685 ◽  
Author(s):  
Yanzhe (Murray) Lei ◽  
Stefanus Jasin
Keyword(s):  
Revenue Management ◽  
Real Time ◽  
Service Time ◽  
Dynamic Pricing ◽  
Advance Reservation ◽  
Time Dynamic ◽  
Different Types ◽  
Time Requirements ◽  
Deterministic Service ◽  
Resource Capacity

In “Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time Requirements,” Lei and Jasin consider a fundamental dynamic pricing problem when resources are reusable. In this problem, demand arrives according to a price-sensitive nonstationary rate, requesting a service that uses a combination of different types of resources for a deterministic duration of time. The resources are reusable in the sense that they can be immediately used to serve a new customer on the completion of the previous service. Moreover, different customers may have different service time requirement and may book the service in advance. The objective is to construct a dynamic pricing control that maximizes expected total revenues. They develop real-time heuristic controls based on the solution of the deterministic relaxation of the original stochastic problem and show that the proposed controls are near optimal in the regime of large demand and large resource capacity.

scholarly journals Real-Time Dynamic Pricing with Minimal and Flexible Price Adjustment

2016 ◽  
Vol 62 (8) ◽  
pp. 2437-2455 ◽  
Author(s):  
Qi (George) Chen ◽  
Stefanus Jasin ◽  
Izak Duenyas
Keyword(s):  
Real Time ◽  
Dynamic Pricing ◽  
Price Adjustment ◽  
Time Dynamic

A Real-Time Dynamic Pricing Algorithm for Smart Grid With Unstable Energy Providers and Malicious Users

2016 ◽  
Vol 3 (4) ◽  
pp. 554-562 ◽  
Author(s):  
Qiang Tang ◽  
Kun Yang ◽  
Dongdai Zhou ◽  
Yuansheng Luo ◽  
Fei Yu
Keyword(s):  
Smart Grid ◽  
Real Time ◽  
Dynamic Pricing ◽  
Time Dynamic

Control Model Constructing on Real-Time Dynamic Reversible Lane in Condition of Connected Vehicle

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Lina Mao ◽  
Wenquan Li ◽  
Pengsen Hu ◽  
Guiliang Zhou ◽  
Huiting Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Control Model ◽  
Connected Vehicle ◽  
Time Dynamic

scholarly journals Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
E. Bertino ◽  
M. R. Jahanshahi ◽  
A. Singla ◽  
R.-T. Wu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Original Data ◽  
Local Analysis ◽  
Civil Infrastructure ◽  
Time Requirements ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
The Times ◽  
Acquisition Processes

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

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