An official website of the United States government
We model the hierarchical and decentralized nature of product transitions using a mixed-integer bilevel program with two followers, a manufacturing unit and an engineering unit. The leader, corporate management, seeks to maximize revenue over a finite planning horizon. The manufacturing unit uses factory capacity to satisfy the demand for current products. The demand for new products, however, cannot be fulfilled until the engineering unit completes their development, which, in turn, requires factory capacity for prototype fabrication. We model this interdependency between the engineering and manufacturing units as a generalized Nash equilibrium game at the lower level of the proposed bilevel model. We present a reformulation where the interdependency between the followers is resolved through the leader’s coordination, and we derive a solution method based on constraint and column generation. Our computational experiments show that the proposed approach can solve realistic instances to optimality in a reasonable time. We provide managerial insights into how the allocation of decision authority between corporate leadership and functional units affects the objective function performance. This paper presents the first exact solution algorithm to mixed-integer bilevel programs with interdependent followers, providing a flexible framework to study decentralized, hierarchical decision-making problems.
more »
« less
Decentralized Chernoff Test in Sensor Networks
We propose a decentralized, sequential and adaptive hypothesis test in sensor networks, which extends Chernoff’s test to a decentralized setting. We show that the proposed test achieves the same asymptotic optimality of the original one, minimizing the expected cost required to reach a decision plus the expected cost of making a wrong decision, when the observation cost per unit time tends to zero. We also show that the proposed test is parsimonious in terms of communications. Namely, in the regime of vanishing observation cost per unit time, the expected number of channel uses required by each sensor to complete the test converges to four.
more »
« less
- Award ID(s):
- 1717942
- PAR ID:
- 10062979
- Date Published:
- Journal Name:
- Proceedings of the International Symposium on Information Theory (ISIT)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract This work details the partially observable markov decision process (POMDP) and the point-based value iteration (PBVI) algorithms for use in multisensor systems, specifically, a sensor system capable of heart rate (HR) estimation through wearable photoplethysmography (PPG) and accelerometer signals. PPG sensors are highly susceptible to motion artifact (MA); however, current methods focus more on overall MA filters, rather than action specific filtering. An end-to-end embedded human activity recognition (HAR) System is developed to represent the observation uncertainty, and two action specific PPG MA reducing filters are proposed as actions. PBVI allows optimal action decision-making based on an uncertain observation, effectively balancing correct action choice and sensor system cost. Two central systems are proposed to accompany these algorithms, one for unlimited observation access and one for limited observation access. Through simulation, it can be shown that the limited observation system performs optimally when sensor cost is negligible, while limited observation access performs optimally when a negative reward for sensor use is considered. The final general framework for POMDP and PBVI was applied to a specific HR estimation example. This work can be expanded on and used as a basis for future work on similar multisensor system.more » « less
-
The electrification of transportation is a growing strategy to reduce mobile source emissions and air pollution globally. To encourage adoption of electric vehicles, there is a need for reliable evidence about pricing in pub-lic charging stations that can serve a greater number of communities. However, user-entered pricing information by thousands of charge point operators (CPOs) has created ambiguity for large-scale aggregation, increasing both the cost of analysis for researchers and search costs for consumers. In this paper, we use large language models to address standing challenges with price discovery in distributed digital data. We show that generative AI models can effectively extract pricing mechanisms from unstructured text with high accuracy, and at substantially lower cost of three to four orders of magnitude lower than human curation (USD 0.006 pennies per observation). We exploit the few-shot learning capabilities of GPT-4 with human-in-the-loop feedback—beating prior classification performance benchmarks with fewer training data. The most common pricing models include free, energy-based (per kWh), and time-based (per unit time), with tiered pricing (variable pricing based on usage) being the most prevalent among paid stations. Behavioral insights from a US nationally representative sample of 13,008 stations suggest that EV users are commonly frustrated with the slower than expected charging rates and the total cost of charging. This study uncovers additional consumer barriers to charging services concerning the need for better price standardization.more » « less
-
Gentry, E; Ju, F; Liu, X (Ed.)This research investigates optimal pricing strategies in a service-providing queueing system where customers may renege before service completion. Prices are quoted upon customer arrivals and the incoming customers join the system if their willingness to pay exceeds the quoted price. While waiting in line or during service, customers may get impatient and leave without service, incurring an abandonment cost. There is also a per-unit time per-customer holding cost. Our objective is to maximize the long-run average profit through optimal pricing policies. We model the problem as a Markov decision process and identify the optimal pricing using policy iteration. We also study the structure of the optimal pricing policy. Furthermore, we show that under mild assumptions, the optimal price increases as the number of customers in the system increases. When those assumptions do not hold, optimal price decreases and then increases as the number of customers in the system grows.more » « less
-
Gentry, E; Ju, F; Liu, X (Ed.)This research investigates optimal pricing strategies in a service-providing queueing system where customers may renege before service completion. Prices are quoted upon customer arrivals and the incoming customers join the system if their willingness to pay exceeds the quoted price. While waiting in line or during service, customers may get impatient and leave without service, incurring an abandonment cost. There is also a per-unit time per-customer holding cost. Our objective is to maximize the long-run average profit through optimal pricing policies. We model the problem as a Markov decision process and identify the optimal pricing using policy iteration. We also study the structure of the optimal pricing policy. Furthermore, we show that under mild assumptions, the optimal price increases as the number of customers in the system increases. When those assumptions do not hold, optimal price decreases and then increases as the number of customers in the system grows.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
