Dynamic Pricing for Electric Vehicle Charging at a Commercial Charging Station in Presence of Uncertainty: A Multi-armed Bandit Reinforcement Learning Approach

Qureshi, Ubaid; Mushtaq, Mehreen; Qureshi, Juveeryah; Aiman, Mir; Ali, Mansha; Ali, Shahnawaz

doi:10.1007/978-981-19-6634-7_44

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Common algorithms employed in the context of stationary MAB comprise the Greedy algorithm, Epsilon-greedy algorithm, and Epsilon-greedy with upper confidence bound algorithm. In accordance with [19], the Greedy method underperforms in the long run due to its convergence to suboptimal policies. The Epsilon-greedy method fares better due to its ongoing exploration.…”

Section: Stationary Vs Non-stationarymentioning

confidence: 96%

“…Qureshi et al [19] tackle the dynamic pricing problem in the context of a non-stationary demand faced by a commercial charging station aiming to maximize long-term profit. They devise a nonstationary algorithm for their dynamic pricing predicament, focusing solely on recent information about uncertain demand rather than historical data.…”

Section: Stationary Vs Non-stationarymentioning

confidence: 99%

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Survey of dynamic pricing based on Multi-Armed Bandit algorithms

2024

ACE

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Dynamic pricing seeks to determine the most optimal selling price for a product or service, taking into account factors like limited supply and uncertain demand. This study aims to provide a comprehensive exploration of dynamic pricing using the multi-armed bandit problem framework in various contexts. The investigation highlights the prevalence of Thompson sampling in dynamic pricing scenarios with a Bayesian backdrop, where the seller possesses prior knowledge of demand functions. On the other hand, in non-Bayesian situations, the Upper Confidence Bound (UCB) algorithm family gains traction due to their favorable regret bounds. As markets often exhibit temporal fluctuations, the domain of non-stationary multi-armed bandits within dynamic pricing emerges as crucial. Future research directions include enhancing traditional multi-armed bandit algorithms to suit online learning settings, especially those involving dynamic reward distributions. Additionally, merging prior insights into demand functions with contextual multi-armed bandit approaches holds promise for advancing dynamic pricing strategies. In conclusion, this study sheds light on dynamic pricing through the lens of multi-armed bandit problems, offering insights and pathways for further exploration.

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Section: Stationary Vs Non-stationarymentioning

confidence: 96%

Section: Stationary Vs Non-stationarymentioning

confidence: 99%