Gaussian processes for unconstraining demand

Price, Ilan; Fowkes, Jaroslav; Hopman, Daniel

doi:10.1016/j.ejor.2018.11.065

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…The case study only considers knowing the target labels in time due to limitations in the data; studies also satisfying the requirement for input features would help validate the findings. Although this paper focuses on uncertainty for NN classifiers, uncertainty can also be quantified for NN regression models and other ML models such as Gaussian Processes (Price et al, 2019) and Random Forests (Shaker & Hüllermeier, 2020). Finally, uncertainty as XAI can be used in active learning, where limited labeled training data is available and the ML system can ask a human expert to label the most uncertain observations (Kadziński & Ciomek, 2021).…”

Section: Discussionmentioning

confidence: 99%

Explainability through uncertainty: Trustworthy decision-making with neural networks

Thuy,

Benoit

2024

European Journal of Operational Research

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Section: Discussionmentioning

confidence: 99%

Explainability through uncertainty: Trustworthy decision-making with neural networks

Thuy,

Benoit

2024

European Journal of Operational Research

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“…In addition, they analyze the impact of the reference price on the gained revenue. Price et al (2019) use a Gaussian Process methodology to track and estimate the dynamic changes in demand, taking into consideration the necessity to unconstrain the demand (estimating the true demand in case inventory is assumed unlimited from finite inventory data). The Gaussian Process is a machine learning/statistical approach that models data as a joint multivariate Gaussian (Atiya et al 2020).…”

Section: Finite Inventorymentioning

confidence: 99%

Novel pricing strategies for revenue maximization and demand learning using an exploration–exploitation framework

2021

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The price demand relation is a fundamental concept that models how price affects the sale of a product. It is critical to have an accurate estimate of its parameters, as it will impact the company's revenue. The learning has to be performed very efficiently using a small window of a few test points, because of the rapid changes in price demand parameters due to seasonality and fluctuations. However, there are conflicting goals when seeking the two objectives of revenue maximization and demand learning, known as the learn/earn trade-off. This is akin to the exploration/exploitation trade-off that we encounter in machine learning and optimization algorithms. In this paper, we consider the problem of price demand function estimation, taking into account its exploration-exploitation characteristic. We design a new objective function that combines both aspects. This objective function is essentially the revenue minus a term that measures the error in parameter estimates. Recursive algorithms that optimize this objective function are derived. The proposed method outperforms other existing approaches.

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“…Weatherford provided two reviews of unconstrained estimation and forecast methods broadly used in different industries up to 2016 [18,19]. Recent discussions include forecast multipliers and hybrid forecasting [9], the effect of customers' reference price on demand [20], Gaussian processes for unconstraining demand [21], and demand forecast accuracy [22]. Due to the important research and application value of revenue management theory, many advances have been made in the hotel, car or truck rental, cargo/freight, internet service and retailing, cruise line, rail, and other industries regarding demand estimation and forecasting [19].…”

Section: Introductionmentioning

confidence: 99%

Unconstrained Estimation of Multitype Car Rental Demand

et al. 2021

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The unconstrained demand forecast for car rentals has become a difficult problem for revenue management due to the need to cope with a variety of rental vehicles, the strong subjective desires and requests of customers, and the high probability of upgrading and downgrading circumstances. The unconstrained demand forecast mainly includes repairing of constrained historical demand and forecasting of future demand. In this work, a new methodology is developed based on multiple discrete choice models to obtain customer choice preference probabilities and improve a known spill model, including a repair process of the unconstrained demand. In addition, the linear Holt–Winters model and the nonlinear backpropagation neural network are combined to predict future demand and avoid excessive errors caused by a single method. In a case study, we take advantage of a stated preference and a revealed preference survey and use the variable precision rough set to obtain factors and weights that affect customer choices. In this case study and based on a numerical example, three forecasting methods are compared to determine the car rental demand of the next time cycle. The comparison with real demand verifies the feasibility and effectiveness of the hybrid forecasting model with a resulting average error of only 3.06%.

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Gaussian processes for unconstraining demand

Cited by 5 publications

References 32 publications

Explainability through uncertainty: Trustworthy decision-making with neural networks

Explainability through uncertainty: Trustworthy decision-making with neural networks

Novel pricing strategies for revenue maximization and demand learning using an exploration–exploitation framework

Unconstrained Estimation of Multitype Car Rental Demand

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