Learning with Posterior Sampling for Revenue Management under Time-varying Demand
Authors: Kazuma Shimizu, Junya Honda, Shinji Ito, Shinji Nakadai
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical study shows that the proposed algorithm performs better than other benchmark algorithms and comparably to the optimal policy in hindsight. We also propose a heuristic modification of the proposed algorithm, which further efficiently learns the pricing policy in the experiments. |
| Researcher Affiliation | Collaboration | 1NEC Corporation 2Kyoto University 3RIKEN AIP 4Intent Exchange, Inc. |
| Pseudocode | Yes | Algorithm 1: TS-episodic; Algorithm 2: TS-dynamic |
| Open Source Code | Yes | 2The code of the experiments is available at: https://github.com/NECDSresearch2007/RM-TSepisodic-and-dynamic. |
| Open Datasets | No | The paper uses simulated demand distributions based on specified parameters (e.g., Poisson distribution with mean parameters λ(t, p) = 50 exp(p+t/5)). It does not use a publicly available dataset or provide a link to a generated dataset. |
| Dataset Splits | No | The paper describes the number of episodes and independent trials for its simulations (e.g., S=5000 episodes, 100 trials) but does not specify traditional train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions using linear programming (LP) and 'off-the-shelf solvers' but does not specify any software names with version numbers (e.g., Python, specific LP solvers, or libraries). |
| Experiment Setup | Yes | Experimental Settings We consider the set of K = 9 prices P = {1, 2, . . . , 9} with a shut-off price p . The selling horizon is set to T = 10. The true demand distribution is set to Poisson distributions with mean demand parameters λ(t, p) = 50 exp p+t 5 , depending on the time t and price p. ... The initial inventory is set to n0 = 1000 and 50... Independent Gamma Prior: For Example 1 in Section 2.1, we set prior gamma distributions with shape α = 10 and scale β = 1 for all k [K] and t [T]. Gaussian Process (GP) Prior: For Example 2 in Section 2.1, we took the mean function µ as a zero function and the kernel function as an anisotropic radial basis function kernel defined as, K ((p, t), (p , t )) = exp (t t )2/σ2 t (p p )2/σ2 p where σt = 3 σp = 2.5. |