Assortment Optimization Under the Mallows model
Authors: Antoine Desir, Vineet Goyal, Srikanth Jagabathula, Danny Segev
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using a simulation study, we show that the MIP provides accurate assortment decisions in a reasonable amount of time for practical problem sizes. ... In this section, we examine how the MIP performs in terms of the running time. We considered the following simulation setup. ... Table 1 shows the running time of the strengthened MIP formulation for different values of e and n. |
| Researcher Affiliation | Academia | Antoine Désir IEOR Department Columbia University antoine@ieor.columbia.edu Vineet Goyal IEOR Department Columbia University vgoyal@ieor.columbia.edu Srikanth Jagabathula IOMS Department NYU Stern School of Business sjagabat@stern.nyu.edu Danny Segev Department of Statistics University of Haifa segevd@stat.haifa.ac.il |
| Pseudocode | Yes | Algorithm 1 Computing choice probabilities |
| Open Source Code | No | The paper does not include any statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper describes generating synthetic data for its experiments: 'Product prices are sampled independently and uniformly at random from the interval [0, 1]. The modal ranking is fixed to the identity ranking with the outside option ranked at the top.' It does not refer to or provide access to a publicly available dataset. |
| Dataset Splits | No | The paper describes a simulation setup for generating data but does not explicitly mention training, validation, or test dataset splits. |
| Hardware Specification | Yes | We solved the MIPs using the Gurobi Optimizer version 6.0.0 on a computer with processor 2.4GHz Intel Core i5, RAM of 8GB, and operating system Mac OSX El Capitan. |
| Software Dependencies | Yes | We solved the MIPs using the Gurobi Optimizer version 6.0.0 |
| Experiment Setup | Yes | We considered the following simulation setup. Product prices are sampled independently and uniformly at random from the interval [0, 1]. The modal ranking is fixed to the identity ranking with the outside option ranked at the top. ... For each pair of parameters, we generated 50 different instances. |