Optimization Models and Algorithms for Pricing in e-Commerce
| dc.contributor.advisor | Hassini, Elkafi | |
| dc.contributor.author | Shams-Shoaaee, Seyed Shervin | |
| dc.contributor.department | Computational Engineering and Science | en_US |
| dc.date.accessioned | 2020-09-23T20:06:11Z | |
| dc.date.available | 2020-09-23T20:06:11Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | With the rise of online retailer giants like Amazon, and enhancements in internet and mobile technologies, online shopping is becoming increasingly popular. This has lead to new opportunities in online price optimization. The overarching motivation and theme of this thesis is to review these opportunities and provide methods and models in the context of retailers' online pricing decisions. In Chapter 2 a multi-period revenue maximization and pricing optimization problem in the presence of reference prices is formulated as a mixed integer nonlinear program. Two algorithms are developed to solve the optimization problem: a generalized Benders' decomposition algorithm and a myopic heuristic. This is followed by numerical computations to illustrate the effciency of the solution approaches as well as some managerial pricing insights. In Chapter 3 a data-driven quadratic programming optimization model for online pricing in the presence of customer ratings is proposed. A new demand function is developed for a multi-product, nite horizon, online retail environment. To solve the optimization problem, a myopic pricing heuristic as well as exact solution approaches are introduced. Using customer reviews ratings data from Amazon.com, a new customer rating forecasting model is validated. This is followed by several analytical and numerical insights. In Chapter 4 a multinomial choice model is used for customer purchase decision to find optimal personalized price discounts for an online retailer that incorporates customer locations and feedback from their reviews. Closed form solutions are derived for two special cases of this problem. To gain some analytical insights extensive numerical experiments are carried followed by several analytical and numerical insights. | en_US |
| dc.description.degree | Doctor of Philosophy (PhD) | en_US |
| dc.description.degreetype | Thesis | en_US |
| dc.description.layabstract | The increase in online retail and the improvements in mobile technologies has lead to advantages and opportunities for both customers and retailers. One of these advantages is the ability to keep and efficiently access records of historical orders for both customers and retailers. In addition, online retailing has dramatically decreased the cost of price adjustments and discounts compared to the brick and mortar environment. At the same time, with the increase in online retailing we are witnessing proliferations of online reviews in e-commerce platforms. Given this availability of data and the new capabilities in an online retail environment, there is a need to develop pricing optimization models that integrate all these new features. The overarching motivation and theme of this thesis is to review these opportunities and provide methods and models in the context of retailers' online pricing decisions. | en_US |
| dc.identifier.uri | http://hdl.handle.net/11375/25818 | |
| dc.language.iso | en | en_US |
| dc.subject | Pricing | en_US |
| dc.subject | Reference pricing | en_US |
| dc.subject | Mixed-integer nonlinear programming | en_US |
| dc.subject | Generalized Benders' decomposition | en_US |
| dc.subject | Customer rating | en_US |
| dc.subject | e-commerce | en_US |
| dc.subject | Quadratic programming | en_US |
| dc.subject | Personalized price discounts | en_US |
| dc.subject | Nonlinear programming | en_US |
| dc.title | Optimization Models and Algorithms for Pricing in e-Commerce | en_US |
| dc.type | Thesis | en_US |