Robust Optimization Model for Internet Shopping Online Problems with Endorsement Costs in the Fashion Industry

Abstract

Online business is a business activity carried out via the internet or digitally. Buying, selling, and advertising are done online through e-commerce, social media, or online shops. The products offered vary, including services, food, household needs, and fashion. Selling online is not limited by time and distance, and consumers can obtain information about products and services that can influence their decisions. At the same time, sellers also have the opportunity to advertise their products in a broader range by making endorsements. An endorsement is a form of advertising using well-known figures who are recognized, trusted, and respected by people. In this thesis, a model for optimizing the problem of online internet shopping with endorsement fees is formulated. This optimization model aims to maximize the profits gained by sellers in marketing their products online. In marketing products, there is uncertainty in the number of requests. To overcome this uncertainty, an approach is needed that can handle this uncertainty, namely Robust optimization. The Robust optimization model is solved using the polyhedral uncertainty set approach, resulting in a computationally tractable optimal solution.

Keywords: internet shopping online; endorsement costs; robust optimization.