Solving Uncertain Online Shopping Problem With Discounts Using Robust Counterpart Methodology

Online Shopping is a phenomenon that is growing rapidly at this time and consumers are an important element in the buying and selling competition in the market and consumers who make a difffference in determining the profifits of the sellers. This research discusses the problem of online shopping using the Robust Optimization method. Robust Optimization Method is a process to get optimal results with an uncertainty. Based on the demand model to optimize the buying price, an Integer Linear Programming model with discount functions is built which will be converted into Robust Optimization. In this study also used a tool that is the Maple application in the numerical calculation process

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

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

A Systematic Review on Integer Multi-objective Adjustable Robust Counterpart Optimization Model Using Benders Decomposition

Multi-objective integer optimization model that contain uncertain parameter can be handled using the Adjustable Robust Counterpart (ARC) methodology with Polyhedral Uncertainty Set. The ARC method has two stages of completion, so completing the second stage can be assisted by the Benders Decomposition. This paper discusses the systematic review on this topic using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). PRISMA presents a database mining algorithm for previous articles and related topics sourced from Scopus, Science Direct, Dimensions, and Google Scholar. Four stages of the algorithm are used, namely Identification, Screening, Eligibility, and Included. In the Eligibility stage, 16 articles obtained and called Dataset 1, used for bibliometric mapping and evolutionary analysis. Moreover, in the Included stage, six final databases obtained and called Dataset 2, which was used to analyze research gaps and novelty. The analysis was carried out on two datasets, assisted by the output visualisation using RStudio software with the " bibliometrix" package, then we use the command 'biblioshiny()' to create a link to the “shiny web interface”. At the final stage of the article using six articles analysis, it is concluded that there is no research on the ARC multi-objective integer optimization model with Polyhedral Uncertainty Sets using the Benders Decomposition Method, which focuses on discussing the general model and its mathematical analysis. Moreover, this research topic is open and becomes the primary references for further research in connection

An AmI-Based Software Architecture Enabling Evolutionary Computation in Blended Commerce: The Shopping Plan Application

This work describes an approach to synergistically exploit ambient intelligence technologies, mobile devices, and evolutionary computation in order to support blended commerce or ubiquitous commerce scenarios. The work proposes a software architecture consisting of three main components: linked data for e-commerce, cloud-based services, and mobile apps. The three components implement a scenario where a shopping mall is presented as an intelligent environment in which customers use NFC capabilities of their smartphones in order to handle e-coupons produced, suggested, and consumed by the abovesaid environment. The main function of the intelligent environment is to help customers define shopping plans, which minimize the overall shopping cost by looking for best prices, discounts, and coupons. The paper proposes a genetic algorithm to find suboptimal solutions for the shopping plan problem in a highly dynamic context, where the final cost of a product for an individual customer is dependent on his previous purchases. In particular, the work provides details on the Shopping Plan software prototype and some experimentation results showing the overall performance of the genetic algorithm

Internet shopping optimization problem

A high number of Internet shops makes it difficult for a customer to review manually all the available offers and select optimal outlets for shopping. A partial solution to the problem is brought by price comparators which produce price rankings from collected offers. However, their possibilities are limited to a comparison of offers for a single product requested by the customer. The issue we investigate in this paper is a multiple-item multiple-shop optimization problem, in which total expenses of a customer to buy a given set of items should be minimized over all available offers. In this paper, the Internet Shopping Optimization Problem (ISOP) is defined in a formal way and a proof of its strong NP-hardness is provided. We also describe polynomial time algorithms for special cases of the problem