The Project

Internet Shopping Optimization Project (IShOP) is an INTER POLLUX project, cofunded by Luxembourg National Research Funds (FNR) and the Polish National Research Centre for Research and Development (NCBiR).

This project is a collaboration between the Laboratory of Algorithm Design and Programming Systems of the Institute of Computing Science, Poznan University of Technology, Poland, and the Interdisciplinary Center of Security, Reliability and Trust (SnT) of the University of Luxembourg, Luxembourg.

The Aim

This project proposes innovative and realistic models for different typical online shopping operations, supported by strong mathematical and operational research fundamentals, and well balanced with lightweight computational algorithms. These models are designed in order to allow the optimization of such transactions. Finding accurate solutions to the defined problems implies both lowering customer expenses and favouring market competitiveness.

One of the main aims of this project is to model and formulate new advanced and realistic flavours of the Internet Shopping Optimization Problem (ISOP), considering discounts and additional conditions like price sensitive shipping costs, incomplete offers from shops, or the minimization of the total realization time, price, and delivery time functions, among others. The models will be mathematically and theoretically well founded. Moreover, the challenge of defining and addressing a multi-criteria version of the problem will be addressed too. Other important contributions will be the mapping of ISOP to other new challenges. One of them is the design of a novel business model for cloud brokering that will benefit both cloud providers and consumers. Providers will be able to easily offer their large number of services, and to get a fast answer from the market to offers (e.g., when infrastructure is under-utilized). Additionally, customers will easily benefit from offers and find the most appropriate deals for his/her needs (according to service level agreements, pricing, performance, etc.). Modelling some of these aspects and coupling it with an optimization tool for the brokering of cloud services among various providers would be a key contribution to the field.

A wide set of optimization algorithms will be designed and developed for the addressed problems. They include from fast lightweight specialized heuristics to highly accurate parallel and multi-objective population-based metaheuristics. They all will be embedded in a software framework for their practical applications, and validation.