Internet shopping optimization problem

Błażewicz, Jacek; Kovalyov, Mikhail Y.; Musiał, Jędrzej; Urbański, A.; Wojciechowski, Adam

doi:10.2478/v10006-010-0028-0

Cited by 30 publications

(24 citation statements)

References 9 publications

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“…The Internet Shopping Optimization Problem where a client wants to purchase a shopping list with a set of online stores that offer them with a minimum final price was initially proposed in [7]. Formally it was defined as follows:…”

Section: Literature Reviewmentioning

confidence: 99%

“…As mentioned before, more competition means lower prices, but also means that the customer can become overwhelmed with a huge number of options that makes it very difficult for him to get the best possible deal. This problem was initially modelled as the Internet Shopping Optimization Problem by [7] which was inspired by the work of Wojciechowski and Musial [32]. Moreover, it is worth noticing the collaborative approach that a coalition of shoppers can implement as a strategy to buy at the lowest costs [23].…”

Section: Introductionmentioning

confidence: 99%

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Trustworthy Online Shopping with Price Impact

Musiał

López-Locés

2017

Foundations of Computing and Decision Sciences

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Abstract. Internet shopping is one of the main pillars of electronic commerce. According to the literature, the Internet Shopping Optimization Problem (ISOP) has been defined in order to optimize the global cost of online purchase, taking into account both the cost of products and shipping. In this study, it was decided to propose and analyze a very interesting, and really substantial, extension of the ISOP. Namely, trust factors were subjected to careful analysis from the customer point of view. The analysis is based on a specially prepared questionnaire, supplemented by the information from the literature and our own observations. Thus, it was possible to propose a definition of a new mathematical model of the problem, and to prove its affiliation to the class of strongly NP-hard problems. In addition, the heuristic algorithm is proposed, which can be used to solve the problem.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trustworthy Online Shopping with Price Impact

Musiał

López-Locés

2017

Foundations of Computing and Decision Sciences

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“…It is assumed that p ij = 1 if i 2 / M j . The problem is to find a sequence of disjoint selections (or carts) of products X = ( X 1 , …, X n ), which we call a cart sequence, such that X j µ M j , j = 1, …, n, It has been proved that ISOP belongs to the NP-hard problems family [3].…”

Section: Problem Definitionmentioning

confidence: 99%

“…For example, due to NP-hardness of the optimization problem, [9] designed a heuristic solution to optimize the shopping basket and evaluate it for the customer basket optimization problem to make it applicable for solving complex shopping cart optimization in on-line applications. Moreover, it is proven that the problem is not approximable in polynomial time [3]. The archetype of the presented problem was a webbased customer assistance system dedicated to pharmacy shopping that helps customers find shops in a geographically defined range where the entire shopping list could be realized at the best total price [10].…”

Section: Related Workmentioning

confidence: 99%

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Algorithms solving the Internet shopping optimization problem with price discounts

Musiał¹,

Pecero²,

López-Locés³

et al. 2016

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. The Internet shopping optimization problem arises when a customer aims to purchase a list of goods from a set of web-stores with a minimum total cost. This problem is NP-hard in the strong sense. We are interested in solving the Internet shopping optimization problem with additional delivery costs associated to the web-stores where the goods are bought. It is of interest to extend the model including price discounts of goods. The aim of this paper is to present a set of optimization algorithms to solve the problem. Our purpose is to find a compromise solution between computational time and results close to the optimum value. The performance of the set of algorithms is evaluated through simulations using real world data collected from 32 web-stores. The quality of the results provided by the set of algorithms is compared to the optimal solutions for small-size instances of the problem. The optimization algorithms are also evaluated regarding scalability when the size of the instances increases. The set of results revealed that the algorithms are able to compute good quality solutions close to the optimum in a reasonable time with very good scalability demonstrating their practicability.

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