Optimal product design using a colony of virtual ants

Albritton, M. David; McMullen, Patrick R.

doi:10.1016/j.ejor.2005.06.042

Cited by 67 publications

(27 citation statements)

References 29 publications

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“…E.g. if r1= (2,3), r2= (3,5), r3= (4,7), then E(ai)={2, 3,4,5,7} and I(ai)={ [2,3], [3,4], [4,5], [5,7]}. Note that |E(ai)| ≤ 2×|Q| and |I(ai)| ≤ 2×|Q|-1, where |Q| is the number of queries in Q.…”

Section: Numeric Sigtreemtd Algorithmmentioning

confidence: 99%

Smallest Set for Reverse Selection Queries to Satisfy All

Miah¹

2017

IJSEA

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Abstract:In recent years, research has moved from traditional query processing (e.g., Selection, Nearest Neighbor (NN), Top-k, Skyline), to reverse query processing (e.g., Reverse NN, Reverse Top-k, Reverse Skyline), to maximal reverse query processing (e.g., find spatial points that maximize the number of Reverse NNs), and so on. This paper considers the Smallest Set Reverse Selection Queries Problem also known as the Multiple Tuple Design Problem: Given a set of selection queries with conjunctive conditions, where the task is to create the smallest set of tuples such that each query returns at least one of these tuples. The problem is an interesting variant of the Maximal Reverse Selection Queries Problem (also referred to as the Tuple Design Problem) introduced by Miah et al. (2016). The paper shows that the problem is NP-Complete and develops approximation algorithms with provable approximation guarantees, as well as carefully designed heuristics that work well in practice. The paper also designs efficient exact algorithm that are feasible for moderate instances. It provides extensive experiments that demonstrate the effectiveness of the proposed algorithms.

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Section: Numeric Sigtreemtd Algorithmmentioning

confidence: 99%

Smallest Set for Reverse Selection Queries to Satisfy All

Miah¹

2017

IJSEA

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“…Product design is determination of the best mix of characteristics for a product or service offering. The approaches adopted address consumer decision-making, including preference, attributes, and choice, as well as company-related costs and profit estimates (Albritton and McMullen 2007). Product-line selection and product positioning models focus on selecting and pricing product variants for markets (Krishnan and Ulrich 2001).…”

Section: Product Design and Optimizationmentioning

confidence: 99%

Computational Support for Functionality Selection in Interaction Design

Oulasvirta

Feit

Lähteenlahti

et al. 2017

ACM Trans. Comput.-Hum. Interact.

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Designing interactive technology entails several objectives, one of which is identifying and selecting appropriate functionality. Given candidate functionalities such as "print," "bookmark," and "share," a designer has to choose which functionalities to include and which to leave out. Such choices critically affect the acceptability, productivity, usability, and experience of the design. However, designers may overlook reasonable designs because there is an exponential number of functionality sets and multiple factors to consider. This article is the first to formally define this problem and propose an algorithmic method to support designers to explore alternative functionality sets in early stage design. Based on interviews of professional designers, we mathematically define the task of identifying functionality sets that strike the best balance among four objectives: usefulness, satisfaction, ease of use, and profitability. We develop an integer linear programming solution that can efficiently solve very large instances (set size over 1,300) on a regular computer. Further, we build on techniques of robust optimization to search for diverse and surprising functionality designs. Empirical results from a controlled study and field deployment are encouraging. Most designers rated computationally created sets to be of the comparable or superior quality than their own. Designers reported gaining better understanding of available functionalities and the design space.

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“…Shocker et al [14] first represented products and consumer preferences as points in a joint attribute space. Later, several techniques [1,2] were developed to design/position a new item. Work in this domain requires direct involvement of consumers, who choose preferences from a set of existing alternative products.…”

Section: Related Workmentioning

confidence: 99%

“…Next, consider a z ′ -dimensional cube with each side of length L = O(n m ). We partition the cube into z ′ -dimensional cells as follows: Along each axis, start with the furthest value L, and then proceed towards the origin by marking the points L/(1 + σ), L/(1 + σ) 2 , and so on. The number of points marked along each axis is log (1+σ) L = O(m log (1+σ) n) which is a polynomial in m and n. Then at each marked point we pass (z ′ − 1)-dimensional hyperplanes perpendicular to the corresponding axis.…”

Section: Proof Of Part 2 (Sketch)mentioning

confidence: 99%

Leveraging collaborative tagging for web item design

Das

Hristidis

2011

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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The popularity of collaborative tagging sites has created new challenges and opportunities for designers of web items, such as electronics products, travel itineraries, popular blogs, etc. An increasing number of people are turning to online reviews and user-specified tags to choose from among competing items. This creates an opportunity for designers to build items that are likely to attract desirable tags when published. In this paper, we consider a novel optimization problem: given a training dataset of existing items with their user-submitted tags, and a query set of desirable tags, design the k best new items expected to attract the maximum number of desirable tags. We show that this problem is NPComplete, even if simple Naive Bayes Classifiers are used for tag prediction. We present two principled algorithms for solving this problem: (a) an exact "two-tier" algorithm (based on top-k querying techniques), which performs much better than the naive brute-force algorithm and works well for moderate problem instances, and (b) a novel polynomialtime approximation algorithm with provable error bound for larger problem instances. We conduct detailed experiments on synthetic and real data crawled from the web to evaluate the efficiency and quality of our proposed algorithms.

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Optimal product design using a colony of virtual ants

Cited by 67 publications

References 29 publications

Smallest Set for Reverse Selection Queries to Satisfy All

Smallest Set for Reverse Selection Queries to Satisfy All

Computational Support for Functionality Selection in Interaction Design

Leveraging collaborative tagging for web item design

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