scholarly journals Using Biased-Randomized Algorithms for the Multi-Period Product Display Problem with Dynamic Attractiveness

Algorithms ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 34
Author(s):  
Mage Marmol ◽  
Leandro do C. Martins ◽  
Sara Hatami ◽  
Angel A. Juan ◽  
Vicenc Fernandez
Keyword(s):  
Randomized Algorithms ◽  
Optimization Problem ◽  
Critical Issue ◽  
Linear Solvers ◽  
Sales Revenue ◽  
Shopping Experience ◽  
Brick And Mortar ◽  
Potential Customers ◽  
Efficient Selection ◽  
Selection Of

From brick-and-mortar stores to omnichannel retail, the efficient selection of products to be displayed on store tables, advertising brochures, or online front pages has become a critical issue. One possible goal is to maximize the overall ‘attractiveness’ level of the displayed items, i.e., to enhance the shopping experience of our potential customers as a way to increase sales and revenue. With the goal of maximizing the total attractiveness value for the visiting customers over a multi-period time horizon, this paper studies how to configure an assortment of products to be included in limited display spaces, either physical or online. In order to define a realistic scenario, several constraints are considered for each period and display table: (i) the inclusion of both expensive and non-expensive products on the display tables; (ii) the diversification of product collections; and (iii) the achievement of a minimum profit margin. Moreover, the attractiveness level of each product is assumed to be dynamic, i.e., it is reduced if the product has been displayed in a previous period (loss of novelty) and vice versa. This generates dependencies across periods. Likewise, correlations across items are also considered to account for complementary or substitute products. In the case of brick-and-mortar stores, for instance, solving this rich multi-period product display problem enables them to provide an exciting experience to their customers. As a consequence, an increase in sales revenue should be expected. In order to deal with the underlying optimization problem, which contains a quadratic objective function in its simplest version and a non-smooth one in its complete version, two biased-randomized metaheuristic algorithms are proposed. A set of new instances has been generated to test our approach and compare its performance with that of non-linear solvers.

Constrained Dual-Level Bandit for Personalized Impression Regulation in Online Ranking Systems

2021 ◽  
Vol 16 (2) ◽  
pp. 1-23
Author(s):  
Zhao Li ◽  
Junshuai Song ◽  
Zehong Hu ◽  
Zhen Wang ◽  
Jun Gao
Keyword(s):  
Optimization Problem ◽  
Initial Conditions ◽  
Global Scale ◽  
Global Constraints ◽  
Economic Losses ◽  
Second Order Cone ◽  
Ranking Systems ◽  
Item Level ◽  
Shopping Experience ◽  
Regulation Method

Impression regulation plays an important role in various online ranking systems, e.g. , e-commerce ranking systems always need to achieve local commercial demands on some pre-labeled target items like fresh item cultivation and fraudulent item counteracting while maximizing its global revenue. However, local impression regulation may cause “butterfly effects” on the global scale, e.g. , in e-commerce, the price preference fluctuation in initial conditions (overpriced or underpriced items) may create a significantly different outcome, thus affecting shopping experience and bringing economic losses to platforms. To prevent “butterfly effects”, some researchers define their regulation objectives with global constraints, by using contextual bandit at the page-level that requires all items on one page sharing the same regulation action, which fails to conduct impression regulation on individual items. To address this problem, in this article, we propose a personalized impression regulation method that can directly makes regulation decisions for each user-item pair. Specifically, we model the regulation problem as a C onstrained D ual-level B andit (CDB) problem, where the local regulation action and reward signals are at the item-level while the global effect constraint on the platform impression can be calculated at the page-level only. To handle the asynchronous signals, we first expand the page-level constraint to the item-level and then derive the policy updating as a second-order cone optimization problem. Our CDB approaches the optimal policy by iteratively solving the optimization problem. Experiments are performed on both offline and online datasets, and the results, theoretically and empirically, demonstrate CDB outperforms state-of-the-art algorithms.

scholarly journals Designing Strong Privacy Metrics Suites Using Evolutionary Optimization

2021 ◽  
Vol 24 (2) ◽  
pp. 1-35
Author(s):  
Isabel Wagner ◽  
Iryna Yevseyeva
Keyword(s):  
Optimization Problem ◽  
Evolutionary Optimization ◽  
Multiple Objectives ◽  
Shared Value ◽  
Vehicular Communications ◽  
Open Problems ◽  
Value Range ◽  
Genomic Privacy ◽  
Metaheuristic Optimization Algorithms ◽  
Selection Of

The ability to measure privacy accurately and consistently is key in the development of new privacy protections. However, recent studies have uncovered weaknesses in existing privacy metrics, as well as weaknesses caused by the use of only a single privacy metric. Metrics suites, or combinations of privacy metrics, are a promising mechanism to alleviate these weaknesses, if we can solve two open problems: which metrics should be combined and how. In this article, we tackle the first problem, i.e., the selection of metrics for strong metrics suites, by formulating it as a knapsack optimization problem with both single and multiple objectives. Because solving this problem exactly is difficult due to the large number of combinations and many qualities/objectives that need to be evaluated for each metrics suite, we apply 16 existing evolutionary and metaheuristic optimization algorithms. We solve the optimization problem for three privacy application domains: genomic privacy, graph privacy, and vehicular communications privacy. We find that the resulting metrics suites have better properties, i.e., higher monotonicity, diversity, evenness, and shared value range, than previously proposed metrics suites.

scholarly journals Network Analysis Based on Unique Spectral Features Enables an Efficient Selection of Genomically Diverse Operational Isolation Units

2021 ◽  
Vol 9 (2) ◽  
pp. 416
Author(s):  
Charles Dumolin ◽  
Charlotte Peeters ◽  
Evelien De Canck ◽  
Nico Boon ◽  
Peter Vandamme
Keyword(s):  
Network Analysis ◽  
Hierarchical Clustering ◽  
Mass Spectra ◽  
Spectral Features ◽  
Maldi Tof ◽  
Maldi Tof Mass Spectra ◽  
Diversity Studies ◽  
Technical Sample ◽  
Efficient Selection ◽  
Selection Of

Culturomics-based bacterial diversity studies benefit from the implementation of MALDI-TOF MS to remove genomically redundant isolates from isolate collections. We previously introduced SPeDE, a novel tool designed to dereplicate spectral datasets at an infraspecific level into operational isolation units (OIUs) based on unique spectral features. However, biological and technical variation may result in methodology-induced differences in MALDI-TOF mass spectra and hence provoke the detection of genomically redundant OIUs. In the present study, we used three datasets to analyze to which extent hierarchical clustering and network analysis allowed to eliminate redundant OIUs obtained through biological and technical sample variation and to describe the diversity within a set of spectra obtained from 134 unknown soil isolates. Overall, network analysis based on unique spectral features in MALDI-TOF mass spectra enabled a superior selection of genomically diverse OIUs compared to hierarchical clustering analysis and provided a better understanding of the inter-OIU relationships.

Efficient selection of feature sets possessing high coefficients of determination based on incremental determinations

2003 ◽  
Vol 83 (4) ◽  
pp. 695-712 ◽  
Author(s):  
Ronaldo F. Hashimoto ◽  
Edward.R. Dougherty ◽  
Marcel Brun ◽  
Zheng-Zheng Zhou ◽  
Michael L. Bittner ◽  
...  
Keyword(s):  
Feature Sets ◽  
Efficient Selection ◽  
Selection Of

General Method for Optimizing Composite Patch Repairs

2015 ◽  
Vol 1120-1121 ◽  
pp. 670-674
Author(s):  
Abdelmadjid Ait Yala ◽  
Abderrahmanne Akkouche
Keyword(s):  
Mechanical Properties ◽  
Stress Intensity Factor ◽  
Stress Intensity ◽  
Intensity Factor ◽  
Optimization Problem ◽  
Composite Patch ◽  
Matlab Program ◽  
Patch Repairs ◽  
Selection Of ◽  
General Method

The aim of this work is to define a general method for the optimization of composite patch repairing. Fracture mechanics theory shows that the stress intensity factor tends towards an asymptotic limit K∞.This limit is given by Rose’s formula and is a function of the thicknesses and mechanical properties of the cracked plate, the composite patch and the adhesive. The proposed approach consists in considering this limit as an objective function that needs to be minimized. In deed lowering this asymptote will reduce the values of the stress intensity factor hence optimize the repair. However to be effective this robust design must satisfy the stiffness ratio criteria. The resolution of this double objective optimization problem with Matlab program allowed us determine the appropriate geometric and mechanical properties that allow the optimum design; that is the selection of the adhesive, the patch and their respective thicknesses.

Online versus offline: preferred retail choice for premium brand purchase

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rituparna Basu ◽  
Neena Sondhi
Keyword(s):  
Predictive Model ◽  
Logit Analysis ◽  
Chi Square ◽  
Content Type ◽  
Sales Personnel ◽  
Study Results ◽  
Shopping Experience ◽  
Brick And Mortar ◽  
Chi Square Analysis ◽  
Retail Format

PurposeThis exploratory study aims to examine the prevalent triggers that motivate a premium brand purchase in an online vs offline retail format.Design/methodology/approachA binary logit analysis is used to build a predictive model to assess the likelihood of the premium brand consumer seeking an online or an offline platform. Demographic and usage-based profile of the two set of consumers is established through a chi-square analysis.FindingsThree hundred and forty six urban consumers of premium branded apparels residing in two Indian Metros were studied. A predictive model with 89.6% accuracy was validated for distinguishing premium brand buyers who shop at brick-and-mortar store or online platforms. Quality and finish were factors sought by the online buyer, whereas autotelic need, pleasurable shopping experience and social approval were important triggers for an in-store purchase.Research limitations/implicationsThe study posits divergent demographics and motivational drivers that led to an online vs offline purchase. Though interesting and directional, the study results need to be examined across geographies and categories for establishing the generalizability of the findings.Practical implicationsThe study findings indicate that premium brand manufacturers can devise an omni-channel strategy that is largely tilted toward the online platform, as the quality conscious and brand aware consumer is confident and thus open to an online purchase. The implication for the physical outlet on the other hand is to ensure exclusive store atmospherics and knowledgeable but non-intrusive sales personnel.Originality/valueThe study is unique as it successfully builds a predictive model to forecast online vs offline purchase decisions among urban millennials.

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