A data-driven approach to grocery store block layout

Özgörmüş, Elif; Smith, Alice E.

doi:10.1016/j.cie.2018.12.009

Cited by 36 publications

(12 citation statements)

References 39 publications

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“…In the last three years according to the digital transformation and big-data, machine learning [4,17,26,14,55,8,16,31] based data driven methodologies are highly used.…”

Section: 31mentioning

confidence: 99%

“…On the other hand, [26] used Gaussian Mixture Model (GMM) with K-mixture components using an Expectation-Maximization (EM) algorithm, Principal Component Analysis (PCA), Ordinary Least Squares (OLS) regression. K-Means Clustering [10], Spatial Clustering-Based Initialization [16], Cluster Analysis [33,52], association rule mining [31] are the other machine learning methodologies.…”

Section: 31mentioning

confidence: 99%

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A bibliometric and social network analysis of data-driven heuristic methods for logistics problems

Deniz

Özceylan

2023

JIMO

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<p style='text-indent:20px;'>Transport and logistics systems include a range of activities that deal with all sorts of decisions and operations from material handling to vehicle routing. One of the main challenges for transport and logistics processes is to deal with large-scale and complex problems. However, with increasingly diverse sets of operational real-world data becoming available, data-driven heuristic approaches are promising to pave the path for solving the problems in the field of transport and logistics. Thus, a comprehensive review is needed to observe the reflections of this path in literature. To bridge this gap, a total of 40 papers on the topic of "data-driven heuristic approaches to logistics and transportation problems" are determined. Before the categorization and content analysis; descriptive, bibliometric and social network analysis are carried out to identify the current state of the literature. All the papers are systemically reviewed based on different perspectives, namely data-driven methodology, heuristics, sub-problems and etc. Based on the review, suggestions for future research are likewise provided. Subsequently, machine learning and deep learning methods are considered to be among the most promising data-driven methodologies. The review may be useful for academicians, researchers, and practitioners for a better understanding of data-driven heuristic approaches to transportation and logistics problems.</p>

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“…In the last three years according to the digital transformation and big-data, machine learning [4,17,26,14,55,8,16,31] based data driven methodologies are highly used.…”

Section: 31mentioning

confidence: 99%

Section: 31mentioning

confidence: 99%

A bibliometric and social network analysis of data-driven heuristic methods for logistics problems

Deniz

Özceylan

2023

JIMO

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“…Association rules have been successfully used in product classification decision [2] , profit mining [3] [4] , credit card business [5] , market analysis [6] and other fields. They are also used in the field of layout [7] [8].…”

Section: Relevancementioning

confidence: 99%

Steel logistics park layout based on market basket analysis and accessibility

Yang

Lü

2021

MATEC Web Conf.

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In this study, we used market basket analysis and land accessibility for the layout of MG Steel Logistics Park. Due to the unreasonable cargo space layout, the outbound trucks in the MG Steel Logistics Park detour and transfer, which caused congestion. Using market basket analysis, this research took outbound trucks as the research object to measure the degree of correlation between cargo locations. Then, we measured the convenience of cargo locations by the accessibility and matched it with the steel outbound frequency. We established a model and got the layout allocation plan with the maximum degree of relevance and the minimum difference in cargo-location matching as the target. In the solution algorithm, we use roulette method and the elite retention strategy to improve the genetic algorithm. Finally, we compared the results before and after optimization. We found that the number of truck transfers was reduced by 33%, and the truck staying time was reduced by 21%.

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“…The correlations, frequent patterns, association, or causal structure among sets of items are commonly expressed in the form of association rules, which were introduced by Agrawal et al (1993). Besides book recommendation, association rules techniques have been extensively studied in many other domains such as store layout (Altuntas, 2017;Ozgormus and Smith, 2018;Sagin and Ayvaz, 2018), medical health (Castro et al, 2018;Yang et al, 2019), and…”

Section: Introductionmentioning

confidence: 99%

A Markov logic network method for reconstructing association rule-mining tasks in library book recommendation

Wang

Feng

et al. 2021

IDD

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Purpose This study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in mining. Second, this project can effectively solve the problem of four types of rules being present in the database at the same time. The traditional association algorithm can only mine one or two types of rules and cannot fully explore the database knowledge in the decision-making process for library recommendation. Design/methodology/approach The authors proposed a Markov logic network method to reconstruct association rule-mining tasks for library recommendation and compared the method proposed in this paper to traditional Apriori, FP-Growth, Inverse, Sporadic and UserBasedCF algorithms on two history library data sets and the Chess and Accident data sets. Findings The method used in this project had two major advantages. First, the authors were able to mine four types of rules in an integrated manner without having to set interest measures. In addition, because it represents the relevance of mining in the network, decision-makers can use network visualization tools to fully understand the results of mining in library recommendation and data sets from other fields. Research limitations/implications The time cost of the project is still high for large data sets. The authors will solve this problem by mapping books, items, or attributes to higher granularity to reduce the computational complexity in the future. Originality/value The authors believed that knowledge of complex real-world problems can be well captured from a network perspective. This study can help researchers to avoid setting interest metrics and to comprehensively extract frequent, rare, positive, and negative rules in an integrated manner.

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A data-driven approach to grocery store block layout

Cited by 36 publications

References 39 publications

A bibliometric and social network analysis of data-driven heuristic methods for logistics problems

A bibliometric and social network analysis of data-driven heuristic methods for logistics problems

Steel logistics park layout based on market basket analysis and accessibility

A Markov logic network method for reconstructing association rule-mining tasks in library book recommendation

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