Complexity Constraint in the Distributor’s Pallet Loading Problem

This paper presents a study on the complexity of cargo arrangements in the pallet loading problem. Due to the diversity of perspectives that have been presented in the literature, complexity is one of the least studied practical constraints. In this work, we aim to refine and propose a new set of metrics to measure the complexity of an arrangement of cargo in a pallet. The parameters are validated using statistical methods, such as principal component analysis and multiple linear regression, using data retrieved from the company logistics. Our tests show that the number of boxes was the main variable responsible for explaining complexity in the pallet loading problem.

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The Research of Wine Quality Evaluation Based on Multiple Linear Regression

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.2489 ◽

2013 ◽

Vol 756-759 ◽

pp. 2489-2493

Author(s):

Huai Hui Liu ◽

Wen Long Ji ◽

Peng Zhang ◽

Chuan Wen Yao

Keyword(s):

Principal Component Analysis ◽

Linear Regression ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Quality Evaluation ◽

Evaluation Model ◽

Multiple Linear Regression Model ◽

Principal Component ◽

Wine Grape ◽

Wine Quality

Through the establishment of evaluation model based on principal component analysis, select 8 principal components from nearly 30 indexes of wine grape. Then we establish the multiple linear regression model and analyse the association between physicochemical indexes of wine grape and wine, and the influence of physicochemical indexes of wine grape and wine on wine quality. Finally study whether we could use the physicochemical indexes to evaluate the wine quality.

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Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis

Journal of Information Processing ◽

10.2197/ipsjjip.26.170 ◽

2018 ◽

Vol 26 (0) ◽

pp. 170-176 ◽

Cited By ~ 4

Author(s):

Stephen J.H. Yang ◽

Owen H.T. Lu ◽

Anna Y.Q. Huang ◽

Jeff C.H. Huang ◽

Hiroaki Ogata ◽

...

Keyword(s):

Principal Component Analysis ◽

Academic Performance ◽

Linear Regression ◽

Multiple Linear Regression ◽

Principal Component ◽

Component Analysis

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Relationship between the Physical Properties and Hand of Jean Fabric

Autex Research Journal ◽

10.1515/aut-2015-0043 ◽

2016 ◽

Vol 16 (3) ◽

pp. 138-145 ◽

Cited By ~ 2

Author(s):

Atsushi Kawamura ◽

Chunhong Zhu ◽

Julie Peiffer ◽

KyoungOk Kim ◽

Yi Li ◽

...

Keyword(s):

Principal Component Analysis ◽

Linear Regression ◽

Physical Properties ◽

Multiple Linear Regression ◽

Principal Components ◽

Evaluation System ◽

Principal Component ◽

Semantic Differential ◽

Differential Method ◽

Fabric Hand

Abstract We investigated the distinctive characteristics of jean fabrics (denim fabrics obtained from jeans) and compared the physical properties and the hand. We used 13 kinds of jean fabric from commercial jeans and 26 other fabric types. The physical properties were measured using the Kawabata evaluation system, and the fabric hand was evaluated by 20 subjects using a semantic differential method. To characterise the hand of jean fabrics compared with other fabrics, we used principal component analysis and obtained three principal components. We found that jean fabrics were characterised by the second principal component, which was affected by feelings of thickness and weight. We further characterised the jean fabrics according to ‘softness & smoothness’ and ‘non-fullness’, depending on country of origin and type of manufacturer. The three principal components were analysed using multiple linear regression to characterise the components according to the physical properties. We explained the hand of fabrics including jean fabrics using its association with physical properties.

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