Effective storage location assignment model based on a genetic simulation annealing algorithm

2020 ◽  
Vol 19 (1) ◽  
pp. 9
Author(s):  
Yang Cao ◽  
Feng Li ◽  
Siqing You ◽  
Lei Chen ◽  
Xi Yang ◽  
...  
Keyword(s):  
Model Based ◽  
Assignment Model ◽  
Storage Location ◽  
Annealing Algorithm ◽  
Location Assignment ◽  
Genetic Simulation

Effective storage location assignment model based on a genetic simulation annealing algorithm

2020 ◽  
Vol 19 (1) ◽  
pp. 9
Author(s):  
Li Zhou ◽  
Xi Yang ◽  
Lei Chen ◽  
Siqing You ◽  
Feng Li ◽  
...  
Keyword(s):  
Model Based ◽  
Assignment Model ◽  
Storage Location ◽  
Annealing Algorithm ◽  
Location Assignment ◽  
Genetic Simulation

Constrained clustering method for class-based storage location assignment in warehouse

2016 ◽  
Vol 116 (4) ◽  
pp. 667-689 ◽  
Author(s):  
Chao-Lung Yang ◽  
Thi Phuong Quyen Nguyen
Keyword(s):  
Principal Component ◽  
Constrained Clustering ◽  
Clustering Method ◽  
Content Type ◽  
Assignment Model ◽  
Retrieval Efficiency ◽  
Storage Location ◽  
Component Loadings ◽  
Location Assignment

Purpose – Class-based storage has been studied extensively and proved to be an efficient storage policy. However, few literature addressed how to cluster stuck items for class-based storage. The purpose of this paper is to develop a constrained clustering method integrated with principal component analysis (PCA) to meet the need of clustering stored items with the consideration of practical storage constraints. Design/methodology/approach – In order to consider item characteristic and the associated storage restrictions, the must-link and cannot-link constraints were constructed to meet the storage requirement. The cube-per-order index (COI) which has been used for location assignment in class-based warehouse was analyzed by PCA. The proposed constrained clustering method utilizes the principal component loadings as item sub-group features to identify COI distribution of item sub-groups. The clustering results are then used for allocating storage by using the heuristic assignment model based on COI. Findings – The clustering result showed that the proposed method was able to provide better compactness among item clusters. The simulated result also shows the new location assignment by the proposed method was able to improve the retrieval efficiency by 33 percent. Practical implications – While number of items in warehouse is tremendously large, the human intervention on revealing storage constraints is going to be impossible. The developed method can be easily fit in to solve the problem no matter what the size of the data is. Originality/value – The case study demonstrated an example of practical location assignment problem with constraints. This paper also sheds a light on developing a data clustering method which can be directly applied on solving the practical data analysis issues.

scholarly journals Defining a storage-assignment strategy for precedence-constrained order picking

2021 ◽  
Vol 31 (2) ◽  
Author(s):  
Maria A. M. Trindade ◽  
Paulo S. A. Sousa ◽  
Maria R. A. Moreira
Keyword(s):  
Assignment Problem ◽  
Real Life ◽  
Order Picking ◽  
Quadratic Assignment ◽  
Assignment Model ◽  
Storage Assignment ◽  
Storage Location ◽  
Assignment Strategy ◽  
Location Assignment ◽  
The Creation

This paper proposes a zero-one quadratic assignment model for dealing with the storage location assignment problem when there are weight constraints. Our analysis shows that operations can be improved using our model. When comparing the strategy currently used in a real-life company with the designed model, we found that the new placement of products allowed a reduction of up to 22% on the picking distance. This saving is higher than that achieved with the creation of density zones, a procedure commonly used to deal with weight constraints, according to the literature.

A probabilistic Passenger-to-Train Assignment Model based on automated data

2017 ◽  
Vol 104 ◽  
pp. 522-542 ◽  
Author(s):  
Yiwen Zhu ◽  
Haris N. Koutsopoulos ◽  
Nigel H.M. Wilson
Keyword(s):  
Model Based ◽  
Assignment Model
Sign in / Sign up

Export Citation Format

Share Document