Dynamic storage assignment with product affinity and ABC classification-a case study.

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Bibliographic Details
Title:	Dynamic storage assignment with product affinity and ABC classification-a case study.
Authors:	Li, Jiaxi¹ li313@purdue.edu, Moghaddam, Mohsen¹ mmoghadd@purdue.edu, Nof, Shimon¹ nof@purdue.edu
Source:	International Journal of Advanced Manufacturing Technology. Jun2016, Vol. 84 Issue 9-12, p2179-2194. 16p.
Subjects:	Dynamic storage allocation (Computer science), Production (Economic theory), Process optimization, Genetic algorithms, Polynomial time algorithms
Abstract:	This article defines a new dynamic storage assignment problem (DSAP) and develops an integrated mechanism for optimization purpose, based on the ABC classification and mutual affinity of products. A product affinity-based heuristic (PABH)-a technique based on data mining-is developed for calculation of pairwise relationships between products. It is shown that the analytical and multi-parametric DSAP is a quadratic assignment problem (QAP) and thus non-deterministic polynomial-time (NP)-hard. A greedy genetic algorithm (GA) is therefore developed for handling the computational complexity of the DSAP. Performance comparisons between the new approach and the traditional ABC classification method are conducted. The experimental results show that a preferred storage assignment approach is to simultaneously maximize the sum of affinity values and the product of zone indicators and order frequencies based on traditional ABC classification. The experiments on a distribution center of a family care product manufacturer indicate 7.14 to 104.48 % improvement in the average order picking time. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	This article defines a new dynamic storage assignment problem (DSAP) and develops an integrated mechanism for optimization purpose, based on the ABC classification and mutual affinity of products. A product affinity-based heuristic (PABH)-a technique based on data mining-is developed for calculation of pairwise relationships between products. It is shown that the analytical and multi-parametric DSAP is a quadratic assignment problem (QAP) and thus non-deterministic polynomial-time (NP)-hard. A greedy genetic algorithm (GA) is therefore developed for handling the computational complexity of the DSAP. Performance comparisons between the new approach and the traditional ABC classification method are conducted. The experimental results show that a preferred storage assignment approach is to simultaneously maximize the sum of affinity values and the product of zone indicators and order frequencies based on traditional ABC classification. The experiments on a distribution center of a family care product manufacturer indicate 7.14 to 104.48 % improvement in the average order picking time. [ABSTRACT FROM AUTHOR]
ISSN:	02683768
DOI:	10.1007/s00170-015-7806-7