Abstract
Most large distribution centers’ order picking processes are highly labor-intensive. Increasing the efficiency of order picking allows these facilities to move higher volumes of products. The application of data mining in distribution centers has the capability of generating efficiency improvements, mainly if these techniques are used to analyze the large amount of data generated by orders received by distribution centers and determine correlations in ordering patterns. This paper proposes a heuristic method to optimize the order picking distance based on frequent itemset grouping and nonuniform product weights. The proposed heuristic uses association rule mining (ARM) to create families of products based on the similarities between the stock keeping units (SKUs). SKUs with higher similarities are located near the rest of the members of the family. This heuristic is applied to a numerical case using data obtained from a real distribution center in the food retail industry. The experiment results show that data mining-driven developed layouts can reduce the traveling distance required to pick orders.
Highlights
Distribution centers (DC) perform many essential functions, such as receiving, putting away, cross-docking, order-picking, sorting and shipping hundreds of products [1]
Order pickers travel around the DC to collect stock keeping units (SKUs) from their slots
Assignment to picking slots—a decision problem well known as SLAP—attempts to determine the best location of the SKU to optimize the order picking travel distance
Summary
Distribution centers (DC) perform many essential functions, such as receiving, putting away, cross-docking, order-picking, sorting and shipping hundreds of products [1]. Minimizing the order picker’s travel distance is a crucial decision problem that has received considerable attention from researchers and practitioners [2,3,4], which includes optimizing picking routes, zoning, storage location assignment and order batching. The detailed literature reviews in [2,3,4] discuss several types of storage assignment policies, including random storage, closest open location storage, dedicated storage, full turnover storage, class-based storage and family grouping From these policies, only family grouping considers the relationship between the SKUs. Family groupings are defined in [2] as a set of items, referred to in this paper as “itemsets”, frequently found in the same order. The focus of this paper is on the sequencing of SKUs based on data mining-generated groupings
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