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Carton Set Optimization in E‐commerce Warehouses: A Case Study
Author(s) -
Singh Manjeet,
Ardjmand Ehsan
Publication year - 2020
Publication title -
journal of business logistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.611
H-Index - 79
eISSN - 2158-1592
pISSN - 0735-3766
DOI - 10.1111/jbl.12255
Subject(s) - carton , set (abstract data type) , computer science , supply chain , footprint , genetic algorithm , selection (genetic algorithm) , crossover , operations research , operations management , business , mathematics , engineering , artificial intelligence , marketing , geography , machine learning , archaeology , programming language , waste management
In this study, a three‐stage methodology for carton set optimization in e‐commerce warehouses is proposed and evaluated on three DHL Supply Chain warehouses. The methodology includes order cubing, carton grouping, and optimal carton set selection. A modified largest area fits first algorithm for order cubing is proposed. For optimal carton set selection, a genetic algorithm with a novel crossover strategy is introduced. The results show that the proposed carton set optimization approach can improve the shipping cost and carton utilization by 7% and 7.8%, and considerably improve the carbon footprint of the operations, even when the number of carton types is not changed.