Improved packing strategy for distribution centers to reduce freight cost

In this thesis, we designed and implemented a data-driven packing strategy for distribution center outbound packing activities to reduce freight cost and carbon footprint. The strategy consists of two parts. First, I proposed an Carton Combination method, an algorithm that can select any predetermined number of distinct cartons from a large carton pools (over 1000 options) to be used for outbound shipment packing such that the annual total wasted air content inside the shipment is minimized. Second, I proposed an Carton Selection algorithm, which can determine the best carton, from the carton options chosen by the Carton Combination method, for an incoming or- der with known dimensions. The entire packing strategy prototype was implemented by MATLAB R2019a; the prototype was tested with the 2018 outbound shipment data from Waters Corporation Global Distribution Center (GDC) and the simulation showed that the annual averaged shipment air percentage was reduced from 60% to 40%, which projects to an annual operation cost saving of 83,000 USD and carbon dioxide emission reduction of 20 ton. The data-driven packing strategy has a potential to be scaled up and implemented via an industrial environment such as SAP ABAP.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Zeng, Bowen,M. Eng.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Stephen C. Graves.

• Mechanical Engineering.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123714

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123714