Improving operational efficiency of a semiconductor equipment manufacturing warehouse through effective utilization of Vertical Lift Modules

This thesis deals with improving the operational efficiency of automated part storage devices, in this case Vertical Lift Modules (VLM). This was accomplished by using dynamic slotting to maximize and maintain high material throughput, eliminating the need for periodic reslotting. Multiple VLMs can be used in parallel picking operations to improve material throughput. Common industry practice is to periodically reslot items once an unbalanced workload is obvious. This thesis investigates a method to avoid periodic reslotting by using incoming parts as a means to maintain a balanced workload amongst VLMs. Three different part allocation strategies are compared, namely Randomization, Snake and Order Grouping to determine their effectiveness and respective feasibility. The three strategies are then crafted into logical systems that could be used to strategically place received parts and eliminate the need for periodic reslotting. The Snake method was found to be the most well suited for this particular situation due to the small order sizes. This method provides a 35% savings in pick time, which is equivalent to approximately 733 hours annually.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2015

Author dc:creator

• Racca, Stephen Douglas

Advisor dc:contributor.advisor

• Stephen C. Graves.

• Mechanical Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/101335

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/101335