Real-time order acceptance in transportation under uncertainty

Without using any order acceptance criteria, retail companies distributing products with private transportation fleets are not able to maximize their profits because they are not adequately utilizing their capacity. The objective of this paper was to create and validate a model to determine if historical demand data can be used by retail firms operating private fleets to make effective real-time order acceptance/rejection decisions with the purpose of eliminating unprofitable orders in a short-haul transportation setting. A Java tool was generated to instantaneously decide whether or not to accept an order depending on the order location and time of receipt. The model was tested against optimal decisions using total demand knowledge and several alternative real-time decision-making strategies. The model was found to significantly outperform the alternative real-time decision making strategies and provided profits approximately eight percent lower than the optimal decisions. We conclude that using historical demand probabilities is useful in informing the decisions of retail firms seeking to utilize private fleets efficiently and increase profitability through cost reduction.

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering Systems Division.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2014

Authors dc:creator

• Nisar, Hiral (Hiral Dhiraj)
• Rosenzweig, Joshua

Advisor dc:contributor.advisor

• Chris Caplice.

• Engineering Systems Division.

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/1721.1/92119

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/92119