Multi-modal, multi-period, multi-commodity transportation : models and algorithms
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2014. === 33 === "June 2014." Cataloged from PDF version of thesis. === Includes bibliographical references (pages 51-54). === In this paper we present a mixed integer optimization...
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ndltd-MIT-oai-dspace.mit.edu-1721.1-913992019-05-02T15:53:49Z Multi-modal, multi-period, multi-commodity transportation : models and algorithms Jernigan, Nicholas R. (Nicholas Richard) Dimitris Bertsimas. Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center. Operations Research Center. Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2014. 33 "June 2014." Cataloged from PDF version of thesis. Includes bibliographical references (pages 51-54). In this paper we present a mixed integer optimization framework for modeling the shipment of goods between origin destination (O-D) pairs by vehicles of different types over a time-space network. The output of the model is an optimal schedule and routing of vehicle movements and assignment of goods to vehicles. Specifically, this framework allows for: multiple vehicles of differing characteristics (including speed, cost of travel, and capacity), transshipment locations where goods can be transferred between vehicles; and availability times for goods at their origins and delivery time windows for goods at their destinations. The model is composed of three stages: In the first, vehicle quantities, by type, and goods are allocated to routes in order to minimize late deliveries and vehicle movement costs. In the second stage, individual vehicles, specified by vehicle identification numbers, are assigned routes, and goods are assigned to those vehicles based on the results of the first stage and a minimization of costs involved with the transfer of goods between vehicles. In the third stage we reallocate the idle time of vehicles in order to satisfy crew rest constraints. Computational results show that provably optimal or near optimal solutions are possible for realistic instance sizes. by Nicholas R. Jernigan. S.M. 2014-11-04T21:33:51Z 2014-11-04T21:33:51Z 2014 Thesis http://hdl.handle.net/1721.1/91399 893482869 eng 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. http://dspace.mit.edu/handle/1721.1/7582 54 pages application/pdf Massachusetts Institute of Technology |
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Operations Research Center. Jernigan, Nicholas R. (Nicholas Richard) Multi-modal, multi-period, multi-commodity transportation : models and algorithms |
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Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2014. === 33 === "June 2014." Cataloged from PDF version of thesis. === Includes bibliographical references (pages 51-54). === In this paper we present a mixed integer optimization framework for modeling the shipment of goods between origin destination (O-D) pairs by vehicles of different types over a time-space network. The output of the model is an optimal schedule and routing of vehicle movements and assignment of goods to vehicles. Specifically, this framework allows for: multiple vehicles of differing characteristics (including speed, cost of travel, and capacity), transshipment locations where goods can be transferred between vehicles; and availability times for goods at their origins and delivery time windows for goods at their destinations. The model is composed of three stages: In the first, vehicle quantities, by type, and goods are allocated to routes in order to minimize late deliveries and vehicle movement costs. In the second stage, individual vehicles, specified by vehicle identification numbers, are assigned routes, and goods are assigned to those vehicles based on the results of the first stage and a minimization of costs involved with the transfer of goods between vehicles. In the third stage we reallocate the idle time of vehicles in order to satisfy crew rest constraints. Computational results show that provably optimal or near optimal solutions are possible for realistic instance sizes. === by Nicholas R. Jernigan. === S.M. |
author2 |
Dimitris Bertsimas. |
author_facet |
Dimitris Bertsimas. Jernigan, Nicholas R. (Nicholas Richard) |
author |
Jernigan, Nicholas R. (Nicholas Richard) |
author_sort |
Jernigan, Nicholas R. (Nicholas Richard) |
title |
Multi-modal, multi-period, multi-commodity transportation : models and algorithms |
title_short |
Multi-modal, multi-period, multi-commodity transportation : models and algorithms |
title_full |
Multi-modal, multi-period, multi-commodity transportation : models and algorithms |
title_fullStr |
Multi-modal, multi-period, multi-commodity transportation : models and algorithms |
title_full_unstemmed |
Multi-modal, multi-period, multi-commodity transportation : models and algorithms |
title_sort |
multi-modal, multi-period, multi-commodity transportation : models and algorithms |
publisher |
Massachusetts Institute of Technology |
publishDate |
2014 |
url |
http://hdl.handle.net/1721.1/91399 |
work_keys_str_mv |
AT jernigannicholasrnicholasrichard multimodalmultiperiodmulticommoditytransportationmodelsandalgorithms |
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1719030949542363136 |