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Mathematical Programming Models and ...

Novoa, Luis J.

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Mathematical Programming Models and Algorithms for Large-Scale Problems in Transportation and Logistics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mathematical Programming Models and Algorithms for Large-Scale Problems in Transportation and Logistics./
Author:	Novoa, Luis J.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	155 p.
Notes:	Source: Dissertations Abstracts International, Volume: 78-03, Section: A.
Contained By:	Dissertations Abstracts International78-03A.
Subject:	Transportation planning. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10142751
ISBN:	9781339980171

Mathematical Programming Models and Algorithms for Large-Scale Problems in Transportation and Logistics.
Novoa, Luis J.

Mathematical Programming Models and Algorithms for Large-Scale Problems in Transportation and Logistics. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 155 p.

Source: Dissertations Abstracts International, Volume: 78-03, Section: A.

Thesis (Ph.D.)--The George Washington University, 2016.

This item is not available from ProQuest Dissertations & Theses.

The research effort presented in this document focuses on the application of optimization methods to solve two real world problems in the logistics and transportation industry. The first essay looks at the problem of assigning package destinations to secondary sorters within an automated sorting facility, in a way that balances the workload, while explicitly accounting for day-to-day fluctuation in package volumes and adhering to the outbound loading capacities of the workcenters in the facility. In the second essay we develop the theory needed for solving the Resource Constrained Elementary Shortest Path Problem (RCESPP) while accounting for idle time costing. This problem arises as the subproblem for generating columns which, in our case, correspond to feasible routes in a large scale routing problem that accounts for deliveries followed by pickups and that penalizes idle time. Finally, in the third essay we consider the problem of optimizing the daily store servicing routes for major grocery chains, while using real data from a well known chain. We propose to frame the problem as a Capacitated Vehicle Routing Problem with Time Windows that accounts not only for the minimization of travelling costs, but also the minimization of idle time costs. Given the nature of the problem, we propose a reformulation using decomposition techniques which we intend to solve under a customized column generation setup, consisting of several phases.

ISBN: 9781339980171Subjects--Topical Terms:

3423850
Transportation planning.
Subjects--Index Terms:

Labeling algorithms

Mathematical Programming Models and Algorithms for Large-Scale Problems in Transportation and Logistics.
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100 1 $a Novoa, Luis J. $3 3423849
245 1 0 $a Mathematical Programming Models and Algorithms for Large-Scale Problems in Transportation and Logistics.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 155 p.
500 $a Source: Dissertations Abstracts International, Volume: 78-03, Section: A.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Jarrah, Ahmad I.
502 $a Thesis (Ph.D.)--The George Washington University, 2016.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a The research effort presented in this document focuses on the application of optimization methods to solve two real world problems in the logistics and transportation industry. The first essay looks at the problem of assigning package destinations to secondary sorters within an automated sorting facility, in a way that balances the workload, while explicitly accounting for day-to-day fluctuation in package volumes and adhering to the outbound loading capacities of the workcenters in the facility. In the second essay we develop the theory needed for solving the Resource Constrained Elementary Shortest Path Problem (RCESPP) while accounting for idle time costing. This problem arises as the subproblem for generating columns which, in our case, correspond to feasible routes in a large scale routing problem that accounts for deliveries followed by pickups and that penalizes idle time. Finally, in the third essay we consider the problem of optimizing the daily store servicing routes for major grocery chains, while using real data from a well known chain. We propose to frame the problem as a Capacitated Vehicle Routing Problem with Time Windows that accounts not only for the minimization of travelling costs, but also the minimization of idle time costs. Given the nature of the problem, we propose a reformulation using decomposition techniques which we intend to solve under a customized column generation setup, consisting of several phases.
590 $a School code: 0075.
650 4 $a Transportation planning. $3 3423850
650 4 $a Operations research. $3 547123
653 $a Labeling algorithms
653 $a Nonlinear models
653 $a Optimization
653 $a Routing
653 $a Transportation
690 $a 0709
690 $a 0796
710 2 $a The George Washington University. $b Decision Sciences. $3 3185393
773 0 $t Dissertations Abstracts International $g 78-03A.
790 $a 0075
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10142751