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Meta-RaPS: An effective solution ap...

Moraga, Reinaldo Javier.

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Meta-RaPS: An effective solution approach for combinatorial problems.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Meta-RaPS: An effective solution approach for combinatorial problems./
作者:	Moraga, Reinaldo Javier.
面頁冊數:	125 p.
附註:	Major Professors: Gary E. Whitehouse; Gail W. DePuy.
Contained By:	Dissertation Abstracts International63-04
標題:	Engineering, Industrial -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3042966
ISBN:	0493668616

Meta-RaPS: An effective solution approach for combinatorial problems.
Moraga, Reinaldo Javier.

Meta-RaPS: An effective solution approach for combinatorial problems. - 125 p.

Major Professors: Gary E. Whitehouse; Gail W. DePuy.

Thesis (Ph.D.)--University of Central Florida, 2002.

*This dissertation includes a CD that is compound (contains both a paper copy and a CD as part of the dissertation). The CD requires the following applications: Microsoft Office; Notepad

ISBN: 0493668616Subjects--Topical Terms:

1260336
Engineering, Industrial

Meta-RaPS: An effective solution approach for combinatorial problems.
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500 $a Major Professors: Gary E. Whitehouse; Gail W. DePuy.
500 $a Source: Dissertation Abstracts International, Volume: 63-04, Section: B, page: 2013.
502 $a Thesis (Ph.D.)--University of Central Florida, 2002.
520 $a *This dissertation includes a CD that is compound (contains both a paper copy and a CD as part of the dissertation). The CD requires the following applications: Microsoft Office; Notepad
520 $a This study introduces a novel and promising solution approach for combinatorial problems called “<underline>Meta</underline>heuristic for <underline> Ra</underline>ndomized <underline>P</underline>riority <underline>S</underline>earch” (or Meta-RaPS). This metaheuristic takes advantage of two experimental findings. First, if some degree of randomness is introduced into a simple construction heuristic (or priority rule), results improve dramatically, and second, if two heuristics are randomly combined, the solution obtained often outperforms the solution of either heuristic individually.
520 $a Meta-RaPS offers a simple and versatile approach that can be easily implemented by practitioners. Meta-RaPS integrates priority rules, randomness and sampling. At each iteration, Meta-RaPS constructs and improves feasible solutions through the utilization of priority rules used in a randomized fashion. After a specified number of iterations, MetaRaPS reports the best solution found.
520 $a This research has been principally devoted to the generalization and extensions of Meta-RaPS to various combinatorial problems including the resource allocation problem, traveling salesman problem, multidimensional knapsack problem, and the vehicle routing problem. Results obtained by Meta-RaPS always outperformed the original construction heuristic by reducing the percent deviation from the optimal by at least 50%. Also, Meta-RaPS' performance deviates within a range of 0.08–1.38% from the optimal, which is better than other solution approaches found in literature such as genetic algorithms, simulated annealing, neural networks, tabu search, branch and bound techniques, and problem specific algorithms
520 $a During execution, Meta-RaPS uses four parameters: the number of iterations, the priority percentage, the restriction percentage, and the improvement percentage. The priority percentage is defined as the probability Meta-RaPS uses the original priority rule during an iteration and is used to combine the original and a modified version of the priority rule that includes randomness. The restriction percentage is defined as the percentage from which a list of non selected activities is expanded or reduced to define the next available activity list from which Meta-RaPS randomly selects the next activity to assign. The improvement percentage is used to decide whether a constructed solution will receive further improvement. These four parameters are important because they control the duration of the search as well as the degree of incorporated randomness
520 $a Additionally, four approaches for setting parameter values in both the priority percentage and the restriction percentage are used to obtain the best solution. The four methods compared are: ranking and selection, multiple comparisons, stochastic genetic algorithms search, and response surface methodology. The stochastic genetic algorithms search produced the best results in terms of solution quality.
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