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dc.contributor.authorShamakhai, Hayat Abdullah-
dc.date.accessioned2017-06-14T14:33:04Z-
dc.date.available2017-06-14T14:33:04Z-
dc.date.issued2017-05-31-
dc.identifier.urihttps://zone.biblio.laurentian.ca/handle/10219/2762-
dc.description.abstractIn operation research, the Multiple Knapsack Problem (MKP) is classified as a combinatorial optimization problem. It is a particular case of the Generalized Assignment Problem. The MKP has been applied to many applications in naval as well as financial management. There are several methods to solve the Knapsack Problem (KP) and Multiple Knapsack Problem (MKP); in particular the Bound and Bound Algorithm (B&B). The bound and bound method is a modification of the Branch and Bound Algorithm which is defined as a particular tree-search technique for the integer linear programming. It has been used to obtain an optimal solution. In this research, we provide a new approach called the Adapted Transportation Algorithm (ATA) to solve the KP and MKP. The solution results of these methods are presented in this thesis. The Adapted Transportation Algorithm is applied to solve the Multiple Knapsack Problem where the unit profit of the items is dependent on the knapsack. In addition, we will show the link between the Multiple Knapsack Problem (MKP) and the multiple Assignment Problem (MAP). These results open a new field of research in order to solve KP and MKP by using the algorithms developed in transportation.en_CA
dc.language.isoenen_CA
dc.subjectgeneralized assignment problemen_CA
dc.subjectassignment problemen_CA
dc.subjectknapsack problemen_CA
dc.subjectmultiple knapsack problemen_CA
dc.subjectbranch and bound algorithmen_CA
dc.subjectbound and bound algorithmen_CA
dc.subjecttransportation problemen_CA
dc.subjectmultiple assignment problemen_CA
dc.subjectadapted transportation problemen_CA
dc.subjectvogel approximation methoden_CA
dc.subjectgroup role assignment problem.en_CA
dc.titleThe 0 -1 multiple knapsack problemen_CA
dc.typeThesisen_CA
dc.description.degreeMaster of Science (MSc) in Computational Scienceen_CA
dc.publisher.grantorLaurentian University of Sudburyen_CA
Appears in Collections:Computational Sciences - Master's theses
Master's Theses

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