dc.contributor.author | Pinar, Mustafa C. | en |
dc.contributor.author | Zenios, Stavros A. | en |
dc.creator | Pinar, Mustafa C. | en |
dc.creator | Zenios, Stavros A. | en |
dc.date.accessioned | 2019-04-24T06:29:43Z | |
dc.date.available | 2019-04-24T06:29:43Z | |
dc.date.issued | 1992 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/46908 | en |
dc.description.abstract | The problem of optimally (re)allocating Navy personnel to permanent stations is compounded by several considerations: budgetary requirements, staffing of positions by occupation groups or ranks, and maintaining an acceptable level of readiness. The problem can be formulated as a transportation problem with side constraints. An additional, non-network, variable measures the readiness level. However the resulting mathematical programs are very large - up to 66,000 variables and 36,000 constraints including 5,400 non-network inequalities. In this paper we report on an application of the Linear-Quadratic Penalty (LQP) method to solve this large scale problem. It is therefore possible to exploit the structure of the embedded transportation problem. The algorithm solves efficiently, and to a high degree of accuracy, models that would not be solved with a general purpose solver. Hence, the model can be used for strategic planning decisions. Further work on the CRAY Y-MP supercomputer illustrates the use of vector computers for solving the Naval personnel scheduling problem in a way that makes it useful for operational planning purposes. | en |
dc.language.iso | eng | en |
dc.title | Naval personnel assignment:an application of linear-quadratic penalty methods | en |
dc.type | info:eu-repo/semantics/bookChapter | |
dc.author.faculty | Σχολή Οικονομικών Επιστημών και Διοίκησης / Faculty of Economics and Management | |
dc.author.department | Τμήμα Λογιστικής και Χρηματοοικονομικής / Department of Accounting and Finance | |
dc.type.uhtype | Book Chapter | en |
dc.contributor.orcid | Zenios, Stavros A. [0000-0001-7576-4898] | |
dc.gnosis.orcid | 0000-0001-7576-4898 | |