Title: Using integer partitioning to determine unique configurations for the information age combat model

Authors: Mahmoud T. Khasawneh; Ghaith Rabadi; Shannon Bowling; Nevan Shearer

Addresses: School of Engineering, Texas A&M International University, Laredo, TX 78043, USA ' Department of Engineering Management and Systems Engineering, College of Engineering, Old Dominion University, Norfolk, VA 23529, USA ' College of Engineering Technology, Bluefield State College, Bluefield, WV 24701, USA ' Department of Engineering Management and Systems Engineering, College of Engineering, Old Dominion University, Norfolk, VA 23529, USA

Abstract: This paper addresses the number partitioning problem as it relates to the modelling of distributed network operations, as envisioned in the information age combat model (IACM). The approach proposed in this paper is an automated integer partitioning framework implemented using Mathematica and visual basic. This methodology allows the formation of unique configurations by implementing a matrix-based approach proposed by Fidanci (2010). Finding the unique combination is extremely helpful because it aids IACM simulation attempts by eliminating the significant redundancy associated with combat configurations, and therefore, making simulations much more computationally efficient. The results of the integer partitioning and the formation of unique combinations are subsequently used in a combat simulation model of the IACM. Most importantly, the methodology proposed is generalisable and can be adapted to partition any number of distributed networks, symmetric or asymmetric, for systems applications, within the boundaries of current computing technology and time constraints.

Keywords: integer partitioning; unique configurations; information age combat model; IACM; distributed networked operations.

DOI: 10.1504/IJISE.2017.084428

International Journal of Industrial and Systems Engineering, 2017 Vol.26 No.3, pp.364 - 379

Received: 31 Mar 2015
Accepted: 01 Jul 2015
Published online: 07 Jun 2017 *

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