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Network Topology and Time Criticality Effects in the Modularised Fleet Mix Problem

Whitacre, Dr James M and Bender, Dr Axel and Baker, Dr Stephen and Fan, Mr Qi and Sarker, Dr Ruhul A and Abbass, Dr Hussein A (2008) Network Topology and Time Criticality Effects in the Modularised Fleet Mix Problem. [Conference Paper]

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Abstract

In this paper, we explore the interplay between network topology and time criticality in a military logistics system. A general goal of this work (and previous work) is to evaluate land transportation requirements or, more specifically, how to design appropriate fleets of military general service vehicles that are tasked with the supply and re-supply of military units dispersed in an area of operation. The particular focus of this paper is to gain a better understanding of how the logistics environment changes when current Army vehicles with fixed transport characteristics are replaced by a new generation of modularised vehicles that can be configured task-specifically. The experimental work is conducted within a well developed strategic planning simulation environment which includes a scenario generation engine for automatically sampling supply and re-supply missions and a multi-objective meta-heuristic search algorithm (i.e. Evolutionary Algorithm) for solving the particular scheduling and routing problems. The results presented in this paper allow for a better understanding of how (and under what conditions) a modularised vehicle fleet can provide advantages over the currently implemented system.

Item Type:Conference Paper
Subjects:Computer Science > Artificial Intelligence
ID Code:6581
Deposited By: Whitacre, Dr James M
Deposited On:06 Jul 2009 09:42
Last Modified:11 Mar 2011 08:57

References in Article

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[1] H. Abbass, A. Bender, S. Baker, and R. Sarker, "Anticipating Future Scenarios for the Design of Modularised Vehicle and Trailer Fleets," in SimTecT2007, Simulation Conference Brisbane, 2007.

[2] S. Baker, A. Bender, H. Abbass, and R. Sarker, "A Scenario Based Evolutionary Scheduling Approach for Assessing Future Supply Chain Fleet Capabilities," in Evolutionary Scheduling, Studies in Computational Intelligence, P. Cowling, K. Tan, and K. Dahal, Eds.: Springer-Verlag, 2007, pp. 485-511.

[3] H. Abbass, A. Bender, S. Baker, and R. Sarker, "Identifying the Fleet-mix in a Military Setting," in The Second International Intelligent Logistics Systems Conference (IILS2006) Brisbane, 2006.

[4] K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms: Wiley, 2001.

[5] G. E. Gallasch, N. Lilith, J. Billington, L. Zhang, A. Bender, and B. Francis, "Modelling Defence Logistics Networks," International Journal on Software Tools for Technology Transfer (STTT), 2007.

[6] A. W. Gill, R. R. Egudo, P. J. Dortmans, D. Grieger, S. Defence, and L. A. B. Technology Organisation Salisbury Systems Sciences, Using Agent Based Distillations in Support of the Army Capability Development Process-A Case Study: Defense Technical Information Center, 2002.

[7] S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D. U. Hwang, "Complex networks: Structure and dynamics," Physics Reports, vol. 424, pp. 175-308, 2006.

[8] M. E. J. Newman, "The structure and function of complex networks," SIAM Review, vol. 45, pp. 167-256, 2003.

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