Abstract
Multicommodity Flow (MCF) problems are an important class of combinatorial optimisation problems which can be used to model practical situations such as computer networks, traffic systems and warehouse allocation. The discrete Multicommodity Flow problem is NP-Hard. Many methods have been presented which attempt to find solutions to combinatorial problems such as the discrete MCF problem within a reasonable time frame. In this paper we look at the binary Multicommodity Flow problem and its representation using linear and nonlinear penalty methods. We implement the Simulated Annealing algorithm to find approximate solutions to the minimum cost problem, and compare the performance of variants of the algorithm on a set of test networks. Simulated Annealing requires the definition of a neighborhood of a solution: to enable this, we introduce the Painted Multi-Path Algorithm.
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© 2000 Kluwer Academic Publishers
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Yang, X.Q., Mees, A.I., Campbell, K. (2000). Simulated Annealing and Penalty Methods for Binary Multicommodity Flow Problems. In: Yang, X., Mees, A.I., Fisher, M., Jennings, L. (eds) Progress in Optimization. Applied Optimization, vol 39. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0301-5_6
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DOI: https://doi.org/10.1007/978-1-4613-0301-5_6
Publisher Name: Springer, Boston, MA
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