Integer Programming Approach to Graph Colouring Problem and Its Implementation in GAMS
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Šeda, Miloš
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Mark
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The graph colouring problem is one of the most studied combinatorial optimisation problems, one with many applications, e. g., in timetabling, resource assignment, team-building problems, network analysis, and cartography. Because of its NP-hardness, the question arises of its solvability for larger instances. Instead of the traditional approaches based on the use of approximate or stochastic heuristic methods, we focus here on the direct use of an integer programming model in the GAMS environment. This environment makes it possible to solve instances much larger than in the past. Neither does it require complex parameter settings or statistical evaluation of the results as in the case of stochastic heuristics because the computational core of software tools, nested in GAMS, is deterministic in nature.
The graph colouring problem is one of the most studied combinatorial optimisation problems, one with many applications, e. g., in timetabling, resource assignment, team-building problems, network analysis, and cartography. Because of its NP-hardness, the question arises of its solvability for larger instances. Instead of the traditional approaches based on the use of approximate or stochastic heuristic methods, we focus here on the direct use of an integer programming model in the GAMS environment. This environment makes it possible to solve instances much larger than in the past. Neither does it require complex parameter settings or statistical evaluation of the results as in the case of stochastic heuristics because the computational core of software tools, nested in GAMS, is deterministic in nature.
The graph colouring problem is one of the most studied combinatorial optimisation problems, one with many applications, e. g., in timetabling, resource assignment, team-building problems, network analysis, and cartography. Because of its NP-hardness, the question arises of its solvability for larger instances. Instead of the traditional approaches based on the use of approximate or stochastic heuristic methods, we focus here on the direct use of an integer programming model in the GAMS environment. This environment makes it possible to solve instances much larger than in the past. Neither does it require complex parameter settings or statistical evaluation of the results as in the case of stochastic heuristics because the computational core of software tools, nested in GAMS, is deterministic in nature.
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en
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