245 research outputs found

    A tabu search heuristic for the Equitable Coloring Problem

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    The Equitable Coloring Problem is a variant of the Graph Coloring Problem where the sizes of two arbitrary color classes differ in at most one unit. This additional condition, called equity constraints, arises naturally in several applications. Due to the hardness of the problem, current exact algorithms can not solve large-sized instances. Such instances must be addressed only via heuristic methods. In this paper we present a tabu search heuristic for the Equitable Coloring Problem. This algorithm is an adaptation of the dynamic TabuCol version of Galinier and Hao. In order to satisfy equity constraints, new local search criteria are given. Computational experiments are carried out in order to find the best combination of parameters involved in the dynamic tenure of the heuristic. Finally, we show the good performance of our heuristic over known benchmark instances

    Proceedings of the 2nd Computer Science Student Workshop: Microsoft Istanbul, Turkey, April 9, 2011

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    Solution Methods for a Scheduling Problem with Incompatibility and Precedence Constraints

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    Consider a project which consists in a set of operations to be performed, assuming the processing time of each operation is at most one time period. In this project, precedence and incompatibility constraints between operations have to be satisfied. The goal is to assign a time period to each operation while minimizing the duration of the whole project and while taking into account all the constraints. Based on the mixed graph coloring model and on an efficient and quick tabu search algorithm for the usual graph coloring problem, we propose a tabu search algorithm as well as a variable neighborhood search heuristic for the considered scheduling problem. We formulate an integer linear program (useful for the CPLEX solver) as well as a greedy procedure for comparison considerations. Numerical results are reported on instances with up to 500 operations

    A study on exponential-size neighborhoods for the bin packing problem with conflicts

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    We propose an iterated local search based on several classes of local and large neighborhoods for the bin packing problem with conflicts. This problem, which combines the characteristics of both bin packing and vertex coloring, arises in various application contexts such as logistics and transportation, timetabling, and resource allocation for cloud computing. We introduce O(1)O(1) evaluation procedures for classical local-search moves, polynomial variants of ejection chains and assignment neighborhoods, an adaptive set covering-based neighborhood, and finally a controlled use of 0-cost moves to further diversify the search. The overall method produces solutions of good quality on the classical benchmark instances and scales very well with an increase of problem size. Extensive computational experiments are conducted to measure the respective contribution of each proposed neighborhood. In particular, the 0-cost moves and the large neighborhood based on set covering contribute very significantly to the search. Several research perspectives are open in relation to possible hybridizations with other state-of-the-art mathematical programming heuristics for this problem.Comment: 26 pages, 8 figure

    Tabu Search: A Comparative Study

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