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Lower and upper bounds for the non-linear generalized assignment problem
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DOI：10.1016/j.cor.2020.104933.png)
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摘要
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We consider a non-linear version of the Generalized Assignment Problem, a well-known strongly AP-hard combinatorial optimization problem. We assume that the variables are continuous and that objective function and constraints are defined by non-linear functions of the variables. A mathematical model is introduced and used to derive upper bounds on the optimal solution value. We present constructive heuristics, obtained from decomposition and non-linear programming tools, and a binary linear programming model that provides approximate solutions. By combining the various methods and a local search framework, we finally obtain a hybrid heuristic approach. Extensive computational experiments show that the proposed methods outperform the direct application of non-linear solvers and provide high quality solutions in a reasonable amount of time. (C) 2020 Published by Elsevier Ltd.
Keyword:
Non-linear generalized assignment problem
Upper bounds
Heuristic algorithms
Computational experiments
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4.3
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