Self-adaptive CMSA for solving the multidimensional multi-way number partitioning problem

Djukanovic, Marko; Kartelj, Aleksandar; Blum, Christian

doi:10.1016/j.eswa.2023.120762

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2024

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Cited by 4 publications

References 26 publications

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Introduction to CMSA

Blum

2024

Computational Intelligence Methods and Applications

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Introduction to CMSA

Blum

2024

Computational Intelligence Methods and Applications

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Self-adaptive CMSA

Blum

2024

Computational Intelligence Methods and Applications

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CMSA based on set covering models for packing and routing problems

Akbay,

Blum,

Kalayci

2024

Ann Oper Res

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Many packing, routing, and knapsack problems can be expressed in terms of integer linear programming models based on set covering. These models have been exploited in a range of successful heuristics and exact techniques for tackling such problems. In this paper, we show that integer linear programming models based on set covering can be very useful for their use within an algorithm called “Construct, Merge, Solve & Adapt”(CMSA), which is a recent hybrid metaheuristic for solving combinatorial optimization problems. This is because most existing applications of CMSA are characterized by the use of an integer programming solver for solving reduced problem instances at each iteration. We present applications of CMSA to the variable-sized bin packing problem and to the electric vehicle routing problem with time windows and simultaneous pickups and deliveries. In both applications, CMSA based on a set covering model strongly outperforms CMSA when using an assignment-type model. Moreover, state-of-the-art results are obtained for both considered optimization problems.

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Self-adaptive CMSA for solving the multidimensional multi-way number partitioning problem

Cited by 4 publications

References 26 publications

Introduction to CMSA

Introduction to CMSA

Self-adaptive CMSA

CMSA based on set covering models for packing and routing problems

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