Many problems arising in different areas such as production or distribution of goods and services are combinatorial optimization problems (COPs). Many examples can be made for all areas: • Production Scheduling (flow shop, job shop, open shop, etc.) • Resource Management (production factors, human capital, lot sizing etc.) • Logistics (warehouses, distribution, location, etc.) • Finance (portfolio management, risk management, etc.) These problems are interesting because of their relevant practical importance but are also well known to be difficult to solve. This difficulty and, at the same time, the fact that they are concrete and important problems, have led to a large number of solution techniques for COPs. The solution techniques for solving them are traditionally split into exact (mostly based on the optimal solution of the integer programming formulation of the real problem) and heuristic algorithms. Recently a new wave has rapidly grown in the community of researchers, the hybridization of these two approaches, the so called Matheuristics which rely on the idea of exploiting the best of the two, leading to a very large scale neighborhood search based on mathematical programming. While for the combination of heuristic procedures there exists a wide literature, matheuristics are still in development. The Thesis, beyond an introduction on that new approach, presents several different examples and results of such matheuristics on a variety of test instances. Finally some conclusion from the performed experiments and trajectories for future research are drawn.

Matheuristics for Combinatorial Optimization Problems:Applications to Services and Production Systems / Salassa, FABIO GUIDO MARIO. - (2011).

Matheuristics for Combinatorial Optimization Problems:Applications to Services and Production Systems

SALASSA, FABIO GUIDO MARIO
2011

Abstract

Many problems arising in different areas such as production or distribution of goods and services are combinatorial optimization problems (COPs). Many examples can be made for all areas: • Production Scheduling (flow shop, job shop, open shop, etc.) • Resource Management (production factors, human capital, lot sizing etc.) • Logistics (warehouses, distribution, location, etc.) • Finance (portfolio management, risk management, etc.) These problems are interesting because of their relevant practical importance but are also well known to be difficult to solve. This difficulty and, at the same time, the fact that they are concrete and important problems, have led to a large number of solution techniques for COPs. The solution techniques for solving them are traditionally split into exact (mostly based on the optimal solution of the integer programming formulation of the real problem) and heuristic algorithms. Recently a new wave has rapidly grown in the community of researchers, the hybridization of these two approaches, the so called Matheuristics which rely on the idea of exploiting the best of the two, leading to a very large scale neighborhood search based on mathematical programming. While for the combination of heuristic procedures there exists a wide literature, matheuristics are still in development. The Thesis, beyond an introduction on that new approach, presents several different examples and results of such matheuristics on a variety of test instances. Finally some conclusion from the performed experiments and trajectories for future research are drawn.
2011
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2495816
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