In this paper, an adaptive variable neighbourhood search (AVNS) algorithm that incorporates large... more In this paper, an adaptive variable neighbourhood search (AVNS) algorithm that incorporates large neighbourhood search (LNS) as a diversification strategy is proposed and applied to the capacitated vehicle routing problem. The AVNS consists of two stages: a learning phase and a multi-level VNS with guided local search. The adaptive aspect is integrated in the local search where a set of highly successful local searches is selected based on the intelligent selection mechanism. In addition, the hybridisation of LNS with the AVNS enables the solution to escape from the local minimum effectively. To make the algorithm more competitive in terms of the computing time, a simple and flexible data structure and a neighbourhood reduction scheme are embedded. Finally, we adapt a new local search move and an effective removal strategy for the LNS. The proposed AVNS was tested on the benchmark data sets from the literature and produced very competitive results.
ABSTRACT The paper investigates a class of extensions to the vehicle routing problem. Different p... more ABSTRACT The paper investigates a class of extensions to the vehicle routing problem. Different problem versions – some well-known, some more recent – are explained and placed in a taxonomy. A central focus of the paper is on the assumptions generally made in the literature and on the benefits of not making too restrictive assumptions. Research issues on novel problem classes are highlighted. An Integer Linear Programming (ILP) formulation is also presented. It is also shown how this formulation can be adapted to cater for other problem versions. This paper also discusses various solution methodologies including meta-heuristics to solve the models and what more is needed the vehicle routing problem.
In this paper, an adaptive variable neighbourhood search (AVNS) algorithm that incorporates large... more In this paper, an adaptive variable neighbourhood search (AVNS) algorithm that incorporates large neighbourhood search (LNS) as a diversification strategy is proposed and applied to the capacitated vehicle routing problem. The AVNS consists of two stages: a learning phase and a multi-level VNS with guided local search. The adaptive aspect is integrated in the local search where a set of highly successful local searches is selected based on the intelligent selection mechanism. In addition, the hybridisation of LNS with the AVNS enables the solution to escape from the local minimum effectively. To make the algorithm more competitive in terms of the computing time, a simple and flexible data structure and a neighbourhood reduction scheme are embedded. Finally, we adapt a new local search move and an effective removal strategy for the LNS. The proposed AVNS was tested on the benchmark data sets from the literature and produced very competitive results.
ABSTRACT The paper investigates a class of extensions to the vehicle routing problem. Different p... more ABSTRACT The paper investigates a class of extensions to the vehicle routing problem. Different problem versions – some well-known, some more recent – are explained and placed in a taxonomy. A central focus of the paper is on the assumptions generally made in the literature and on the benefits of not making too restrictive assumptions. Research issues on novel problem classes are highlighted. An Integer Linear Programming (ILP) formulation is also presented. It is also shown how this formulation can be adapted to cater for other problem versions. This paper also discusses various solution methodologies including meta-heuristics to solve the models and what more is needed the vehicle routing problem.
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Papers by Niaz Wassan