The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Bekir Guler - One of the best experts on this subject based on the ideXlab platform.
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a multi phase oscillated variable neighbourhood search algorithm for a real world open vehicle routing Problem
Applied Soft Computing, 2017Co-Authors: Aise Zulal şevkli, Bekir GulerAbstract:Abstract The aim of this study is to solve the newspaper delivery optimization Problem for a media delivery company in Turkey by reducing the total cost of carriers. The Problem is modelled as an open vehicle routing Problem (OVRP), which is a variant of the vehicle routing Problem. A variable neighbourhood search-based algorithm is proposed to solve a Real-World OVRP. The proposed algorithm is tested with varieties of small and large-scale benchmark suites and a very large-scale Real-World Problem instance. The results of the proposed algorithm provide either the best known solution or a competitive solution for each of the benchmark instances. The algorithm also improves the Real-World company’s solutions by more than 10%.
Alain Delchambre - One of the best experts on this subject based on the ideXlab platform.
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a genetic algorithm for bin packing and line balancing
International Conference on Robotics and Automation, 1992Co-Authors: Emanuel Falkenauer, Alain DelchambreAbstract:The authors present an efficient genetic algorithm for two NP-hard Problems, the bin packing and the line balancing Problems. They define the two Problems precisely and specify a cost function suitable for the bin packing Problem. It is shown that the classic genetic algorithm performs poorly on grouping Problems and an encoding of solutions of fitting these Problems is presented. Efficient crossover and mutation operators are introduced for bin packing. The modification necessary to fit these operators for line balancing is given. Results of performance tests on randomly generated data are included. The line balancing tests cover Real-World Problem sizes. The results and areas of further research are discussed. >
Gabriella Kokai - One of the best experts on this subject based on the ideXlab platform.
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self adaptive ant colony optimisation applied to function allocation in vehicle networks
Genetic and Evolutionary Computation Conference, 2007Co-Authors: Manuel Forster, Bettina Bickel, Bernd Hardung, Gabriella KokaiAbstract:Modern vehicles possess an increasing number of softwareand hardware components that are integrated in electroniccontrol units (ECUs). Finding an optimal allocation forall components is a multi-objective optimisation Problem,since every valid allocation can be rated according to multipleobjectives like costs, busload, weight, etc. Additionally,several constraints mainly regarding the availability of resourceshave to be considered. This paper introduces a newvariant of the well-known ant colony optimisation, whichhas been applied to the Real-World Problem described above.Since it concerns a multi-objective optimisation Problem,multiple ant colonies are employed. In the course of thiswork, pheromone updating strategies specialised on constrainthandling are developed. To reduce the effort neededto adapt the algorithm to the optimisation Problem by tuningstrategic parameters, self-adaptive mechanisms are establishedfor most of them. Besides the reduction of theeffort, this step also improves the algorithm's convergencebehaviour.
Aise Zulal şevkli - One of the best experts on this subject based on the ideXlab platform.
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a multi phase oscillated variable neighbourhood search algorithm for a real world open vehicle routing Problem
Applied Soft Computing, 2017Co-Authors: Aise Zulal şevkli, Bekir GulerAbstract:Abstract The aim of this study is to solve the newspaper delivery optimization Problem for a media delivery company in Turkey by reducing the total cost of carriers. The Problem is modelled as an open vehicle routing Problem (OVRP), which is a variant of the vehicle routing Problem. A variable neighbourhood search-based algorithm is proposed to solve a Real-World OVRP. The proposed algorithm is tested with varieties of small and large-scale benchmark suites and a very large-scale Real-World Problem instance. The results of the proposed algorithm provide either the best known solution or a competitive solution for each of the benchmark instances. The algorithm also improves the Real-World company’s solutions by more than 10%.
Joaquin Bautista - One of the best experts on this subject based on the ideXlab platform.
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multiobjective constructive heuristics for the 1 3 variant of the time and space assembly line balancing Problem aco and random greedy search
Information Sciences, 2010Co-Authors: Manuel Chica, Iscar Cordon, Sergio Damas, Joaquin BautistaAbstract:In this work we present two new multiobjective proposals based on ant colony optimisation and random greedy search algorithms to solve a more realistic extension of a classical industrial Problem: time and space assembly line balancing. Some variants of these algorithms have been compared in order to find out the impact of different design configurations and the use of heuristic information. Good performance is shown after applying every algorithm to 10 well-known Problem instances in comparison to NSGA-II. In addition, those algorithms which have provided the best results have been employed to tackle a Real-World Problem at the Nissan plant, located in Spain.
