The Experts below are selected from a list of 19755 Experts worldwide ranked by ideXlab platform
Ramaswami Sridharan - One of the best experts on this subject based on the ideXlab platform.
-
Design and development of a hybrid ant colony-variable neighbourhood search algorithm for a multi-depot green vehicle routing Problem
Transportation Research Part D: Transport and Environment, 2017Co-Authors: E. Jabir, Vinay V. Panicker, Ramaswami SridharanAbstract:The traditional distribution planning Problem in a supply chain has often been studied mainly with a focus on economic benefits. The growing concern about the effects of anthropogenic pollutions has forced researchers and supply chain practitioners to address the socio-environmental concerns. This research study focuses on incorporating the environmental impact on route design Problem. In this work, the aim is to integrate both the objectives, namely economic cost and emission cost reduction for a capacitated multi-depot green vehicle routing Problem. The proposed models are a significant contribution to the field of research in green vehicle routing Problem at the operational level. The formulated integer linear programming model is solved for a set of small scale instances using LINGO solver. A computationally efficient Ant Colony Optimization (ACO) based meta-heuristic is developed for solving both small scale and large scale Problem instances in reasonable amount of time. For solving large scale instances, the performance of the proposed ACO based meta-heuristic is improved by integrating it with a variable neighbourhood search.
József Váncza - One of the best experts on this subject based on the ideXlab platform.
-
Scheduling with alternative routings in CNC workshops
CIRP Annals - Manufacturing Technology, 2012Co-Authors: Youichi Nonaka, Gábor Erdos, Tamás Kis, Takahiro Nakano, József VánczaAbstract:In workshops of CNC machines, jobs may have alternative sequences of operations, where each operation must be performed on one of a pre-specified subset of machines. The key to solving to this extremely hard scheduling Problem is balancing the load on machines of a flexible job shop. The proposed method combines mathematical programming for selecting the best routing alternatives and tabu search for finding the best assignment of machines to operations along with the routings. Experiments in an industrial case study refer to the primary role of optimized load balancing that proved to be computationally tractable on Large-Scale Problem instances. © 2012 CIRP.
Shiwei Guan - One of the best experts on this subject based on the ideXlab platform.
-
Empirical study of effect of grouping strategies for large scale optimization
2016 International Joint Conference on Neural Networks (IJCNN), 2016Co-Authors: Yuping Wang, Shiwei GuanAbstract:The cooperative co-evolution framework (CC) is widely used in the large scale global optimization. It is believed that the CC framework is very sensitive to grouping strategies and the performance deteriorate if interacted variables are not correctly grouped. So many efforts have been devoted to find good ways to correctly decompose the large scale Problem into smaller sub-Problems so as to effectively solve the original Problem by optimizing these smaller sub-Problems using a search algorithm. However, what is the relationship between the grouping strategy and the search algorithm adopted in CC? what is the effect of grouping strategies on the CC framework? This work will tackle these issues. We try to unveil the impact of different grouping strategies on CC and the relationship between the grouping strategies and the search algorithms by empirical study. The experiment results show that the correct result of variable grouping is very important since it can turn the large scale Problem into smaller sub-Problems and make the Problem solving easier. It indeed has a big influence on the results obtained by the search algorithm. However, when the search algorithm adopted is not suitable or effective, even if the grouping strategy gives the correct grouping results, the final results may be poor. In this case, grouping strategy only plays little role on the CC. Thus, only effective grouping strategy plus efficient search algorithm can result in good solutions for large global optimization Problems.
-
Cooperative Co-evolution with Formula Based Grouping and CMA for Large Scale Optimization
2015 11th International Conference on Computational Intelligence and Security (CIS), 2015Co-Authors: Shiwei Guan, Yuping WangAbstract:Cooperative co-evolution framework is widely used in large scale optimization Problems. Usually, the large scale Problem is divided into smaller sub groups using black-box decomposition methods based on variable interactions. However these black-box decomposition methods have limitations in finding correct variable interactions. In this paper, a white-box decomposition method named formula based grouping (FBG) is adopted and further improved. Also, we extend the covariance matrix adaptation to work with FBG under the cooperative co-evolution framework. Based on it, a new evolutionary algorithm is proposed for handling large scale optimization Problems. The numerical experiments on CEC' 2013 benchmark suit shows the efficiency of the proposed algorithm.
Kenneth Runesson - One of the best experts on this subject based on the ideXlab platform.
-
A multiscale model for reinforced concrete with macroscopic variation of reinforcement slip
Computational Mechanics, 2019Co-Authors: Adam Sciegaj, Fredrik Larsson, Karin Lundgren, Filip Nilenius, Kenneth RunessonAbstract:A single-scale model for reinforced concrete, comprising the plain concrete continuum, reinforcement bars and the bond between them, is used as a basis for deriving a two-scale model. The Large-Scale Problem, representing the “effective” reinforced concrete solid, is enriched by an effective reinforcement slip variable. The subscale Problem on a Representative Volume Element (RVE) is defined by Dirichlet boundary conditions. The response of the RVEs of different sizes was investigated by means of pull-out tests. The resulting two-scale formulation was used in an FE $$^2$$ 2 analysis of a deep beam. Load–deflection relations, crack widths, and strain fields were compared to those obtained from a single-scale analysis. Incorporating the independent macroscopic reinforcement slip variable resulted in a more pronounced localisation of the effective strain field. This produced a more accurate estimation of the crack widths than the two-scale formulation neglecting the effective reinforcement slip variable.
E. Jabir - One of the best experts on this subject based on the ideXlab platform.
-
Design and development of a hybrid ant colony-variable neighbourhood search algorithm for a multi-depot green vehicle routing Problem
Transportation Research Part D: Transport and Environment, 2017Co-Authors: E. Jabir, Vinay V. Panicker, Ramaswami SridharanAbstract:The traditional distribution planning Problem in a supply chain has often been studied mainly with a focus on economic benefits. The growing concern about the effects of anthropogenic pollutions has forced researchers and supply chain practitioners to address the socio-environmental concerns. This research study focuses on incorporating the environmental impact on route design Problem. In this work, the aim is to integrate both the objectives, namely economic cost and emission cost reduction for a capacitated multi-depot green vehicle routing Problem. The proposed models are a significant contribution to the field of research in green vehicle routing Problem at the operational level. The formulated integer linear programming model is solved for a set of small scale instances using LINGO solver. A computationally efficient Ant Colony Optimization (ACO) based meta-heuristic is developed for solving both small scale and large scale Problem instances in reasonable amount of time. For solving large scale instances, the performance of the proposed ACO based meta-heuristic is improved by integrating it with a variable neighbourhood search.
