Along with the concerns about global climate change, more and more researchers pay attention to the low carbon logistics. Since the logistics industry is an important source of carbon emissions, it is becoming more an...
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Along with the concerns about global climate change, more and more researchers pay attention to the low carbon logistics. Since the logistics industry is an important source of carbon emissions, it is becoming more and more important to reduce carbon emissions in logistics operation. In this paper, we study a low carbon for location routing problem with heterogeneous fleet, simultaneous pickup-delivery and time windows and design a two-phased hybrid heuristicalgorithm to solve the problem. Firstly, we introduce the concept of temporal-spatial distance and use genetic algorithm to cluster the customer points to construct the initial path. Then, we use variable neighborhood search algorithm for local search. By incorporating the idea of simulated annealing algorithm into the framework of variable neighborhood algorithm, the global optimization ability of the algorithm is improved. At the same time, the vehicle adjustment strategy is added in the optimization process. The computational experiments are implemented to investigate the performance of the proposed heuristicalgorithm. Computational results show that the initial solution considering temporal-spatial distance has obvious advantages in the efficiency of the algorithm and the quality of the solution. (C) 2017 The Authors. Published by Elsevier B.V.
Along with the concerns about global climate change, more and more researchers pay attention to the low carbon logistics. Since the logistics industry is an important source of carbon emissions, it is becoming more an...
详细信息
Along with the concerns about global climate change, more and more researchers pay attention to the low carbon logistics. Since the logistics industry is an important source of carbon emissions, it is becoming more and more important to reduce carbon emissions in logistics operation. In this paper, we study a low carbon for location routing problem with heterogeneous fleet, simultaneous pickup-delivery and time windows and design a two-phased hybrid heuristicalgorithm to solve the problem. Firstly, we introduce the concept of temporal-spatial distance and use genetic algorithm to cluster the customer points to construct the initial path. Then, we use variable neighborhood search algorithm for local search. By incorporating the idea of simulated annealing algorithm into the framework of variable neighborhood algorithm, the global optimization ability of the algorithm is improved. At the same time, the vehicle adjustment strategy is added in the optimization process. The computational experiments are implemented to investigate the performance of the proposed heuristicalgorithm. Computational results show that the initial solution considering temporal-spatial distance has obvious advantages in the efficiency of the algorithm and the quality of the solution.
With the growing complexity of embedded VLSI products, traditional System-on-Chip (SoC) are facing severe challenges in the aspects of communicating speed and scalability. Network-on-Chip (NoC) has emerged as a viable...
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With the growing complexity of embedded VLSI products, traditional System-on-Chip (SoC) are facing severe challenges in the aspects of communicating speed and scalability. Network-on-Chip (NoC) has emerged as a viable alternative. In NoC design, application mapping is one of the most holistic researching dimensions, which maps the cores in the application to the routers in the NoC platform. Application mapping problem usually aims to reduce communication cost and power consumption of the overall system. In this paper, we focus on application mapping onto mesh network, and propose a novel two-phase heuristic algorithm. The first phase attempts to explore the potential searching spaces, while the second phase focuses on exploiting the local optima within the searching basin. To verify the effectiveness of the algorithm, this paper performs a quantitative comparisons between our proposed method and the existing mapping methods under both real application and custom generated application benchmarks.
According to the demand of integrated production and the characteristic of charge plan in the steel industry, firstly, use graph theory to describe charge plan;Secondly, a bi-objective charge plan model is introduced ...
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ISBN:
(纸本)9781424421138
According to the demand of integrated production and the characteristic of charge plan in the steel industry, firstly, use graph theory to describe charge plan;Secondly, a bi-objective charge plan model is introduced in detail considering weight range, flow limitation and other factors;To solve this model, a two-phase heuristic algorithm(THA) is introduced, which deals with the simple graph with vertex clustering method firstly, and then design a novel probability match algorithm to solve the optimization model;Finally, the simulation with large-scale production data shows that the model and algorithm is feasible.
According to the demand of integrated production and the characteristic of charge plan in the steel industry, firstly, use graph theory to describe charge plan;Secondly, a bi-objective charge plan model is introduced ...
详细信息
According to the demand of integrated production and the characteristic of charge plan in the steel industry, firstly, use graph theory to describe charge plan;Secondly, a bi-objective charge plan model is introduced in detail considering weight range, flow limitation and other factors;To solve this model, a two-phase heuristic algorithm (THA) is introduced, which deals with the simple graph with vertex clustering method firstly, and then design a novel probability match algorithm to solve the optimization model;Finally, the simulation with large-scale production data shows that the model and algorithm is feasible.
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