The enormous growth in the volume of river transportation and the upgrade of inland waterways make inland shipping more and more important in transportation and logistics. This work proposes a hub-and-spoke network de...
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The enormous growth in the volume of river transportation and the upgrade of inland waterways make inland shipping more and more important in transportation and logistics. This work proposes a hub-and-spoke network design (HSND) for container shipping in inland waterways based on the tree-like structure river. Firstly, the characteristics of the hub-and-spoke network in the inland waterways are presented in detail. Then, an integer linear programming model is proposed to simultaneously determine the optimal hub location, feeder port allocation, and fleet deployment to minimize the total cost of ships, transportation, and transshipment. A decomposition-based math-heuristic method and an enhanced genetic algorithm are then proposed to solve the HSND. A case study based on the traffic on the Yangtze River and extensive computational experiments are conducted to verify the effectiveness of the proposed models and methods. In addition, the impacts of the number of hub ports and the economies of scale resulting from the hub-and-spoke network are investigated from the economic and network structure perspectives, which leads to some managerial insights.
Nowadays, it is very popular to employ geneticalgorithm (GA) and its improved strategies to optimize neural networks (i.e., WNN) to solve the modeling problems of aluminum electrolysis manufacturing system (AEMS). Ho...
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Nowadays, it is very popular to employ geneticalgorithm (GA) and its improved strategies to optimize neural networks (i.e., WNN) to solve the modeling problems of aluminum electrolysis manufacturing system (AEMS). However, the traditional GA only focuses on restraining the infinite growth of the optimal species without reducing the similarity among the remaining excellent individuals when using the exclusion operator. Additionally, when performing arithmetic crossover or Cauchy mutation, a functional operator that conforms to the law of evolution is not constructed to generate proportional coefficients, which seriously restricted the exploitation of the hidden potential in geneticalgorithms. To solve the above problems, this paper adopts three new methods to explore the performance enhancement of geneticalgorithms (EGA). First, the mean Hamming distance (H-Mean) metric is designed to measure the spatial dispersion of individuals to alleviate selection pressure. Second, arithmetic crossover with transformation of the sigmoid-based function is developed to dynamically adjust the exchange proportion of offspring. Third, an adaptive scale coefficient is introduced into the Gauss-Cauchy mutation, which can regulate the mutation step size in real time and search accuracy for individuals in the population. Finally, the EGA solver is employed to deeply mine the optimal initial parameters of wavelet neural network (EGAWNN). Moreover, the paper provides the algorithm performance test, convergence analysis and significance test. The experimental results reveal that the EGAWNN model outperforms other relevant wavelet-based forecasting models, where the RMSE in test sets based on EGAWNN is 305.72 smaller than other seven algorithms.
This paper proposes the efficient approaches for solving the Optimal Power Flow (OPF) problem using the meta-heuristic algorithms. Mathematically, OPF is formulated as non-linear equality and inequality constrained op...
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This paper proposes the efficient approaches for solving the Optimal Power Flow (OPF) problem using the meta-heuristic algorithms. Mathematically, OPF is formulated as non-linear equality and inequality constrained optimization problem. The main drawback of meta-heuristic algorithm based OPF is the excessive execution time required due to the large number of power flows needed in the solution process. The proposed efficient approaches uses the lower and upper bounds of objective function values. By using this approach, the number of power flows to be performed are reduced substantially, resulting in the solution speed up. The efficiently generated objective function bounds can result in the faster solutions of meta-heuristic algorithms. The original advantages of meta-heuristic algorithms, such as ability to handle complex non-linearities, discontinuities in the objective function, discrete variables handling, and multi-objective optimization, etc., are still available in the proposed efficient approaches. The proposed OPF formulation includes the active and reactive power generation limits, Valve Point Loading (VPL) and Prohibited Operating Zones (POZs) effects of generating units. The effectiveness of proposed approach is examined on IEEE 30, 118 and 300 bus test systems, and the simulation results confirm the efficiency and superiority of the proposed approaches over the other meta-heuristic algorithms. The proposed efficient approach is generic enough to use with any type of metaheuristic algorithm based OPF.
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