In the treatment of substations, it is very crucial to make a reasonable arrangement of route used for the maintenance of each substation. Moreover, given the urgency degree of different substations, the priority of each substation should be carefully considered for a good arrangement of route used for the maintenance. In this paper, considering the complexity of the routing arrangement, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) were adopted with the designed priority coding methods and priority constraints for a more reasonable arrangement of route. Moreover, with the analysis of the performances of GA and ACO on the priority-based routing arrangement, a fused method was designed to obtain a good routing arrangement in an efficient manner. The experimental results show that, with the designed priority coding method and the priority constraints, a more reason result can be obtained by the fusion-based method.
KEYWORDS: Data modeling, Raw materials, Algorithms, Optimization (mathematics), Neural networks, Visualization, Visual analytics, Process modeling, Autoregressive models, Time series analysis
In a highly competitive market, it is critical for companies to effectively reduce raw material procurement and forwarding costs in the production of their products. Based on the data of suppliers and forwarders, we optimize all the details of the procurement process and minimize the procurement cost of enterprises through a complete solution called APSA. APSA first selects some of the most critical suppliers from all supplier data using multiple evaluation methods combined with the TOPSIS evaluation model. In this process, three attributes, “Time-weight Mean”, “Order Quantity Variance” and “Trading Stability Factor” were defined by us to evaluate each supplier more comprehensively from multiple perspectives. For the prediction of forwarder data, the rational analysis of the periodical features of each forwarder is used first. Then ARIMA and LSTM are applied, respectively, with the different data types. Finally, reasonable multi-objective optimization equations are established, and the optimal procurement and transfer solution is solved by the simulated annealing algorithm.
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