Smart Sustainable Production and Distribution Network Model for City Multi-Floor Manufacturing Clusters

This study focuses on management ways within a city multi-floor manufacturing cluster (MFMC). The application of MFMC in megapolises is closely related to the problem of urban spatial development and the problem of matching transport and logistics services. The operation of the MFMC depends on the efficiency of production and transport management considering technical, economic, end environmental factors. Therefore, conditions affecting decision-making in the field of production planning by MFMCs and accompanying transports within the agglomeration area with the use of the production-service platform were presented. Assumptions were created for the decision model, allowing for the selection of partners within the MFMC to execute the production order. A simplified decision model using the Hungarian algorithm was proposed, which was verified with the use of test data. The model is universal for material flow analysis and is an assessments basis for smart sustainable supply chain decision-making and planning. Despite the narrowing of the scope of the analysis and the simplifications applied, the presented model using the Hungarian algorithm demonstrated its potential to solve the problem of partner selection for the execution of the contract by MFMC.

A Multi-Criteria Approach to Decision Making in Broadband Technology Selection

A new European Union regulatory framework for the telecom sector has been under a process of transposition to national laws by its member states that should have been completed by the end of 2020, notwithstanding some delays. A core purpose of the regulatory framework is to guarantee that most citizens will have access to very fast Internet connections, capable of 100 Mbps download link speed, regardless of where they live. According to this new framework, in areas where the market does not deliver, governments are to launch public tenders for the deployment, maintenance, and operations of network infrastructure as well as services, and public funds should be used to support the deployment of these broadband networks in less densely populated areas. Needless to say, public tenders of this nature are subject to different criteria when it comes to candidate evaluation. In this paper, we present a decision model for the selection of operators to deploy and maintain broadband networks in scarcely populated areas, taking into consideration infrastructure costs, the technical quality offered by the solutions, and the credibility of the candidates. We suggest an integrated multi-stakeholder multi-criteria approach and demonstrate how it can be used in this complex area and find that in the example provided, taking a relevant set of criteria into the analysis, optical fibre networks hold much higher chances to be used in these public tenders compared to networks based on the broadly favoured 5G technology.

Construction and Simulation of a Strategic HR Decision Model Based on Recurrent Neural Network

In this paper, RNN (Recurrent Neural Network) algorithm is used to conduct an in-depth analysis of HR strategic decision-making and an HR strategic decision model is constructed for simulation. The four evaluation dimensions of index screening are extracted, the generalized gray correlation analysis is applied to screen the initial selection indexes of HR strategic decision-making, and then the HR strategic decision-making index system of power supply enterprises is constructed. On this basis, the applicability of the BP neural network in HR strategic decision-making is analysed and demonstrated, a BP neural network-based HR strategic decision-making model for power supply enterprises is constructed, and the rationality of the model is illustrated through the model training and testing. Finally, an empirical study is conducted with S power supply company as an example to illustrate the operability of the BP network model for HR strategic decision-making. The results of this paper provide a scientific basis for human resource decision-making in power supply enterprises and provide theoretical support for promoting the healthy development of power supply enterprises. Improving the level of human resource management can make fuller use of human resources and realize the strategic goals of the enterprise. A strategy for optimizing the training of hybrid convolutional neural networks is proposed, using an exponential linear unit activation function to solve the problem of neuron dead zones, a hybrid pooling strategy is used to improve the problem of feature information loss in maximum pooling processing, and an improved cross-entropy loss function is used to solve the problem of insufficient learning of difficult classification samples. The optimization of the model training process is finally completed, and the recommendation quality is improved. The Bayesian probability distribution table is learned to be filled in the expert data set; finally, the accuracy and effectiveness of the model are tested on the experimental platform. The results of the experiments show that the established Bayesian model beats the platform’s built-in intelligent method with a 78.2% win rate. That is, the model can make intelligent recommendations for strategies in staffed and unmanned platform decision-making and command-and-control combat units to execute tactical actions to achieve the best operational effectiveness.

An approach to optimize container locations in a containership with Electre III

In this article, we treat the problem of container storage in the export direction, exactly in the containership loading process. We propose an approach to the problem of container placement in a containership by describing a decision model to help decision-makers (handling operators) to minimize the total containers shifting. This is obtained by using a multicriteria decision method named Electre III (Elimination and Choice Expressing Reality) to identify the best location of any container. Here, we consider four criteria: the container destination, the container weight, the departure date of the container and the container type. This method has as input a matrix of performance and the subjective parameters and gives a ranking of alternatives as an output.

An Towards a multi-criteria decision approach to solving the container storage problem Container Ship loading component.

In this article, the authors treat the problem of container storage in the export direction, exactly in the containership loading process. The authors propose an approach to the problem of container placement in a containership by describing a decision model to help decision-makers (handling operators) to minimize the total container movement. This is obtained by using a multicriteria decision method AHP (analytic hierarchy process) to identify the best location of any container. Here, the authors consider four criteria: the container destination, the container weight, the departure date of the container, and the container type.