A hierarchical analytical model for performance management of integrated logistics
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Cited by 22 publications
References 34 publications
This research focuses on the analytic hierarchy model in the decision-making system that has a more complex structure and maintains the stability of the system, models the application process with the complexity and diversity of the rural economy, collects sample data with the help of different types of rural tourism questionnaire surveys, and integrates the data of rural tourism and other tourism into the model. The following are obtained: (1) During the level analysis, each phenotype track uses RRM, C 1 = 0.26 , C 2 = 0.223 , C 3 = 0.52 , C 4 = 0.25 , C 5 = 0.833 , C 6 = 0.442 , C 7 = 0.75 , C 8 = 0.127 , C 9 = 0.876 , C 10 = 0.792 , C 11 = 0.049 , C 12 = 0.16 , C 13 = 0.166 , and C 14 = 0.049 . The problems of the complex structure of the evaluation can be divided into simple analysis modules, and each module is analyzed at a level. The phenotypic trajectory of each individual is divided into target layer, standard layer, and scheme layer. (2) Arrangement and decision modeling were performed according to one or several indicators of different factors. In the hierarchical random regression model, APC = 0.214 , UPUA = 0.042 , TO = 0.081 , YPUA = 0.082 , PCP = 0.068 , and APS = 0.067 . The characteristic quantity analysis of different environments can be carried out, and the amplitude error and frequency error obtained are relatively small. IAND = 0.115 , AVA = 0.198 , RD = 0.119 , PI = 0.041 , PCCL = 0.142 , IOC = 0.201 , and DSTC = 0.069 . The comparison shows that the hierarchical analysis model is better than the hierarchical random regression model. (3) High-efficiency hybrid model correlation acceleration is the worst model. The experimental data are APC = 0.147 , UPUA = 0.029 , TO = 0.055 , YPUA = 0.06 , PCP = 0.047 , APS = 0.046 , IAND = 0.079 , AVA = 0.136 , RD = 0.082 , PI = 0.028 , PCCL = 0.098 , IOC = 0.139 , and DSTC = 0.048 . (4) The predicted 2020 data and the actual data have small errors. The data obtained by the AHP model is GDP = 1262.1 , finance = 185.09 , budget = 68 , tax = 51.92 , fund budget = 69.23 , transfer income = 40.14 , debt income = 7.73 , disposable financial power = 177.37 , fiscal expenditure = 191.26 , public budget = 88.68 , government expenditure = 71.39 , transfer expenditure = 23.46 , debt expenditure = 7.73 , and last year balance = 2.39 .
This research focuses on the analytic hierarchy model in the decision-making system that has a more complex structure and maintains the stability of the system, models the application process with the complexity and diversity of the rural economy, collects sample data with the help of different types of rural tourism questionnaire surveys, and integrates the data of rural tourism and other tourism into the model. The following are obtained: (1) During the level analysis, each phenotype track uses RRM, C 1 = 0.26 , C 2 = 0.223 , C 3 = 0.52 , C 4 = 0.25 , C 5 = 0.833 , C 6 = 0.442 , C 7 = 0.75 , C 8 = 0.127 , C 9 = 0.876 , C 10 = 0.792 , C 11 = 0.049 , C 12 = 0.16 , C 13 = 0.166 , and C 14 = 0.049 . The problems of the complex structure of the evaluation can be divided into simple analysis modules, and each module is analyzed at a level. The phenotypic trajectory of each individual is divided into target layer, standard layer, and scheme layer. (2) Arrangement and decision modeling were performed according to one or several indicators of different factors. In the hierarchical random regression model, APC = 0.214 , UPUA = 0.042 , TO = 0.081 , YPUA = 0.082 , PCP = 0.068 , and APS = 0.067 . The characteristic quantity analysis of different environments can be carried out, and the amplitude error and frequency error obtained are relatively small. IAND = 0.115 , AVA = 0.198 , RD = 0.119 , PI = 0.041 , PCCL = 0.142 , IOC = 0.201 , and DSTC = 0.069 . The comparison shows that the hierarchical analysis model is better than the hierarchical random regression model. (3) High-efficiency hybrid model correlation acceleration is the worst model. The experimental data are APC = 0.147 , UPUA = 0.029 , TO = 0.055 , YPUA = 0.06 , PCP = 0.047 , APS = 0.046 , IAND = 0.079 , AVA = 0.136 , RD = 0.082 , PI = 0.028 , PCCL = 0.098 , IOC = 0.139 , and DSTC = 0.048 . (4) The predicted 2020 data and the actual data have small errors. The data obtained by the AHP model is GDP = 1262.1 , finance = 185.09 , budget = 68 , tax = 51.92 , fund budget = 69.23 , transfer income = 40.14 , debt income = 7.73 , disposable financial power = 177.37 , fiscal expenditure = 191.26 , public budget = 88.68 , government expenditure = 71.39 , transfer expenditure = 23.46 , debt expenditure = 7.73 , and last year balance = 2.39 .
Research background: The article describes the main trends in the globalization of economic processes, analyses the economic situation of world commodity markets, considers factors that influence the development of the transport system of Russia, defines the transport component in the cost of bulk cargo transported by Russian railways, presents a conceptual model of its formation for the purpose of improving tariff policy and competitiveness. A study of the dynamics of market conditions helps to identify factors that increase the efficiency of interaction between cargo-forming enterprises and organizations with the transport complex, develop and justify a system of measures to increase the efficiency of the transport system and ensure the sustainability of the national economy, as well as identify factors that contribute to globalization and the integration of production processes of transnational structures into the world economic system. Purpose of the article: To develop a methodological approach to assessing the impact of global commodity markets in the context of the globalization of economic processes on the development of the transport system of Russia. Methods: Analysis, classification, deduction, economic and mathematical modelling. Findings & Value added: The developed conceptual model for assessing the impact of global commodity markets in the context of the globalization of economic processes on the development of the country’s transport system contributes to the improvement of the tariff policy in the field of freight transportation, creates favourable conditions for increasing freight volumes for both transit and export-import cargoes.
This paper aimed to investigate the performance of intermodal terminals located in Southeastern Brazil, verifying their efficiency levels in logistics operations of grain agricultural crop runoff, using data envelopment analysis (DEA). The results show only 25% of terminals with total and pure technical efficiency, which are able, therefore, to use their inputs efficiently, without incurring large losses. The inefficient ones only present pure technical efficiency, suggesting they are likely to present scale inefficiency of operations. The analysis of terminals has shown that a better operational productivity can be obtained in relation to the use of their structure and available resources.
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