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Demand-Responsive Transportation Vehicle Routing Optimization Based on Two-Stage Method

Computers, Materials and Continua 2024年第1期81卷 443-469页

作者： Ma J. Liu H. Chen L. School of Railway Transportation Shanghai Institute of Technology Shanghai 201418 China

Demand-responsive transportation (DRT) is a flexible passenger service designed to enhance road efficiency, reduce peak-hour traffic, and boost passenger satisfaction. However, existing optimization methods for initial passenger requests fall short in addressing real-time passenger needs. Consequently, there is a need to develop real-time DRT route optimization methods that integrate both initial and real-time requests. This paper presents a two-stage, multi-objective optimization model for DRT vehicle scheduling. The first stage involves an initial scheduling model aimed at minimizing vehicle configuration, and operational, and CO2 emission costs while ensuring passenger satisfaction. The second stage develops a real-time scheduling model to minimize additional operational costs, penalties for time window violations, and costs due to rejected passengers, thereby addressing real-time demands. Additionally, an enhanced genetic algorithm based on Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is designed, incorporating multiple crossover points to accelerate convergence and improve solution efficiency. The proposed scheduling model is validated using a real network in Shanghai. Results indicate that real-time scheduling can serve more passengers, and improve vehicle utilization and occupancy rates, with only a minor increase in total operational costs. Compared to the traditional NSGA-II algorithm, the improved version enhances convergence speed by 31.7% and solution speed by 4.8%. The proposed model and algorithm offer both theoretical and practical guidance for real-world DRT scheduling. © 2024 The Authors.

关键词： artificial intelligence applications Demand responsive transit genetic algorithm muti-objective optimization

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The Role of artificial intelligence Tax in Reducing the Unemployment Problem

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Journal of Modern Accounting and Auditing 2022年第5期18卷 225-232页

作者： Mohamed Abd Elmenam Elshawarby Elsaleheya Elgadida University El saleheya El Gadida CityEgypt

The fear of the impact of artificial intelligence applications in the labor market on unemployment rates has *** the total global investment in this field did not exceed eight billion dollars in 2015,the global market for artificial intelligence globally will reach about 70 billion dollars by *** economic analysts believe that the application of artificial intelligence in industrial fields in particular will produce factories with much fewer employees than the current number,which will cause an increase in unemployment ***,countries are trying to adapt to these systems,not only at the level of research and development of these systems,but also on the extent of strategic planning for the change they bring about on the economic level in general,and on the labor market in *** of the tools that these countries can use in their attempts to solve this problem is the artificial intelligence tax.

关键词： artificial intelligence applications artificial intelligence tax unemployment problem strategic planning

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