Ms. Lu Jia Cao Profile

Lu Jia Cao

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Proceedings Article | 14 June 2023 Paper

Optimization of low-carbon multimodal transport paths in an ambiguous demand environment

Ying Shao, Xiao Long Han, Lu Jia Cao

Proceedings Volume 12708, 127082Q (2023) https://doi.org/10.1117/12.2684014

KEYWORDS: Carbon, Transportation, Genetic algorithms, Fuzzy logic, Algorithms, Mathematical modeling, Mathematical optimization, Roads, Uncertainty analysis, Engineering

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The study of low-carbon multimodal transport path optimization problems considered in a fuzzy demand environment has important theoretical and practical significance in the situation of high-quality development. By analysing the demand uncertainty problem in the transport process, an improved simulated annealing genetic algorithm is designed to solve the model. The impact of various carbon policies on multimodal transport solutions, costs and carbon emissions is analysed through arithmetic examples. The results show that: 1) the improved simulated annealing genetic algorithm is better than the traditional genetic algorithm in terms of time finding and effect finding to achieve the lowest cost and lowest carbon emission; 2) the carbon tax policy is studied through the example and it is found that the carbon tax constraint is relatively lenient and the improper setting of carbon tax will lead to the increase of total cost; the model and algorithm proposed in this paper can provide theoretical support to the policy making departments and multimodal transport enterprises to optimize transport solutions. The model and algorithm proposed in this paper can provide a theoretical basis for policy making authorities and multimodal transport enterprises to optimize transport solutions.

Proceedings Article | 16 February 2023 Paper

Optimization of multimodal transport path considering carbon policy

Ying Shao, Xiao Long Han, Lu Jia Cao

Proceedings Volume 12591, 125912V (2023) https://doi.org/10.1117/12.2668501

KEYWORDS: Carbon, Transportation, Genetic algorithms, Algorithms, Mathematical optimization, Roads, Mathematical modeling, Lithium, Engineering, Design and modelling

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The study of multimodal transport path optimization considering carbon policy is of great theoretical and practical significance in the situation of high-quality development. By establishing a multi-objective multimodal transportation path optimization base model considering transportation cost, time cost and carbon emission cost, and a model considering three carbon policies: mandatory carbon emission, carbon tax and carbon trading, and designing an improved simulated annealing genetic algorithm to analyze the cases to compare the multimodal transportation solutions and costs under different carbon policies. The results show that: 1) the improved simulated annealing genetic algorithm can achieve the lowest cost and lowest carbon emission than the traditional genetic algorithm in terms of time search and effect search and can obtain more appropriate transportation solutions for intermodal transport operators. 2) The comparison of carbon policies by example shows that the mandatory carbon emission policy has a strong constraint effect, while the carbon tax and carbon trading have relatively loose constraints. 3) The choice of different carbon policies can achieve the goal of controlling carbon emissions and reducing total costs. The model and algorithm proposed in this paper can provide a theoretical basis for administrative departments and logistics service providers to optimize transportation solutions.

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