Modeling carbon emission estimation for hinterland-based container intermodal network

Guo, Xiaoyan; He, Junliang; Mo, Lan; Yu, Hao; Yan, Wei

doi:10.1016/j.jclepro.2022.134593

Cited by 23 publications

(17 citation statements)

References 41 publications

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“…This study formulates the uncertainty of both capacities and carbon emission factors of the multimodal network when modeling multimodal routing. We use a = a (1) , a (2) , a (3) to denote an uncertain parameter. When a is a random parameter in stochastic programming, a (1) and a (3) determine the support of the random parameter, and a (2) can be presented by the expectation.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…We use a = a (1) , a (2) , a (3) to denote an uncertain parameter. When a is a random parameter in stochastic programming, a (1) and a (3) determine the support of the random parameter, and a (2) can be presented by the expectation. However, considering the limitations of stochastic programming, this study uses fuzzy programming to deal with the routing problem under twofold uncertainty, in which a represents a triangular fuzzy parameter.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…However, considering the limitations of stochastic programming, this study uses fuzzy programming to deal with the routing problem under twofold uncertainty, in which a represents a triangular fuzzy parameter. In this case, a (1) and a (3) are the minimum and maximum possible values of a, respectively, and a (2) is its most likely value. a (1) and a (3) reflect two extreme conditions and appear in practice at a low possibility, while a (2) represents the condition of a in most cases.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Currently, transportation accounts for 9.17% of carbon emissions in China, and road transportation accounts for 72.3% of total transportation emissions [1]. The transportation industry has been the key to promoting China's 'Dual Carbon' strategy.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Chance-Constrained Optimization for a Green Multimodal Routing Problem with Soft Time Window under Twofold Uncertainty

Li,

Sun,

et al. 2024

Axioms

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This study investigates a green multimodal routing problem with soft time window. The objective of routing is to minimize the total costs of accomplishing the multimodal transportation of a batch of goods. To improve the feasibility of optimization, this study formulates the routing problem in an uncertain environment where the capacities and carbon emission factors of the travel process and the transfer process in the multimodal network are considered fuzzy. Taking triangular fuzzy numbers to describe the uncertainty, this study proposes a fuzzy nonlinear programming model to deal with the specific routing problem. To make the problem solvable, this study adopts the fuzzy chance-constrained programming approach based on the possibility measure to remove the fuzziness of the proposed model. Furthermore, we use linear inequality constraints to reformulate the nonlinear equality constraints represented by the continuous piecewise linear functions and realize the linearization of the nonlinear programming model to improve the computational efficiency of problem solving. After model processing, we can utilize mathematical programming software to run exact solution algorithms to solve the specific routing problem. A numerical experiment is given to show the feasibility of the proposed model. The sensitivity analysis of the numerical experiment further clarifies how improving the confidence level of the chance constraints to enhance the possibility that the multimodal route planned in advance satisfies the real-time capacity constraint in the actual transportation, i.e., the reliability of the routing, increases both the total costs and carbon emissions of the route. The numerical experiment also finds that charging carbon emissions is not absolutely effective in emissions reduction. In this condition, bi-objective analysis indicates the conflicting relationship between lowering transportation activity costs and reducing carbon emissions in routing optimization. The sensitivity of the Pareto solutions concerning the confidence level reveals that reliability, economy, and environmental sustainability are in conflict with each other. Based on the findings of this study, the customer and the multimodal transport operator can organize efficient multimodal transportation, balancing the above objectives using the proposed model.

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Section: Problem Descriptionmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Chance-Constrained Optimization for a Green Multimodal Routing Problem with Soft Time Window under Twofold Uncertainty

Li,

Sun,

et al. 2024

Axioms

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show abstract

“…Tao and Wu (2021) introduced "yard-door-port" into a generalized analytical framework to analyze the carbon emissions from the movement of loaded containers and the repositioning of empty containers. Guo et al (2022) constructed a carbon emission estimation model of a container multimodal transport network based on the hinterland and carried out a case study of Shanghai Port and the hinterland of the Yangtze River Delta. Olgay (2023) estimated the total carbon emissions generated by container handling equipment used in container port operations and planned carbon emission reduction strategies related to climate change adaptation policies.…”

Section: Literature Reviewmentioning

confidence: 99%

A study on the influence of reposition threshold on low-carbon empty container repositioning strategy under an uncertain environment

Tang

Wang

et al. 2023

Front. Mar. Sci.

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The optimization of empty container repositioning nets has become an essential problem in low-carbon port cooperation. This paper proposed three optimization models of multi-port low-carbon empty container repositioning considering threshold under input and output of empty containers as random variables. Non repositioning strategy means the highest threshold, and complete-repositioning strategy means the lowest threshold; threshold-repositioning strategy is in the middle. The probability of empty-container inventory in each port and the storage cost, repositioning cost, lease cost, and carbon emission cost of empty containers are calculated. This paper mainly compares each cost of three models. The results have shown that: (1) Compared with the non repositioning strategy, the threshold-repositioning strategy and complete-repositioning strategy can reduce the ports storage costs and lease costs of empty containers and also reduce carbon emissions. The lower the repositioning threshold of empty containers between ports is, the more obvious the advantages of the threshold-repositioning strategy become. (2) When the cost of storage per empty container increases, under three strategies, the total cost, storage cost, lease cost, and carbon emission cost of the port will all increase. The ports proportion of dependence on its own empty-container storage will decrease, and the proportion of dependence on other ports and leasing companies will both increase.

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Cybersecurity Considerations for the Design of an AI-Driven Distributed Optimization of Container Carbon Emissions Reduction for Freight Operations

Paternina-Arboleda,

Nestler,

Kascak

et al. 2023

Lecture Notes in Computer Science

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Modeling carbon emission estimation for hinterland-based container intermodal network

Cited by 23 publications

References 41 publications

Chance-Constrained Optimization for a Green Multimodal Routing Problem with Soft Time Window under Twofold Uncertainty

Chance-Constrained Optimization for a Green Multimodal Routing Problem with Soft Time Window under Twofold Uncertainty

A study on the influence of reposition threshold on low-carbon empty container repositioning strategy under an uncertain environment

Cybersecurity Considerations for the Design of an AI-Driven Distributed Optimization of Container Carbon Emissions Reduction for Freight Operations

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