Integrated scheduling of intermodal transportation with seaborne arrival uncertainty and carbon emission

Wang, Wenyuan; Xu, Xinglu; Jiang, Ying; Xu, Yunzhuo; Cao, Zhen; Liu, Suri

doi:10.1016/j.trd.2020.102571

Cited by 20 publications

(6 citation statements)

References 28 publications

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“…However, it is acknowledged that addressing numerous sources of scientific uncertainty remains a crucial focus for further research in this domain. Other similar works that need to be taken into account in the context of the complexity of the topic of climate change are those of [42,43].…”

Section: Discussionmentioning

confidence: 99%

Future Cities Carbon Emission Models: Hybrid Vehicle Emission Modelling for Low-Emission Zones

Mądziel

2023

Energies

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Current emission models primarily focus on traditional combustion vehicles and may not accurately represent emissions from the increasingly diverse vehicle fleet. The growing presence of hybrid and electric vehicles requires the development of accurate emission models to measure the emissions and energy consumption of these vehicles. This issue is particularly relevant for low-emission zones within cities, where effective mobility planning relies on simulation models using continuously updated databases. This research presents a two-dimensional emission model for hybrid vehicles, employing artificial neural networks for low-emission zones. The key outcome is the methodology developed to create a CO2 emission model tailored for hybrid vehicles, which can be used to simulate various road solutions. The CO2 emission model achieved an R2 coefficient of 0.73 and an MSE of 0.91, offering valuable information for further advancements in emission modelling.

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Section: Discussionmentioning

confidence: 99%

Future Cities Carbon Emission Models: Hybrid Vehicle Emission Modelling for Low-Emission Zones

Mądziel

2023

Energies

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“…The chance-constrained programming approach then transforms the fuzzy constraints, i.e., Equations ( 9) and (10), into their respective chance constraints. Based on the possibility measure [21], the chance constraint reformulations of Equations ( 8) and ( 10) are shown as Equations ( 17) and (18), respectively.…”

Section: Model Processingmentioning

confidence: 99%

“…Hrusovsky et al [9] developed a hybrid simulation and optimization approach for a green multimodal routing problem considering the same uncertainty as Guo et al [8]. Wang et al [10] addressed a green multimodal scheduling problem considering seaborn arrival uncertainty. In the existing literature represented by the above studies, the carbon emission factors of transportation activities are the key elements to calculate the emissions of the multimodal route(s) and are modeled as deterministic parameters.…”

Section: Introductionmentioning

confidence: 99%

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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“…According to the emission limit of freight orders through intermodal transportation network, Heinold et al [33] proposed a MIP formulation to make decisions on the optimized order routing, transportation mode selection and emission allocation. Wang et al [34] presented an integrated production and distribution model for multimodal transport considering the uncertain delay of marine arrival, and designed an improved multiple heterogeneous coded genetic algorithm. They also analyzed the impact of different operation strategies on carbon emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Hierarchical Multimodal Hub Location Problem with Carbon Emissions

Wang

2023

Sustainability

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The increasing demand for multimodal freight transportation makes the expansion of new secondary hubs necessary. The carbon tax has also received attention from various sectors due to the development requirements of green transportation. Therefore, this study proposed a multimodal transport hub location problem, where carbon emissions are specifically considered to achieve green strategic planning. A mixed integer non-linear programming model was formulated to determine the location of newly-built secondary hubs, the distribution of cargo flow and the modes of the transportation links. A tangible adaptive genetic algorithm (AGA) is designed to effectively search for the optimal solution. The result of the secondary hub location based on Ningbo Port shows that the additional secondary hub brought a 41.46% increase in cargo flow, a 2.2% saving in transportation time and a 2.35% saving in total cost. Furthermore, the sensitivity analysis of the carbon tax reveals that a higher carbon tax increases the transport time, and more secondary hubs are needed to reduce the average cargo transport volume and save the total cost.

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Integrated scheduling of intermodal transportation with seaborne arrival uncertainty and carbon emission

Cited by 20 publications

References 28 publications

Future Cities Carbon Emission Models: Hybrid Vehicle Emission Modelling for Low-Emission Zones

Future Cities Carbon Emission Models: Hybrid Vehicle Emission Modelling for Low-Emission Zones

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

Hierarchical Multimodal Hub Location Problem with Carbon Emissions

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