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Research on optimization of multimodal hub-and-spoke transport network under uncertain demand

Zhang Jianpeng, Li Huijun, Han Wei, Li Yuanping

Archives of Transport

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2024

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Vol. 70, iss. 2

137--157

EN

In the cargo transportation system, the hub-and-spoke transport network can make full use of the scale effect between logistics hubs and reduce logistics costs. Joint transportation of multiple modes of transportation can give full play to the advantages of different modes of transportation, which not only reduces logistics costs but also improves transportation efficiency. Therefore, this paper combines the advantages of multimodal transportation and hub-and-spoke network, and establishes an optimization model of multimodal hub-and-spoke transport network under demand uncertainty. The model takes into account hub capacity constraints and customer satisfaction with respect to transportation time, and to facilitate the model solution, we utilize the fuzzy expected value method and the fuzzy chance constraints based on credibility to clarify the uncertain variables in the model. We use mixed coding to describe the selection of hubs, assignment of nodes, and choice of transportation modes in this study and use the NSGA-II algorithm with local reinforcement to solve the problem. Finally, numerical experiments are designed to verify the validity of the model and algorithm through sensitivity analysis of relevant parameters, determine the reasonable number of hubs and confidence level, and obtain the influence of the change of hub capacity limit and the ratio of single and double hub transit on the research objectives. The results show that: the NSGA-II algorithm with local reinforcement can significantly improve the convergence speed of the algorithm; There is benefit inversion between economic cost and time cost, and the pursuit of economic cost minimization and time cost minimization, respectively, will lead to different choices of the number of hubs; Increasing the ratio of goods transfer between hubs is beneficial for fully utilizing the scale effect between hubs, achieving the goal of reducing economic costs, but at the same time, it will increase time costs.

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Optimized Design of Multi-Level Low-Carbon Logistics Distribution Scheme Based on Two Stages

Fu Zhongning, Yue Jintian, Yan Yunzhu

Archives of Transport

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2024

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Vol. 69, iss. 1

145--165

EN

The logistics network, as a key component of commodity distribution, has a direct impact on carbon emissions and resource utilization. Its main objective is to optimize the distribution process of commodities in order to improve efficiency, reduce costs, ensure timely delivery of commodities, and simultaneously satisfy customers' needs. The problem of multiple factors in the optimal allocation of logistics network objectives leading to decision-making difficulties is addressed. The complex multilevel logistics network optimization problem is decomposed into two stages. The first stage determines the selection of cargo transit points and the distribution of cargo flow between nodes, starting with the establishment of a Comprehensive Modal Emission Model (CMEM) taking into account the speed of the vehicle, the amount of cargo loaded, the road surface conditions and the characteristics of the vehicle itself. Secondly, the carbon emission cost generated from the flow of goods, together with the transportation cost, distribution cost and fixed cost at the transit point, constitute the comprehensive cost, and establish a multi-objective optimization model of low-carbon logistics network with the goal of minimizing the comprehensive cost and transportation time. The Non-dominated Sorted Genetic Algorithm with Elite Strategies (NSGA-II) is used for the solution. Finally, MATLAB soft ware was used to numerically analyze the two schemes of "Considering Carbon Tax Levy" and "Not Considering Carbon Tax Levy". The results show that the government's imposition of an environmental tax on companies will change the distribution of transit points and flows within the logistics network, reducing CO2 emissions by 226.5 kg and saving 257.65 CNY in comprehensive costs. The second stage determines the order and path of distribution from each transit point to its own customers, establishes a low-carbon logistics network distribution path optimization model with the goal of minimizing the cost of carbon emissions, and solves the problem using Genetic Algorithm (GA). Through the coordinated use of the two-stage optimization model, it provides enterprises with a network distribution solution that takes into account the low-carbon goal and logistics efficiency, and provides the government with a basis for carbon tax levy and a reference for the tax rate.

3

Model optymalizacji tras przejazdowych jako narzędzie zmniejszenia kosztów logistycznych

Baryła-Paśnik M., Piekarski W., Kuranc A., Piecak A., Ignaciuk S., Wawrzosek J.

Logistyka

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2015

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nr 5

41--45, CD1

PL

W pracy przedstawiono problemy dotyczące sposobu modelowania tras przejazdowych w zakładach produkcyjnych. Podkreślono istotę właściwego doboru modelu w kontekście zmniejszenia kosztów logistycznych ponoszonych w zakładach. W artykule zawarty jest również przegląd i krótka charakterystyka po-szczególnych już istniejących modeli.

EN

This paper presents a method of modeling problems of crossing paths in manufacturing plants. It high-lights the essence of proper selection of the model in the context of reducing logistics costs incurred in the plants. The article also included an overview and a short description of the different existing models. modeling of crossing paths, reduce costs, manufacturing plants.