Research on Multimodal Transportation Path Optimization with Time Window Based on Ant Colony Algorithm in Low Carbon Background

Yao, Dongxin; Liu, Zhishuo

doi:10.1007/978-981-15-0644-4_77

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Authors in Galvez et al [28] proposed a unique integration technique that combines a heuristic algorithm and exact solution optimization for multimodal petroleum supply chain design. Yao and Liu [29] developed an ant colony algorithm to minimize the total transportation cost involving multimodal transportation path. The results obtained show that the algorithm was able to solve the multimodal path optimization well.…”

Section: Literature Review Multimodal Transportation Problemmentioning

confidence: 99%

Maximizing the Supply Chain Profit in Multimodal Transportation Problem with Transfer Part using Two-Echelon Genetic Algorithm

Johar,

Mohd Zawawi,

Nordin

2024

Mal. J. Fund. Appl. Sci.

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Multimodal transportation is a highly effective method for optimizing deadlines and reducing inventory costs, both of which are crucial in a supply chain environment. This study employs a mathematical programming model to optimize the supply chain profit for multimodal transportation distribution within a specified time window. The model considers five factors, such as production cost, transportation cost, transport time, penalty cost, and sales price. Additionally, a Two-Echelon Genetic Algorithm (TEGA) is proposed to solve the optimization problem, and a numerical example is provided to validate the model and algorithm. The study compares the performance of the proposed algorithm with the exact solution from a previous study, presents implementation details and numerical experiment results, and analyses the findings. The results demonstrate the efficiency and robustness of the algorithm, making it a significant contribution to transportation planning for freight transportation and supply chain management.

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Section: Literature Review Multimodal Transportation Problemmentioning

confidence: 99%

Maximizing the Supply Chain Profit in Multimodal Transportation Problem with Transfer Part using Two-Echelon Genetic Algorithm

Johar,

Mohd Zawawi,

Nordin

2024

Mal. J. Fund. Appl. Sci.

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“…A hard time window (HTW) requires the delivery of the goods to be accomplished within a given time interval expected by the customer. An HTW provides strict bound constraints and reduces the flexibility of the MRP; therefore, it has been modeled in only a few studies [9][10][11][12]. Most MRP studies focusing on time windows prefer to model a soft time window (STW), where violations of the expected time window are allowed and penalized, providing customers with potential economic benefits [2,[13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Modeling a Multimodal Routing Problem with Flexible Time Window in a Multi-Uncertainty Environment

Ge,

Sun,

Zhang

2024

Systems

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In this study, we extend the research on the multimodal routing problem by considering flexible time window and multi-uncertainty environment. A multi-uncertainty environment includes uncertainty regarding the demand for goods, the travel speed of the transportation mode, and the transfer time between different transportation modes. This environment further results in uncertainty regarding the delivery time of goods at their destination and the earliness and lateness caused by time window violations. This study adopts triangular fuzzy numbers to model the uncertain parameters and the resulting uncertain variables. Then, a fuzzy mixed integer nonlinear programming model is established to formulate the specific problem, including both fuzzy parameters and fuzzy variables. To make the problem easily solvable, this study employs chance-constrained programming and linearization to process the proposed model to obtain an equivalent credibilistic chance-constrained linear programming reformulation with an attainable global optimum solution. A numerical case study based on a commonly used multimodal network structure is presented to demonstrate the feasibility of the proposed method. Compared to hard and soft time windows, the numerical case analysis reveals the advantages of the flexible time window in reducing the total costs, avoiding low reliability regarding timeliness, and providing confidence level-sensitive route schemes to achieve flexible routing decision-making under uncertainty. Furthermore, the numerical case analysis verifies that it is necessary to model the multi-uncertainty environment to satisfy the improved customer requirements for timeliness and enhance the flexibility of the routing, and multimodal transportation is better than unimodal transportation when routing goods in an uncertain environment. The sensitivity analysis in the numerical case study shows the conflicting relationship between the economic objective and the reliability regarding the timeliness of the routing, and the result provides a reference for the customer to find a balance between them.

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Research on Multimodal Transportation Path Optimization with Time Window Based on Ant Colony Algorithm in Low Carbon Background

Cited by 2 publications

References 6 publications

Maximizing the Supply Chain Profit in Multimodal Transportation Problem with Transfer Part using Two-Echelon Genetic Algorithm

Maximizing the Supply Chain Profit in Multimodal Transportation Problem with Transfer Part using Two-Echelon Genetic Algorithm

Modeling a Multimodal Routing Problem with Flexible Time Window in a Multi-Uncertainty Environment

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