Publisher

AHMARAHMAR

Journal ARRUS Journal of Mathematics and Applied ScienceARRUS Journal of Mathematics and Applied Science Abstract

The Covid-19 pandemic has had a significant impact on every industry globally, especially the transportation industry being severely disrupted. With peoples travel demand at an all-time low, maintaining the countrys transportation infrastructure is crucial to ensure the movement of essential goods. However, the Vietnamese governments decision to apply social distancing measures to limit and prevent the spread of the disease has generated extreme challenges in the freight transport industry, which have harmed the Vietnamese economy by restricting levels of business activity, causing the movement and distribution of essential commodities to be halted. Based on the practical needs of transportation enterprises, this study proposes a low-cost freight allocation strategy using mix-integer linear programming approaches to ensure an efficient operation for businesses in order to resume normal operations in the context of the Covid-19 epidemics complex development. The research focuses on developing a mathematical model with an objective function that minimizes total cost and associated constraints. The findings of the study point to the development of a solution that solves businesses problems in determining the optimal transportation plan and assist in making decisions on allocating goods to suitable locations, ensuring the quantity of serving the needs of each district in Ho Chi Minh City, minimizing transportation time while still saving costs. Furthermore, the article aids a decision-making process for other transportation industries.

Conclusion

The Covid-19 pandemic has significantly disrupted global industries, particularly transportation.This research proposed a cost-effective freight allocation strategy utilizing mix-integer linear programming to aid businesses in resuming operations during the pandemic.The developed mathematical model minimizes total costs and provides optimal transportation plans, assisting in efficient resource allocation and decision-making for logistics enterprises.

Future Research

Further research could explore the integration of real-time data, such as traffic conditions and fuel prices, into the optimization model to enhance its responsiveness to dynamic changes. Investigating the resilience of the proposed model under various pandemic scenarios, including different levels of social distancing and supply chain disruptions, would be valuable. Additionally, a study could focus on extending the model to incorporate multiple transportation modes, such as road, rail, and waterways, to provide a more comprehensive and flexible solution for logistics enterprises, ultimately improving the efficiency and adaptability of transportation networks in the face of future crises.

References

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