AHMARAHMAR

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

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.

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.

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.

  1. A Two-Echelon Location Routing Problem for Bio-Waste Transportation | IEEE Conference Publication | IEEE... ieeexplore.ieee.org/document/9497293A Two Echelon Location Routing Problem for Bio Waste Transportation IEEE Conference Publication IEEE ieeexplore ieee document 9497293
  2. Hierarchical Multimodal Hub Location With Time Restriction for China Railway (CR) Express Network | IEEE... ieeexplore.ieee.org/document/9047858Hierarchical Multimodal Hub Location With Time Restriction for China Railway CR Express Network IEEE ieeexplore ieee document 9047858
  3. The Optimization of Transportation Costs in Logistics Enterprises during the Covid-19 Pandemic | ARRUS... doi.org/10.35877/mathscience567The Optimization of Transportation Costs in Logistics Enterprises during the Covid 19 Pandemic ARRUS doi 10 35877 mathscience567
  4. View of MULTI-PERIOD MARITIME LOGISTICS NETWORK OPTIMIZATION USING MIXED INTEGER PROGRAMMING. view multi... geomatejournal.com/geomate/article/view/3038/2577View of MULTI PERIOD MARITIME LOGISTICS NETWORK OPTIMIZATION USING MIXED INTEGER PROGRAMMING view multi geomatejournal geomate article view 3038 2577
  5. Computational methods for martingale optimal transport problems. methods martingale optimal transport... projecteuclid.org/journals/annals-of-applied-probability/volume-29/issue-6/Computational-methods-for-martingale-optimal-transport-problems/10.1214/19-AAP1481.fullComputational methods for martingale optimal transport problems methods martingale optimal transport projecteuclid journals annals of applied probability volume 29 issue 6 Computational methods for martingale optimal transport problems 10 1214 19 AAP1481 full
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