Authors :
Shreya.S; Ankita Yadav; Aivarajan S.N; Bharani Kumar Depuru
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/4xumpnzb
DOI :
https://doi.org/10.5281/zenodo.8324108
Abstract :
Transportation cost optimization plays a vital
role in the operations of sand manufacturing companies
that depend on third-party logistics for the efficient
movement of their products. This research focuses on
reducing the total transportation costs for a sand
manufacturing company by selecting the best logistics
supplier using the PuLP library, a popular open-source
Linear Programming tool in Python. The objective is to
minimize the total transportation cost while meeting the
demand requirements of customers by determining the
optimal allocation of sand shipment from the company's
multiple warehouses to various customers while
considering factors such as distance, freight rates, and
capacity constraints. To address this transportation
problem, the PuLP library is employed. PuLP provides a
powerful interface for formulating and solving solver
capabilities to facilitate finding the optimal solution that
minimizes the total transportation cost. By leveraging
the Pulp library, sand manufacturing companies can
optimize their transportation operations and reduce the
total transportation cost associated. The project aims to
provide valuable insights into supplier selection for
improved cost efficiency, enhanced customer service,
and increased operational effectiveness.
Keywords :
Transportation Cost Reduction, Logistics Management, Python Programming, Prescriptive Analytics, Microsoft Solver, Linear Programming.
Transportation cost optimization plays a vital
role in the operations of sand manufacturing companies
that depend on third-party logistics for the efficient
movement of their products. This research focuses on
reducing the total transportation costs for a sand
manufacturing company by selecting the best logistics
supplier using the PuLP library, a popular open-source
Linear Programming tool in Python. The objective is to
minimize the total transportation cost while meeting the
demand requirements of customers by determining the
optimal allocation of sand shipment from the company's
multiple warehouses to various customers while
considering factors such as distance, freight rates, and
capacity constraints. To address this transportation
problem, the PuLP library is employed. PuLP provides a
powerful interface for formulating and solving solver
capabilities to facilitate finding the optimal solution that
minimizes the total transportation cost. By leveraging
the Pulp library, sand manufacturing companies can
optimize their transportation operations and reduce the
total transportation cost associated. The project aims to
provide valuable insights into supplier selection for
improved cost efficiency, enhanced customer service,
and increased operational effectiveness.
Keywords :
Transportation Cost Reduction, Logistics Management, Python Programming, Prescriptive Analytics, Microsoft Solver, Linear Programming.
