Minimization of transportation costs using linear programming

Abstract

This paper intricately explores the utilization of Linear Programming (LP), a distinguished mathematical optimization technique, within the logistics and transportation sector, a domain persistently pursuing methods to bolster efficiency and curtail expenses. LP, characterized by its adeptness in optimizing a linear objective function subject to linear constraints, emerges as a pragmatic solution. This exposition elucidates how LP can be harnessed to ascertain the most economically viable transportation routes and quantities of goods transported from warehouses to consumers. A case study is introduced to spotlight the pragmatic applicability of LP in authentic scenarios, and the potential fiscal savings corporations can realize through its adoption. While recognizing the merits of LP, such as clarity, versatility, and computational efficacy, the paper also sheds light on its limitations, involving its dependency on certain presumptions, a necessity for precise data, and its concentration on single-objective optimization. The discourse concludes by prospecting the future of LP in transportation, exploring its amalgamation with artificial intelligence, machine learning, multi-objective optimization, and green logistics, intending to underscore the significance of LP in transportation and furnish insights for ensuing research and application.