This chapter focuses on route selection by simultaneously evaluating the
safety performance and transportation cost associated with each alternative path. It
introduces the concept of a Cost–Safety Matrix as a tool for risk analysis of
transportation routes. In this framework, the cost matrix quantifies the transportation
expenditure for each route, whereas the safety matrix characterizes the safety-related
attributes of those routes. The combined cost–safety matrix incorporates both the
economic considerations and the inherent risk present on each individual path. The
corresponding risk matrix is developed using a defined numerical scale derived from
historical (past) data. The cost of transportation and risk level combine to make a
matrix that can be divided into three parts. The categories used to describe the danger
level are low, moderate, and high. This matrix may be used to pick a route before
commencing the transport. Integrating Artificial Intelligence (AI) into material
handling procedures is critical for increasing productivity and maintaining worker
safety. The primary purpose of this study is to identify and reduce potential dangers
associated with lifting, transporting, and handling products. The primary focus is on
using AI to estimate the danger of material handling jobs. The study uses machine
learning techniques, data analytics, and sensor technologies to examine large datasets
relevant to material handling processes. By harnessing the power of AI, the research
aims to develop a robust risk assessment framework that can automatically detect,
predict, and mitigate potential risks associated with falling materials, collapsing loads,
improper lifting techniques, and struck-by hazards.
Keywords: Artificial intelligence, Cost-safety matrix, Material handling, Risk analysis, Transportation.
