Linear programming is a business analytics technique that can lend itself to supporting these types of
business decisions. Linear programming is a mathematical method that optimizes the allocation of
limited resources to achieve a desired objective, subject to a set of constraints1. Linear programming
can help the operations manager to determine the optimum number of vans to purchase, the most
efficient routes and schedule to follow, and the minimum cost or time to shuttle guests to/from the
airport, by formulating a linear objective function and a system of linear inequalities that represent
the relevant variables, parameters, and restrictions2.
The other options are not correct business analytics techniques for these types of business
decisions. Factor analysis is a statistical method that reduces the dimensionality of a large set of
correlated variables into a smaller set of uncorrelated factors that explain the underlying structure or
patterns of the data3. Factor analysis can help the operations manager to identify the key factors
that influence the guest satisfaction or loyalty, but it cannot help to optimize the resource allocation
or efficiency. Regression is a statistical method that estimates the relationship between one or more
independent variables and a dependent variable. Regression can help the operations manager to
predict the demand or revenue of the hotel based on the variables such as season, price, or location,
but it cannot help to optimize the resource allocation or efficiency. K-means clustering is a machine
learning method that partitions a set of data points into a predefined number of clusters based on
the similarity or distance between the data points. K-means clustering can help the operations
manager to segment the guests into different groups based on their characteristics or p
