Using Nonlinear Programming and Queuing in Quantitative Decision Making

102500 The subsection on Forecasting has a detailed discussion on forecasting techniques. The subsection on Queuing tackled different queuing systems. The subsections end with the author’s take on how managers can benefit from each model and how the particular methodology addresses actual real-world situations. Managers have the daunting task of making a multitude of decisions every day for the respective institutions that they head. Depending on the nature of the variables that a particular situation entails, some decisions are arrived at quite straightforwardly while others need to undergo a series of rigorous processes before they are made. Among these challenging yet indispensable methods are Nonlinear Programming, Decision Analysis, Forecasting, and Queuing. With Hillier and Hillier (2010) as its main reference, the subsections that follow will discuss these methods in detail. According to Feiring (1986), Linear Programming is a part of mathematical programming that deals with the competent and effective allocation of limited resources to a number of known activities to obtain the desired goal, which, most commonly concerns maximizing profit or minimizing cost. It is linear in the sense that the criterion (objective function or index) and the constraints (operating rules) of the process can be expressed as linear formulas. When at least one of these formulas is nonlinear in nature, then Nonlinear Programming is used. As a result, while Linear Programming assumes a proportional relationship between activity levels and an overall measure of performance, Nonlinear Programming is used to model nonproportional relationships. The graph of a piecewise linear function consists of a sequence of connected line segments. Thus, the slope of the profit graph remains the same within each line segment but then decreases at the kink where the next line segment begins.