Identify the two types of decision models mentioned in chapter 1 Select one of the two and provide and discuss three examples it. Using a Goggle search find three examples and briefly explain them. Identify the resource i.e., website URL. timely, accurate, flexible, economical, reliable, easy to understand, and easy to use. lly results in solutions that are 1.2 Types of Decision Models Decision models can be broadly classified into two categories, based on the type and nature of the decision-making problem environment under consideration: (1) deter- ministic models and (2) probabilistic models. We define each type in the following sections. Deterministic Models Deterministic models assume that all the relevant input data values are known with certainty; that is, they assume that all the information needed for modeling a decision- making problem environment is available, with fixed and known values. An example of such a model is the case of Dell Corporation, which makes several different types of PC products (e.g., desktops, laptops), all of which compete for the same resources (e.g., labor, hard disks, chips, working capital). Dell knows the specific amounts of each resource required to make one unit of each type of PC, based on the PC's design specifications. Further, based on the expected selling price and cost prices of various resources, Dell knows the expected profit contribution per unit of each type of PC. In such an environment, if Dell decides on a specific production plan, it is a simple task to compute the quantity required of each resource to satisfy that production plan. For example, if Dell plans to ship 50,000 units of a specific laptop model, and each unit includes a pair of 8.0 GB DDR4 memory chips, then Dell will need 100,000 units of these memory chips. Likewise, it is easy to compute the total profit that will be rea- lized by this production plan (assuming that Dell can sell all the laptops it makes). Perhaps the most common and popular deterministic modeling technique is linear programming (LP). In Chapter 2, we first discuss how small LP models can be set up and solved. We extend our discussion of LP in Chapter 3 to more complex pro- blems drawn from a variety of business disciplines. In Chapter 4, we study how the solution to LP models produces, as a byproduct, a great deal of information useful for managerial interpretation of the results. Finally, in Chapters 5 and 6, we study a few extensions to LP models. These include several different network flow models (Chapter 5), as well as integer, nonlinear, and multi-objective (goal) programming models (Chapter 6)