Areas of Simulation Application

[Page 643 ( continued )]

Simulation is one of the most useful of all management science techniques. The reason for this popularity is that simulation can be applied to a number of problems that are too difficult to model and solve analytically. Some analysts feel that complex systems should be studied via simulation whether or not they can be analyzed analytically because simulation provides such an easy vehicle for experimenting on the system. As a result, simulation has been applied to a wide range of problems. Surveys conducted during the 1990s indicated that a large majority of major corporations use simulation in such functional areas as production, corporate planning, engineering, financial analysis, research and development, marketing, information systems, and personnel. Following are descriptions of some of the most common applications of simulation.

Queuing

A major application of simulation has been in the analysis of queuing systems. As indicated in Chapter 13, the assumptions required to solve the operating characteristic formulas are relatively restrictive . For the more complex queuing systems (which result from a relaxation of these assumptions), it is not possible to develop analytical formulas, and simulation is often the only available means of analysis.

---

[Page 644]

Inventory Control

Most people are aware that product demand is an essential component in determining the amount of inventory a commercial enterprise should keep. Most of the mathematical formulas used to analyze inventory systems make the assumption that this demand is certain (i.e., not a random variable). In practice, however, demand is rarely known with certainty . Simulation is one of the few means for analyzing inventory systems in which demand is a random variable, reflecting demand uncertainty. Inventory control is discussed in Chapter 16.

Production and Manufacturing

Simulation is often applied to production problems, such as production scheduling, production sequencing, assembly line balancing (of work-in-process inventory), plant layout, and plant location analysis. It is surprising how often various production processes can be viewed as queuing systems that can only be analyzed using simulation. Because machine breakdowns typically occur according to some probability distributions, maintenance problems are also frequently analyzed using simulation.

Finance

Capital budgeting problems require estimates of cash flows, which are often a result of many random variables. Simulation has been used to generate values of the various contributing factors to derive estimates of cash flows. Simulation has also been used to determine the inputs into rate of return calculations in which the inputs are random variables , such as market size , selling price, growth rate, and market share.

Marketing

Marketing problems typically include numerous random variables, such as market size and type, and consumer preferences. Simulation can be used to ascertain how a particular market might react to the introduction of a product or to an advertising campaign for an existing product. Another area in marketing where simulation is applied is the analysis of distribution channels to determine the most efficient distribution system.

Public Service Operations

The operations of police departments, fire departments, post offices, hospitals , court systems, airports, and other public systems have all been analyzed by using simulation. Typically, such operations are so complex and contain so many random variables that no technique except simulation can be employed for analysis.

Environmental and Resource Analysis

Some of the more recent innovative applications of simulation have been directed at problems in the environment. Simulation models have been developed to ascertain the impact on the environment of projects such as nuclear power plants, reservoirs, highways, and dams. In many cases, these models include measures to analyze the financial feasibility of such projects. Other models have been developed to simulate pollution conditions. In the area of resource analysis, numerous models have been developed in recent years to simulate energy systems and the feasibility of alternative energy sources.

---

[Page 645]

Management Science Application: Simulating a 10-km Race in Boulder, Colorado

The Bolder Boulder, a popular 10-kilometer race held each Memorial Day in Colorado, attracts many of the world's best runners among its 20,000 participants. The race starts at the Bank of Boulder at the northeastern corner of the city, winds through the city streets , and ends at the University of Colorado's football stadium in the center of the city. As the race grew in size (from 2,200 participants in 1979 to 20,000 in 1985), its quality suffered from overcrowding problems, especially at the finish line, where runners are individually tagged as they finish. Large waiting lines built up at the finish line, causing many complaints from the participants.

To correct this problem, race management implemented an interval-start system in 1986, wherein 24 groups of up to 1,000 runners each were started at 1-minute intervals. While this solution alleviated the problem of street crowding, it did not solve the queuing problem at the finish line.

A simulation model of the race was then developed to evaluate several possible solutionsspecifically, increasing the number of finish line chutes from the 8 used previously to either 12 or 15. The model was also used to identify a set of block-start intervals that would eliminate finish line queuing problems with either chute scenario. Recommendations based on the simulation model were for a 12-chute finish line configuration and specific block-start intervals. The race conducted using the recommendations from the simulation model was flawless. The actual race behavior was almost identical to the simulation results. No overcrowding or queuing problems occurred at the finish line. The simulation model was used to fine-tune the 1986 and 1987 races, which were also conducted with virtually no problems.

Source: R. Farina, et al., "The Computer Runs the Bolder Boulder: A Simulation of a Major Running Race," Interfaces 19, no. 2 (MarchApril 1989): 4855.