Spreadsheet Models for Managers


Getting Access to Spreadsheet Models for Managers


If Spreadsheet Models for Managersyou use Excel to model businesses, business processes, or business transactions, this course will change your life. You’ll learn how to create tools for yourself that will amaze even you. Unrestricted use of this material is available in two ways.

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Order "Spreadsheet Models for Managers, on-line edition, one month" by credit card, for USD 69.95 each, using our secure server, and receive download instructions by return email.
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Spreadsheet Models for Managers

Performance measures for single server systems 12/10
Session Links
Probability of n customers in the system Pn = (1 - λ/μ)(λ/μ)n 12.1
Utilization of the server ρ = 1 - P0 = λ/μ 12.2
Probability of more than k customers in the system Pn>k = (λ/μ)k+1 12.3
Average number of customers in the system Ls = λ/(μ - λ) 12.4
Average time a customer spends in the system Ws = 1/(μ - λ) 12.5
Average waiting time Wq = λ/(μ (μ - λ)) 12.6
Average number waiting Lq = λ2/(μ (μ - λ)) 12.7
Average number waiting when there is a line La = μ/(μ - λ) 12.8

As you can see, the convention in these formulas is to use Greek letters to represent some of the parameters of this model. If you’re unfamiliar with the Greek alphabet, this can be a little challenging, but not to worry, there are only three symbols in use here. The Greek letters we use are λ (called “lambda”), for the mean arrival rate; μ (called “mu”), for the mean service rate; and ρ (called “rho”), which is the ratio λ/μ.

By the way, if the mean service rate is less than the mean arrival rate, then customers are arriving faster than they can be serviced, and the queue grows indefinitely. In that situation, the system is not at equilibrium. Thus, since we assume that the system is at equilibrium, we know that the mean service rate is greater then the mean arrival rate, for all systems to which this model applies. That is, ρ < 1.

Last Modified: Wednesday, 27-Apr-2016 04:15:26 EDT

The Power of Simplifying Assumptions

Modeling service systems in general is extraordinarily complex, but as we’ve seen, if we make reasonable approximations, we can gain powerful tools that are very easy to apply. In the case of service systems, we assumed that the system was at equilibrium or close to it. Analogously, we can make simplifying assumptions for many other complex questions. Examples are process control, resource scheduling, resource allocation, cost allocation, vehicle routing, and many more.