The primary objective of many discrete-event simulation projects is system investigation. Output data from the simulation model are used to better understand the operation of the system (whether that system is real or theoretical), as well as to conduct various "what-if"-type analyses. However, I recently worked on another model

## Tag: **optimization**

In the traveling salesman problem (TSP), a salesman must minimize travel distance while visiting each of a given set of cities exactly once. Recently, the TSP has generated some buzz in the popular media, after a blog post by Randy Olson. The tour shown was not quite optimal, and Bill

♦We learned this week that SAS is ranked #4 on Fortune's 100 Best Companies to Work For in 2015. This makes six straight years ranking in the top four (including twice at #1). ♦The March/April 2015 issue of Analytics Magazine includes a SAS company profile by my colleague Kathy Lange. As

Suppose someone needs a kidney transplant and a family member is willing to donate one. If the donor and recipient are incompatible (because of blood types, tissue mismatch, and so on), the transplant cannot happen. Now suppose two donor-recipient pairs A and B are in this situation, but donor A

Why do people steal ATMs? Because that's where the money is!!! While the old "smash-n-grab" remains a favorite modus operandi of would-be ATM thieves, the biggest brains on the planet typically aren't engaged in such endeavors (see Thieves Steal Empty ATM, Chain Breaks Dragging Stolen ATM, An A for Effort). And of

Just yesterday, Santa called my cell phone asking for a favor... Yes, Santa has my direct line, and I owe him (he once did me a solid, back in 1984, for Christmas, scoring me an awesome Optimus Prime Transformer). That's me there in the front - sporting plaid duds and

Do you have an Uncle Louie? Yep - we all do! You know what I mean - this guy: When my wife and I were planning to get married, we had all sorts of big decisions to make. Where would our future home be? How many kids would we have?

Nonlinear optimization routines enable you to find the values of variables that optimize an objective function of those variables. When you use a numerical optimization routine, you need to provide an initial guess, often called a "starting point" for the algorithm. Optimization routines iteratively improve the initial guess in an

Oil companies are being forced to explore in geologically complex and remote areas to exploit more unconventional hydrocarbon deposits. New engineering technology has pushed the envelope of previous upstream experience. No guidebook existed on how computing methodologies can contribute to E&P performance at reduced risk. Until now. A new book

Last week I showed how to find parameters that maximize the integral of a certain probability density function (PDF). Because the function was a PDF, I could evaluate the integral by calling the CDF function in SAS. (Recall that the cumulative distribution function (CDF) is the integral of a PDF.)

SAS programmers use the SAS/IML language for many different tasks. One important task is computing an integral. Another is optimizing functions, such as maximizing a likelihood function to find parameters that best fit a set of data. Last week I saw an interesting problem that combines these two important tasks.

I’m not a coupon junkie. In fact, I feel like a coupon victim. Hardly a day goes by that I don’t get an avalanche of offers from: Email. Snail mail (in catalogs, flyers, coupon books, etc.). In-store flyers. Mobile apps. Store checkout. The vast majority get tossed in my garbage

Once I was chairing a conference where the speaker was explaining the business model for the licensing of the Peanuts cartoon characters - Charlie Brown, Snoopy and the gang - and how all that works when it comes to the balloons for the Macy’s Thanksgiving Day Parade (in case you are wondering,

The truncated normal distribution TN(μ, σ, a, b) is the distribution of a normal random variable with mean μ and standard deviation σ that is truncated on the interval [a, b]. I previously blogged about how to implement the truncated normal distribution in SAS. A friend wanted to simulate data

Dateline: October 4, 2012 – Facebook reaches one billion users! One billion people connected on a single platform; one-seventh of the world’s population. If you assume 40,000 BCE as the start of modern humans, it took the planet 41,804 years to reach a population level of one billion; it took

The April 2012 issue of ORMS Today contains a piece on "How analytics enhance the guest experience at Walt Disney World," by Pete Buczkowski and Hai Chu. While many of us are used to forecasting just one or two things (such as unit sales or revenue), Pete and Hai illustrate

I previously wrote about using SAS/IML for nonlinear optimization, and demonstrated optimization by maximizing a likelihood function. Many well-known optimization algorithms require derivative information during the optimization, including the conjugate gradient method (implemented in the NLPCG subroutine) and the Newton-Raphson method (implemented in the NLPNRA method). You should specify analytic

A popular use of SAS/IML software is to optimize functions of several variables. One statistical application of optimization is estimating parameters that optimize the maximum likelihood function. This post gives a simple example for maximum likelihood estimation (MLE): fitting a parametric density estimate to data. Which density curve fits the

Decision management expert James Taylor wins the prize for most prolific blogger from The Series. James gives us thorough summaries of great presentations on: Balancing Intuition and Analytics in Decision Making. Analytics & Innovation, Analytics in the Executive Suite. SAS Media Day customer panels on fraud detection. and optimization. By