Monte Carlo Optimization for Beginners

The Monte Carlo method is a well-known simulation technique that uses statistical random sampling to solve mathematical problems. In use for about 85 years, many variants exist across a wide range of disciplines. If not familiar, I suggest reading this Wiki page.

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Managing Bottlenecks in Business to increase profitability

The key to increasing profitability is to identify which processes in your operations are or may become bottlenecks in the flow of work. Bottlenecks in business are the points where potential changes can have the most dramatic impact on the bottom line. While non-bottlenecks are important to consider from a cost accumulation standpoint, the majority of a manager’s focus should remain on the bottlenecks.

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Why Optimization Modeling Should Move Beyond Using Scripts

In the first decade of my career, most optimization models that I authored also required one or more scripts. They were necessary to automate tasks such as importing data, setting parameters, solving the model, executing another program (e.g., to crunch numbers), generating reports, and so on. Most were written in DOS shell, with an occasional UNIX shell depending on the platform used.

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Top 5 Modeling Capabilities to Achieve Optimization in Manufacturing

Enterprise Optimization™ in manufacturing has become increasingly beneficial for corporations in recent years, as software and our understanding of what makes companies tick have improved in tandem—but all the computing power and theoretical knowledge in the world won’t guarantee accurate, useful modeling for your optimization efforts. To get it right, and secure a real improvement in your company, you need to work with a solution which offers you several key factors—starting with the five we’re going to discuss here.

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Optimizing Capex Decisions for Aging Assets

We here at River Logic are proud to announce a new resource aging feature now offered in Enterprise Optimizer® (EO), our modeling and analytics platform built to increase performance through fully integrated decision-making. Why have we decided to add this new feature you, might ask.  For those familiar with EO, there are many types of resource decision making capabilities that are already supported. These include:

    • Capacity Planning – evaluating production capacity needs for present and future periods, e.g. how many resources/production lines do I need to run and when?
    • Production Planning – allocating production requirements to existing capacity, e.g. what products should I make on what resources?
    • Resource Buy/Sell – evaluating major capital investment decisions to buy or sell individual resources.
    • Facility/Location Open/Close – evaluating decisions to open/close entire physical locations and plants
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Are You Correctly Defining Time Periods in Your Optimization Modeling?

When designing an enterprise model for planning purposes — one that accounts for strategic planning, financial planning and operations — several decisions are required. One of the most important is how to define time. The assumption of time definition has enormous implications for the type of questions that can be answered with the model and the value derived from such answers.

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Speed and Flexibility: The Key to Effective Logistics Modeling

Last spring in Portland, Oregon, the two primary container shipping companies with regularly scheduled service abruptly stopped calling, partly due to a longshoreman strike at the time. The impact was fast; the fallout substantial.

Whereas containers used to be transported directly to the port terminal, they are now transported (mostly) by truck to Tacoma, Seattle, Oakland or ports further way. Many companies — such as Nike and Columbia Sportswear — were affected, but agriculture-based companies — such as Lamb Weston’s potato business and other companies selling commodities like wheat, onions, and corn — were severely affected. Shipping the same products to the same customers overseas now costs between $600 and $1,000 more per container. By some estimates, there are approximately 2,000 more truckloads on the highways every week.

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A Multi-Purpose Approach to Logistics Modeling

About a decade ago, I would occasionally watch the TV show Good Eats. I’m not a chef; I’m not even much of a cook. I can still burn microwaved popcorn. Truth is, I just enjoyed the science aspect of the show. You might be asking “What does a food show have to do with logistics, besides the obvious issues of transporting and storing food?”

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Why Simulation is a Must for Optimization-based Scenario Planning

In the age of Big Data, corporate leaders have more power at their disposal than ever before. Meanwhile, globalization has increased the number and quality of competitors fighting for the same customers. There is enormous pressure on planners to achieve economical, efficient strategies that increase return on investment with as little risk as possible. In the past, intuition was a planners’ tool of choice, but most today are tech savvy enough to have moved onto better techniques. With the sheer number of variables to consider, there are not enough hours in the day to cope with the resultant information overload.

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A Code Free Approach to Stochastic Programming

This is the second of a two-part series on stochastic optimization, defined in Wikipedia as “optimization methods that generate and use random variables. For stochastic problems, the random variables appear in the formulation of the optimization problem itself, which involve random objective functions or random constraints, for example.”

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Supply Chain Brief