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Fuzzy Logic in Solving Logistics Optimization Problems

Quickstudy by Russell Kay. Computerworld (August 30, 2004). “The digital computing world is built on a structure of Boolean logic applied to binary values — one or zero, yes or no, in or out. But this powerful structure is a gross oversimplification of the real world, where many shades of gray exist between black and white. In everyday life, we use quasimetric notions that are clearly related to numerical concepts or values but lack precision or demarcation. … The real world simply doesn’t map well to binary distinctions, and numerical precision is often unhelpful in making qualitative statements. Fuzzy logic gives us a way to deal with such situations. In fuzzy systems, values are indicated by a number (called a truth value) in the range from 0 to 1, where 0.0 represents absolute falseness and 1.0 represents absolute truth. While this range evokes the idea of probability, fuzzy logic and fuzzy sets operate quite differently from probability.”

Fuzzy Logic has a wide range of application especially in solving Logistics Optimization Problems which are widely found in most Accounting Software that integrates Logistics Management. A sample  application is the distribution centers location problem which is concerned with how to select distribution centers from the potential set so that the total relevant cost is minimized. Other applications which Fuzzy Logic is used to solve the problems are depot location-allocation problems, vehicle routing problems, and inventory control problems which are three key problems in optimizing logistics distribution systems.

Fuzzy Logic requires some numerical parameters in order to operate such as what is considered significant error and significant rate-of-change-of-error, but exact values of these numbers are usually not critical unless very responsive performance is required in which case empirical tuning would determine them. Fuzzy Logic was conceptualized by  Lotfi Zadeh, a professor at the University of California at Berkley. And today, his work can be found in major Software and Hardware that solves optimization problems.

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One Response to “Fuzzy Logic in Solving Logistics Optimization Problems”

  1. How does fuzzy logic solve the logistic optimization problem? It’s not obvious to me.

    I have heard the one about air conditioners - from Bart Kosko’s book.

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