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In this context, it is often more important to quickly obtain a solution that is satisfactory as opposed to expending a


In this context, it is often more important to quickly obtain a solution that is satisfactory as opposed to expending a
In this context, it is often more important to quickly obtain a solution that is satisfactory as opposed to expending a lot of effort to determine the optimum one, especially when the marginal gain from doing so is small.
The economist Herbert Simon uses the term satisficing to describe this concept - one searches for the optimum but stops along the way when an acceptably good solution has been found.
At this point, some words about computational aspects are in order.
When applied to a nontrivial, real-world problem almost all of the techniques discussed in this section require the use of a computer.
Indeed, the single biggest impetus for the increased use of O.
methods has been the rapid increase in computational power.Fassilia
United States. Alabama



day: 19.09.2018
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In this context, it is often more important to quickly obtain a solution that is satisfactory as opposed to expending a

Although heuristics could be simple, common-sense, rule-of-thumb type techniques, they are typically methods that exploi
Although heuristics could be simple, common-sense, rule-of-thumb type techniques, they are typically methods that exploit specific problem features to obtain good results.
A relatively recent development in this area are so-called meta-heuristics such as genetic algorithms, tabu search, evolutionary programming and simulated annealing which are general purpose methods that can be applied to a number of different problems.
These methods in particular are increasing in popularity because of their relative simplicity and the fact that increases in computing power have greatly increased their effectiveness.
In applying a specific technique something that is important to keep in mind from a practitioner s perspective is that it is often sufficient to obtain a good solution even if it is not guaranteed to be the best solution.
If neither resource-availability nor time were an issue, one would of course look for the optimum solution.
However, this is rarely the case in practice, and timeliness is of the essence in many instances.Fassilia
United States. Alabama



day: 19.09.2018
views - 61


photo:

In this context, it is often more important to quickly obtain a solution that is satisfactory as opposed to expending a

The third category consists of optimum-seeking techniques, which are typically used to solve the mathematical programs d
The third category consists of optimum-seeking techniques, which are typically used to solve the mathematical programs described in the previous section in order to find the optimum i.
, best values for the decision variables.
Specific techniques include linear, nonlinear, dynamic, integer, goal and stochastic programming, as well as various network-based methods.
A detailed exposition of these is beyond the scope of this chapter, but there are a number of excellent texts in mathematical programming that describe many of these methods and the interested reader should refer to one of these.
The final category of techniques is often referred to as heuristics.
The distinguishing feature of a heuristic technique is that it is one that does not guarantee that the best solution will be found, but at the same time is not as complex as an optimum-seeking technique.Fassilia
United States. Alabama



day: 19.09.2018
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First, there are simulation techniques, which obviously are used to analyze simulation models.
First, there are simulation techniques, which obviously are used to analyze simulation models.
A significant part of these are the actual computer programs that run the model and the methods used to do so correctly.
However, the more interesting and challenging part involves the techniques used to analyze the large volumes of output from the programs; typically, these encompass a number of statistical techniques.
The interested reader should refer to a good book on simulation to see how these two parts fit together.
The second category comprises techniques of mathematical analysis used to address a model that does not necessarily have a clear objective function or constraints but is nevertheless a mathematical representation of the system in question.
Examples include common statistical techniques such as regression analysis, statistical inference and analysis of variance, as well as others such as queuing, Markov chains and decision analysis.Fassilia
United States. Alabama



day: 19.09.2018
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Maximize Profit 10G 9W , subject to 0.
Maximize Profit 10G 9W , subject to 0.
25W 135 G, W 0 and integers.
This mathematical program tries to maximize the profit as a function of the production quantities G and W , while ensuring that these quantities are such that the corresponding production is feasible with the resources available.
At the lowest level one might be able to use simple graphical techniques or even trial and error.
However, despite the fact that the development of spreadsheets has made this much easier to do, it is usually an infeasible approach for most nontrivial problems.
techniques are analytical in nature, and fall into one of four broad categories.Fassilia
United States. Alabama



day: 19.09.2018
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