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Although there are still large scale problems whose solution requires the use of mainframe computers or powerful worksta


Although there are still large scale problems whose solution requires the use of mainframe computers or powerful worksta
Although there are still large scale problems whose solution requires the use of mainframe computers or powerful workstations, many big problems today are capable of being solved on desktop microcomputer systems.
There are many computer packages and their number is growing by the day that have become popular because of their ease of use and that are typically available in various versions or sizes and interface seamlessly with other software systems; depending on their specific needs end-users can select an appropriate configuration.
Many of the software vendors also offer training and consulting services to help users with getting the most out of the systems.
Some specific techniques for which commercial software implementations are available today include optimization mathematical programming including linear, nonlinear, integer, dynamic and goal programming , network flows, simulation, statistical analysis, queuing, forecasting, neural networks, decision analysis, and PERT CPM.
Also available today are commercial software systems that incorporate various O.
techniques to address specific application areas including transportation and logistics, production planning, inventory control, scheduling, location analysis, forecasting, and supply chain management.Fassilia
United States. Alabama



day: 19.09.2018
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Although there are still large scale problems whose solution requires the use of mainframe computers or powerful worksta

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
views - 187


photo:

Although there are still large scale problems whose solution requires the use of mainframe computers or powerful worksta

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 - 179


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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
views - 172


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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
views - 174


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