By Prof. H. A. Eiselt, Prof. C.-L. Sandblom (auth.)
The function of this booklet is to supply readers with an advent to the fields of choice making, situation research, and undertaking and computing device scheduling. the combo of those themes isn't really an coincidence: determination research can be utilized to enquire selection seenarios normally, situation research is without doubt one of the major examples of determination making at the strategic Ievel, venture scheduling is usually concemed with determination making at the tactical Ievel, and computer scheduling offers with choice making at the operational Ievel. a number of the chapters have been initially contributed by way of diverse authors, and now we have made each try to unify the notation, sort, and, most significantly, the Ievel of the exposition. just like our publication on Integer Programming and community versions (Eiselt and Sandblom, 2000), the emphasis of this quantity is on versions instead of resolution equipment. this is often fairly vital in a publication that purports to advertise the technological know-how of determination making. As such, complex undergraduate and graduate scholars, as weil as practitioners, will locate this quantity precious. whereas diverse authors favor diversified levels of mathematical sophistication, we've made each attainable try to unify the techniques, offer transparent causes, and make this quantity obtainable to as many readers as possible.
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Additional info for Decision Analysis, Location Models, and Scheduling Problems
It is apparent that with searches as demonstrated for the weighting method and the constraint method, a large number of linear programming problems need to be solved. This is not really a drawback; the real difficulty is that these solutions will have to be evaluated and compared with each other by a decision maker. In order to ensure the cooperation of a decision maker, we have to ascertain whether the number of comparisons is limited to a reasonable level. Whatever method we use, we will have to determine a fmite-and fairly small-number of solutions that are shortlisted for further consideration.
M. The basic idea of this problern is to fmd a composite which is as efficient as, or possibly even more efficient than, decision d 1• This is accomplished by ensuring that the composite requires inputs that are no larger than a fraction E 1 of the input of d,, while, at the same time, producing an output that is at least as large as that of d1• This is to be accomplished while trying to fmd an efficiency of decision d1 that is a small as possible. If, for instance, the composite could produce at least as much output as d1 with, say, only 80% ofthe input required by d1, then decision d, would be called no more than 80% efficient.
Through the range. 4. 4 is given by the so-called Edwards procedure that was used in the above example. The concave utility function assigns dirninishing utility increases to the scores. For example, the first dollar in profits has a higher value to the decision maker than an additional dollar that is gained when the profit has already reached a higher Ievel. Concave utility functions typically infer that some degree of Saturation is achieved at higher Ievels. Concave utility functions are found in many Situations.
Decision Analysis, Location Models, and Scheduling Problems by Prof. H. A. Eiselt, Prof. C.-L. Sandblom (auth.)