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Thesubjectofthisbookisthenested partitions
method(NP),arelativelynew optimization method that has been found
to be very e?ective solving discrete optimization problems. Such
discrete problems are common in many practical applications and the
NP method is thus useful in diverse application areas. It can be
applied to both operational and planning problems and has been
demonstrated to e?ectively solve complex problems in both
manufacturing and service industries. To illustrate its broad
applicability and e?ectiveness, in this book we will show how the
NP method has been successful in solving complex problems in
planning and scheduling, logistics and transportation, supply chain
design, data mining, and health care. All of these diverse app-
cationshaveonecharacteristicincommon:theyallleadtocomplexlarge-scale
discreteoptimizationproblemsthatareintractableusingtraditionaloptimi-
tion methods. 1.1 Large-Scale Optimization
IndevelopingtheNPmethodwewillconsideroptimization problemsthatcan
be stated mathematically in the following generic form: minf(x),
(1.1) x?X where the solution space or feasible region X is either a
discrete or bounded ? set of feasible solutions. We denote a
solution to this problem x and the ? ? objective function value f =
f (x ).
Thesubjectofthisbookisthenested partitions
method(NP),arelativelynew optimization method that has been found
to be very e?ective solving discrete optimization problems. Such
discrete problems are common in many practical applications and the
NP method is thus useful in diverse application areas. It can be
applied to both operational and planning problems and has been
demonstrated to e?ectively solve complex problems in both
manufacturing and service industries. To illustrate its broad
applicability and e?ectiveness, in this book we will show how the
NP method has been successful in solving complex problems in
planning and scheduling, logistics and transportation, supply chain
design, data mining, and health care. All of these diverse app-
cationshaveonecharacteristicincommon:theyallleadtocomplexlarge-scale
discreteoptimizationproblemsthatareintractableusingtraditionaloptimi-
tion methods. 1.1 Large-Scale Optimization
IndevelopingtheNPmethodwewillconsideroptimization problemsthatcan
be stated mathematically in the following generic form: minf(x),
(1.1) x?X where the solution space or feasible region X is either a
discrete or bounded ? set of feasible solutions. We denote a
solution to this problem x and the ? ? objective function value f =
f (x ).
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