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Introductory examples

Description
approx burglar chess Problem name and type, features Difficulty Approximation: Piecewise linear approximation ** SOS-2, Special Ordered Sets, piecewise linear approximation of a nonlinear function MIP modeling: Knapsack problem: 'Burglar' * simple MIP model with binary variables, data input from text data file, array initialization, numerical indices, string indices, record data structure LP modeling: Production planning: 'Chess' problem * simple LP model, solution output, primal solution values, slack values, activity values, dual solution values All item discount pricing: Piecewise linear function *** SOS-1, Special Ordered Sets, piecewise linear function, approximation of non-continuous function, step function Incremental pricebreaks: Piecewise linear function *** SOS-2, Special Ordered Sets, piecewise linear function, step function

Further explanation of this example: 'Applications of optimization with Xpress-MP', Introductory examples (Chapters 1 to 5) of the book 'Applications of optimization with Xpress-MP'

Source Files

Data Files

chess.mos

```(!******************************************************
Mosel Example Problems
======================

file chess.mos
``````````````
Production of chess boards

(c) 2008 Fair Isaac Corporation
author: R.C. Daniel, Jul. 2002
*******************************************************!)

model Chess
uses "mmxprs"

declarations
xs, xl: mpvar                   ! Decision variables: produced quantities
end-declarations

Profit:=  5*xs + 20*xl           ! Objective function
Boxwood:= 1*xs + 3*xl <=  200    ! kg of boxwood
Lathe:=   3*xs + 2*xl <=  160    ! Lathehours

maximize(Profit)                 ! Solve the problem

writeln("LP Solution:")          ! Solution printing
writeln(" Objective: ", getobjval)
writeln("Make ", getsol(xs), " small sets")
writeln("Make ", getsol(xl), " large sets")
end-model

```