# IP Models - Contents 7 Modeling Integer and Combinatorial...

This preview shows pages 1–6. Sign up to view the full content.

Contents 7 Modeling Integer and Combinatorial Programs 287 7.1 Types of Integer Programs, an Example Puzzle Problem, andaC l a s s i c a lS o lu t i onM e th od. ............ 2 8 7 7 . 2 Th eKn ap s a c kP r ob l em s ................. 2 9 6 7.3 Set Covering, Set Packing, and S e tP a r t i t i on in gP r l s ................ 3 0 2 7 . 4 P l an tL o c a t i onP r l s................. 3 2 3 7 . 5 B a t chS i z eP r l s ................... 3 2 8 7 . 6 O e r“E i e r ,O r ”C s t r a t s ............. 3 3 0 7 . 7 Ind i c a t o rV a r i ab l e s .................... 3 3 3 7 . 8 D i s c r e t eV a edV a r i l e 3 4 0 7 . 9 Th eG r aphC o l o r r l em . ............. 3 4 0 7 . 1 0Th eT r a v e l gS a l e sm anP r l em(TSP ) ........ 3 4 8 7 . 1 1Ex e r c i s e s.......................... 3 5 0 7 . 1 2R e f e r en c e s......................... 3 7 1 i

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
ii
286

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Chapter 7 Modeling Integer and Combinatorial Programs This is Chapter 7 of “Junior Level Web-Book Optimization Models for decision Making ”byKattaG .Murty . 7.1 Types of Integer Programs, an Exam- ple Puzzle Problem, and a Classical Solution Method So far, we considered continuous variable optimization models. In this chapter we will discuss modeling discrete or mixed discrete opti- mization problems in which all or some of the decision variables are restricted to assume values within speci f ed discrete sets, and com- binatorial optimization problems in which an optimum combina- tion/arrangement out of a possible set of combinations/arrangements has to be determined. Many of these problems can be modeled as LPs with additional integer restrictions on some, or all, of the variables. LP models with additional integer restrictions on decision variables are called integer linear programming problems or just integer programs .Theycanbec la s s i f ed into the following types. 287
288 Ch.7. Integer Programs Pure (or, all) integer programs: These are integer programs in which all the decision variables are restricted to assume only integer val- ues. 0 1 pure integer programs: These are pure integer programs, and in addition, all decision variables are bounded variables with lower bound 0, and upper bound 1; i.e., in e f ect, every decision variable in them is required to be either 0 or 1. Mixed integer programs or MIPs: Integer programs in which there are some continuous decision variables and some integer decision variables. 0 1 mixed integer programs (0-1 MIPs): These are MIPs in which all the integer decision variables are 0 1var iab les . Integer feasibility problems: Mathematical models in which it is required to f nd an integer solution to a given system of linear constraints, without any optimization. 0 1 integer feasibility problems: Integer feasibility problems to f nd a0 1 solution to a given system of linear constraints. Many problems involve various yes - no decisions ,wh ichcanbe considered as the 0 1 values of integer variables so constrained. Vari- ables which are restricted to the values 0 or 1 are called 0 1variables or binary variables or boolean variables .Tha t swhy0 1 integer programs are also called binary (or boolean) variable optimiza- tion problems .

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

### Page1 / 90

IP Models - Contents 7 Modeling Integer and Combinatorial...

This preview shows document pages 1 - 6. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online