LEADER 05295nam 2200637Ia 450 001 9910821845803321 005 20200520144314.0 010 $a1-282-25370-0 010 $a9786613814357 010 $a1-118-16600-0 010 $a1-118-16599-3 035 $a(CKB)2550000000054318 035 $a(EBL)818939 035 $a(OCoLC)757487547 035 $a(SSID)ssj0000534062 035 $a(PQKBManifestationID)11359972 035 $a(PQKBTitleCode)TC0000534062 035 $a(PQKBWorkID)10508886 035 $a(PQKB)11717093 035 $a(MiAaPQ)EBC818939 035 $a(EXLCZ)992550000000054318 100 $a20090630d2010 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApplied integer programming $emodeling and solution /$fDer-San Chen, Robert G. Batson, Yu Dang 205 $a1st ed. 210 $aHoboken, NJ $cJohn Wiley & Sons$dc2010 215 $a1 online resource (490 p.) 300 $aDescription based upon print version of record. 311 $a0-470-37306-7 320 $aIncludes bibliographical references (p. 411-121) and index. 327 $aApplied Integer Programming: Modeling and Solution; CONTENTS; PREFACE; PART I MODELING; 1 Introduction; 1.1 Integer Programming; 1.2 Standard Versus Nonstandard Forms; 1.3 Combinatorial Optimization Problems; 1.4 Successful Integer Programming Applications; 1.5 Text Organization and Chapter Preview; 1.6 Notes; 1.7 Exercises; 2 Modeling and Models; 2.1 Assumptions on Mixed Integer Programs; 2.2 Modeling Process; 2.3 Project Selection Problems; 2.3.1 Knapsack Problem; 2.3.2 Capital Budgeting Problem; 2.4 Production Planning Problems; 2.4.1 Uncapacitated Lot Sizing; 2.4.2 Capacitated Lot Sizing 327 $a2.4.3 Just-in-Time Production Planning 2.5 Workforce/Staff Scheduling Problems; 2.5.1 Scheduling Full-Time Workers; 2.5.2 Scheduling Full-Time and Part-Time Workers; 2.6 Fixed-Charge Transportation and Distribution Problems; 2.6.1 Fixed-Charge Transportation; 2.6.2 Uncapacitated Facility Location; 2.6.3 Capacitated Facility Location; 2.7 Multicommodity Network Flow Problem; 2.8 Network Optimization Problems with Side Constraints; 2.9 Supply Chain Planning Problems; 2.10 Notes; 2.11 Exercises; 3 Transformation Using 0-1 Variables; 3.1 Transform Logical (Boolean) Expressions 327 $a3.1.1 Truth Table of Boolean Operations 3.1.2 Basic Logical (Boolean) Operations on Variables; 3.1.3 Multiple Boolean Operations on Variables; 3.2 Transform Nonbinary to 0-1 Variable; 3.2.1 Transform Integer Variable; 3.2.2 Transform Discrete Variable; 3.3 Transform Piecewise Linear Functions; 3.3.1 Arbitrary Piecewise Linear Functions; 3.3.2 Concave Piecewise Linear Cost Functions: Economy of Scale; 3.4 Transform 0-1 Polynomial Functions; 3.5 Transform Functions with Products of Binary and Continuous Variables: Bundle Pricing Problem; 3.6 Transform Nonsimultaneous Constraints 327 $a3.6.1 Either/Or Constraints 3.6.2 p Out of m Constraints Must Hold; 3.6.3 Disjunctive Constraint Sets; 3.6.4 Negation of a Constraint; 3.6.5 If/Then Constraints; 3.7 Notes; 3.8 Exercises; 4 Better Formulation by Preprocessing; 4.1 Better Formulation; 4.2 Automatic Problem Preprocessing; 4.3 Tightening Bounds on Variables; 4.3.1 Bounds on Continuous Variables; 4.3.2 Bounds on General Integer Variables; 4.3.3 Bounds on 0-1 Variables; 4.3.4 Variable Fixing Redundant Constraints, and Infeasibility; 4.4 Preprocessing Pure 0-1 Integer Programs; 4.4.1 Fixing 0-1 Variables 327 $a4.4.2 Detecting Redundant Constraints And Infeasibility 4.4.3 Tightening Constraints (or Coefficients Reduction); 4.4.4 Generating Cutting Planes from Minimum Cover; 4.4.5 Rounding by Division with GCD; 4.5 Decomposing a Problem into Independent Subproblems; 4.6 Scaling the Coefficient Matrix; 4.7 Notes; 4.8 Exercises; 5 Modeling Combinatorial Optimization Problems I; 5.1 Introduction; 5.2 Set Covering and Set Partitioning; 5.2.1 Set Covering Problem; 5.2.2 Set Partitioning and Set Packing; 5.2.3 Set Covering in Networks; 5.2.4 Applications of Set Covering Problem; 5.3 Matching Problem 327 $a5.3.1 Matching Problems in Network 330 $aAn accessible treatment of the modeling and solution of integer programming problems, featuring modern applications and software In order to fully comprehend the algorithms associated with integer programming, it is important to understand not only how algorithms work, but also why they work. Applied Integer Programming features a unique emphasis on this point, focusing on problem modeling and solution using commercial software. Taking an application-oriented approach, this book addresses the art and science of mathematical modeling related to the mixed integer 606 $aInteger programming 606 $aMathematical optimization 615 0$aInteger programming. 615 0$aMathematical optimization. 676 $a519.7/7 700 $aChen$b Der-San$f1940-$0786035 701 $aBatson$b Robert G.$f1950-$01601810 701 $aDang$b Yu.$f1977-$01601811 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910821845803321 996 $aApplied Integer Programming$93925577 997 $aUNINA