| 000 | 22580cam a2204837 i 4500 | ||
|---|---|---|---|
| 001 | 103 | ||
| 008 | 141027s1991 nju 001 0 eng u | ||
| 020 |
_a0135002818 _q(hardback) |
||
| 040 |
_aTR-IsMEF _beng _erda _cTR-IsMEF |
||
| 041 | 0 | _aeng | |
| 049 | _aTR-IsMEF | ||
| 050 | 0 | 0 |
_aHD30.25 _b.E67 1991 |
| 100 | 1 |
_aEppen, Gary D., _d1936-, _eauthor. |
|
| 245 | 1 | 0 |
_aIntroductory management science / _cG.D. Eppen, F.J. Gould, C.P. Schmidt. |
| 264 | 1 |
_aNew Jersey : _bPrentice-Hall, _c1991. |
|
| 300 |
_axxiii, 830 pages : _billustrations ; _c25 cm. |
||
| 500 | _aIncludes index. | ||
| 650 | 0 |
_aManagement _xMathematical models |
|
| 650 | 0 | _aManagement science | |
| 700 | 1 |
_aGould, F. J. _q(Floyd Jerome), _d1936-, _eauthor. |
|
| 700 |
_aSchmidt, C. P., _eauthor. |
||
| 900 | _aMEF Üniversitesi Kütüphane katalog kayıtları RDA standartlarına uygun olarak üretilmektedir / MEF University Library Catalogue Records are Produced Compatible by RDA Rules | ||
| 920 | _aBağış sahibi bilinmiyor. | ||
| 942 |
_2lcc _cBKS |
||
| 970 | 0 | 1 |
_tChatper 1 introduction, _p1. |
| 970 | 1 | 1 |
_tThe role of this book, _p1 |
| 970 | 1 | 1 |
_tDifferent types of models and what they are all about, _p3. |
| 970 | 1 | 1 |
_tSpreadsheet models, _p9. |
| 970 | 1 | 1 |
_tA taxonomy of management science models, _p13. |
| 970 | 1 | 1 |
_tModel building, _p14. |
| 970 | 1 | 1 |
_tOn the use and implementation of modeling, _p16. |
| 970 | 1 | 1 |
_tConstraidned optimization models, _p18. |
| 970 | 1 | 1 |
_tWhy constraints are imposed, _p22. |
| 970 | 1 | 1 |
_tIntuitive versus formal modeling, _p25. |
| 970 | 0 | 1 |
_aSummary, _p26. |
| 970 | 0 | 1 |
_aMajor concepts quiz, _p27. |
| 970 | 0 | 1 |
_aQuestions for discussion, _p29. |
| 970 | 0 | 1 | _aComputer printouts: figures 1.6, 1.7, 1.8, 1.9. |
| 970 | 1 | 2 |
_tChapter 2 linear programming: formal and speadsheet models, _p30. |
| 970 | 1 | 2 |
_tApplication capsule: allocating a scarce resource, _p30. |
| 970 | 0 | 1 |
_tIntroduction, _p31. |
| 970 | 1 | 1 |
_tPROTRAC, Inc., _p33. |
| 970 | 1 | 1 |
_tA spreadsheet representation of PROTRAC E and F, _p39. |
| 970 | 1 | 1 |
_tThe spreadsheet versus the formal LP model, _p44. |
| 970 | 1 | 1 |
_tCrawler tread: a blending example, _p49. |
| 970 | 1 | 1 |
_tGuidelines and comments on model formulation, _p53. |
| 970 | 1 | 1 |
_tSank versus variable cost, _p54. |
| 970 | 1 | 1 |
_tExample 1: astro and cosmo (a product-mix problem), _p55. |
| 970 | 1 | 1 |
_tExample 2: blending gruel (a blending problem), _p56. |
| 970 | 1 | 1 |
_tExample 3: security force scheduling (a scheduling problem), _p57. |
| 970 | 1 | 1 |
_tExample 4: a transportation model, _p60. |
| 970 | 1 | 1 |
_tExample 5: winston-salem development corporation (financial planning), _p61. |
| 970 | 1 | 1 |
_tExample 6: longer boats yatcht company-a vignette in constrained break-even analysis, _p63. |
| 970 | 1 | 1 |
_tExample 7: multiperiod inventory models, _p65. |
| 970 | 1 | 1 |
_tExample 8: the bumles, Inc., minicase (production and inventory control), _p68. |
| 970 | 0 | 1 |
_aSummary, _p71. |
| 970 | 0 | 1 |
_aKey terms, _p72. |
| 970 | 0 | 1 |
_aMajor concepts quiz, _p73. |
| 970 | 0 | 1 |
_aProblems, _p77. |
| 970 | 1 | 1 |
_tCase: red brand canners (formulation), _p93. |
| 970 | 1 | 1 |
_tCase: an application of spreadsheet analysis to foreign exchange markets, _p96. |
| 970 | 1 | 1 | _tComputer printouts: figures 2.3, 2.4, 2.5, 2.6, 2.7, 2.18, 2.19, 2.27, 2.28, Exhibits 1,2,3,4. |
| 970 | 1 | 2 |
_tChapter 3 Linear programming: geometric representations and graphical solutions, _p104. |
| 970 | 0 | 1 |
_aIntroduction, _p104. |
| 970 | 1 | 1 |
_tPlotting inequalities and contours, _p104. |
| 970 | 1 | 1 |
_tThe graphical solution method applied to PROTRAC. Inc., _p107. |
| 970 | 1 | 1 |
_tActive and inactive constraints, _p114. |
| 970 | 1 | 1 |
_tExtreme points and optimal solutions, _p118. |
| 970 | 1 | 1 |
_tSummary of the graphical solution method for a max model, _p120. |
| 970 | 1 | 1 |
_tThe graphical method applied to a min model, _p120. |
| 970 | 1 | 1 |
_tUnbounded and infeasible problems, _p22. |
| 970 | 0 | 1 |
_aSummary, _p125. |
| 970 | 0 | 1 |
_aKey terms, _p126. |
| 970 | 0 | 1 |
_aMajor concepts quiz, _p127. |
| 970 | 0 | 1 |
_aProblems, _p128. |
| 970 | 1 | 2 |
_tChapter 4 analysis of LP models: the graphical approach, _p133. |
| 970 | 1 | 1 |
_tIntroduction to sensitivity analysis (PROTRAC, Inc., revisited), _p133. |
| 970 | 1 | 1 |
_tChanges in the objective function coefficients, _p135. |
| 970 | 1 | 1 |
_tChanges in the right-hand sides, _p137. |
| 970 | 1 | 1 |
_tTightening and loosening an inequality constraint, _p139. |
| 970 | 1 | 1 |
_tRedundant constraints, _p140. |
| 970 | 1 | 1 |
_tWhat is an important constraint?, _p142. |
| 970 | 1 | 1 |
_tAdding of deleting constraints, _p144. |
| 970 | 0 | 1 |
_aSummary, _p145. |
| 970 | 0 | 1 |
_aKey terms, _p146. |
| 970 | 0 | 1 |
_aMajor concepts quiz, _p147. |
| 970 | 0 | 1 |
_aProblems, _p148. |
| 970 | 1 | 2 |
_tChapter 5 linear programs: computer analysis, interpreting sensitivity output, and the dual problem, _p151. |
| 970 | 1 | 1 |
_tApplication capsule: an inventory of trucks, _p151. |
| 970 | 0 | 1 |
_aIntroduction, _p152. |
| 970 | 1 | 1 |
_tThe problem the computer solves, _p152. |
| 970 | 1 | 1 |
_tThe computer analysis of PROTRAC, Inc., _p161. |
| 970 | 1 | 1 |
_tThe crawler tread output: a dialogue with management (sensitivity analysis in action), _p172. |
| 970 | 1 | 1 |
_tA synopsis of the solution output, _p181. |
| 970 | 1 | 1 |
_tThe dual problem, _p181. |
| 970 | 1 | 1 |
_tNotes on implementation, _p192. |
| 970 | 0 | 1 |
_aSummary, _p193. |
| 970 | 0 | 1 |
_aKey terms, _p194. |
| 970 | 0 | 1 |
_aMajor concepts quiz, _p194. |
| 970 | 0 | 1 |
_aProblems, _p196. |
| 970 | 0 | 1 |
_aAppendix 5.1 solving an LP when not all variables are required to be nonnegative, _p203. |
| 970 | 0 | 1 |
_aCase: saw mill river feed and grain company, _p205. |
| 970 | 0 | 1 |
_aCase: kiwi computer, _p207. |
| 970 | 0 | 1 |
_aCase: production planning at bumles, _p210. |
| 970 | 1 | 1 |
_tDiagnostic assignment: crawler tread and a new angle, _p213. |
| 970 | 1 | 1 | _tComputer printouts: figures 5.6, 5.11, 5.12, 5.14, 5.18, 5.20, 5.21. |
| 970 | 1 | 2 |
_tChapter 6 linear programming: the simplex method, _p216. |
| 970 | 0 | 1 |
_aIntroduction, _p216. |
| 970 | 1 | 1 |
_tThe astro/cosmo problem revisited, _p217. |
| 970 | 1 | 1 |
_tTypes of solutions to the original quations, _p218. |
| 970 | 1 | 1 |
_tBasic feasible solutions and extreme points, _p222. |
| 970 | 1 | 1 |
_tTransformed equations, _p224. |
| 970 | 1 | 1 |
_tThe characterization of adjacent extreme points, _p226. |
| 970 | 1 | 1 |
_tThe initial tableau, _p227. |
| 970 | 1 | 1 |
_tIncreasing the objective function by computing opportunity costs, _p228. |
| 970 | 1 | 1 |
_tThe full tableau representation, _p231. |
| 970 | 1 | 1 |
_tDetermining the exit variable, _p234. |
| 970 | 1 | 1 |
_tILP and MILP in practice, _p335. |
| 970 | 1 | 1 |
_tNotes on implementation of integer programming, _p357. |
| 970 | 1 | 1 |
_tSummary of IP, _p359. |
| 970 | 1 | 1 |
_tIntroduction to quadratic programming, _p359. |
| 970 | 1 | 1 |
_tComputer solution of QP problems, _p361. |
| 970 | 1 | 1 |
_tGeometric interpretation of the sensitivity analysis, _p362. |
| 970 | 1 | 1 |
_tPortfolio selection, _p366. |
| 970 | 1 | 1 |
_tA portfolio example with live data, _p368. |
| 970 | 0 | 1 |
_aKey terms, _p372. |
| 970 | 0 | 1 |
_aMajor concepts quiz, _p373. |
| 970 | 1 | 1 |
_tPart 1. integer programming questions, _p373. |
| 970 | 1 | 1 |
_tPart 2. quadratic programming questions, _p376. |
| 970 | 1 | 1 |
_tPart 1. problems on integer programs, _p378. |
| 970 | 1 | 1 |
_tPart 2. problems on quadratic programs, _p383. |
| 970 | 1 | 1 |
_tCase: municipal bond underwriting, _p384. |
| 970 | 1 | 1 |
_tCase: cash flow matching, _p387. |
| 970 | 1 | 1 |
_tDiagnositic assignment: assigning sales representatives, _p390. |
| 970 | 1 | 1 | _tComputer printouts: figures 8.5, 8.6, 8.9, 8.24, 8.35 |
| 970 | 1 | 2 |
_tChapter 9 network models, _p393. |
| 970 | 1 | 2 |
_tApplication capsule: a network model at air products and chemicals, Inc., _p393. |
| 970 | 0 | 1 |
_aIntroduction, _p394. |
| 970 | 1 | 1 |
_tAn example: seymour miles (a capacitated transshipment model), _p394. |
| 970 | 1 | 1 |
_tA general formulation (the capacitated transshipment model), _p397. |
| 970 | 1 | 1 |
_tThe shortest-route problem, _p399. |
| 970 | 1 | 1 |
_tThe minimum spanning tree problem (communication links), _p405. |
| 970 | 1 | 1 |
_tThe maximal-flow problem, _p410. |
| 970 | 1 | 1 |
_tNotes on implementation, _p416. |
| 970 | 0 | 1 |
_aSummary, _p416. |
| 970 | 0 | 1 |
_aKey terms, _p417. |
| 970 | 0 | 1 |
_aMajor concepts quiz, _p417. |
| 970 | 0 | 1 |
_aProblems, _p419. |
| 970 | 1 | 1 |
_tAppendix 9.1 A PC approach to network problems, _p424. |
| 970 | 1 | 1 | _tComputer printouts: figues 9.40, 9.42, 9.44. |
| 970 | 1 | 2 |
_tChapter 10 project management: PERT and CPM, _p429. |
| 970 | 0 | 1 |
_aIntroduction, _p429. |
| 970 | 1 | 1 |
_tThe global oil credit card operation, _p430. |
| 970 | 1 | 1 |
_tThe critical path-meeting the board's deadline, _p435. |
| 970 | 1 | 1 |
_tVariability in activity times, _p444. |
| 970 | 1 | 1 |
_tA mid-chapter summary, _p448. |
| 970 | 1 | 1 |
_tCPM and time-cost trade-offs, _p449. |
| 970 | 1 | 1 |
_tProject cost management: PERT/COST, _p455. |
| 970 | 1 | 1 |
_tNotes on implementation, _p461. |
| 970 | 0 | 1 |
_aSummary, _p462. |
| 970 | 0 | 1 |
_aKey terms, _p463. |
| 970 | 0 | 1 |
_aMajor concepts quiz, _p464. |
| 970 | 0 | 1 |
_aProblems, _p466. |
| 970 | 1 | 1 |
_tAppendix 10.1 A PC approach to PERT/CPM, _p475. |
| 970 | 0 | 1 | _aComputer printouts: figures 10.13, 10.14, 10.16, 10.18, 10.24, 10.29, 10.43, 10.44, 10.46. |
| 970 | 1 | 2 |
_tChapter 11 inventory control with known demand, _p479. |
| 970 | 1 | 2 |
_tApplication capsule: coordinating decisions for increased profits, _p479. |
| 970 | 0 | 1 |
_aIntroduction, _p480. |
| 970 | 1 | 1 |
_tSteco wholesaling: the current policy, _p482. |
| 970 | 1 | 1 |
_tThe economic order quantatity model, _p487. |
| 970 | 1 | 1 |
_tQuantatity discounts and steco's overall optinum, _p496. |
| 970 | 1 | 1 |
_tThe EOQ model with backlogging, _p499. |
| 970 | 1 | 1 |
_tThe production lot size model: Victor's heat treatment problem, _p504. |
| 970 | 1 | 1 |
_tMaterial requirements planning: farmcraft manufacturing co., _p506. |
| 970 | 0 | 1 |
_aSummary, _p510. |
| 970 | 0 | 1 |
_aKey terms, _p510. |
| 970 | 0 | 1 |
_aMajor concepts quiz, _p511. |
| 970 | 0 | 1 |
_aProblems, _p513. |
| 970 | 0 | 1 |
_aAppendiz 11.1 mathematical derivation of EOQ results, _p517. |
| 970 | 1 | 2 |
_tChapter 12 inventory models with probabilistic demand, _p519. |
| 970 | 0 | 1 |
_aIntroducion, _p519. |
| 970 | 1 | 1 |
_tThe reorder point-reorder quantatity model, _p520. |
| 970 | 1 | 1 |
_tThe appliance angle problem, _p520. |
| 970 | 1 | 1 |
_tVictor's choice of r. uniform lead-time demand, _p522. |
| 970 | 1 | 1 |
_tSelecting a probability of stocking out, _p523. |
| 970 | 1 | 1 |
_tVictor's choice of r. normal lead-time demand, _p525. |
| 970 | 1 | 1 |
_tExpected annual cost of safety stock, _p527. |
| 970 | 1 | 1 |
_tOne-period models with probabilistic demand (wiles' housewares problem), _p528. |
| 970 | 1 | 1 |
_tThe newsboy problem, _p529. |
| 970 | 1 | 1 |
_tNotes on implementation, _p532. |
| 970 | 0 | 1 |
_aSummary, _p534. |
| 970 | 0 | 1 |
_aKey terms, _p535. |
| 970 | 0 | 1 |
_aMajor concepts quiz, _p535. |
| 970 | 0 | 1 |
_aProblems, _p537. |
| 970 | 0 | 1 |
_aDiagnostic assignment: inventory turns per year, _p540. |
| 970 | 1 | 2 |
_tChapter 12 queuing models, _p542. |
| 970 | 0 | 1 |
_aIntroduction, _p542. |
| 970 | 1 | 1 |
_tThe basic model, _p543. |
| 970 | 1 | 1 |
_tLittle's flow equation and other generalities, _p547. |
| 970 | 1 | 1 |
_tThe generalized model, _p549. |
| 970 | 1 | 1 |
_tProblem 1: a multiserver queue (hematology lab), _p550. |
| 970 | 1 | 1 |
_tA taxonomy of quening models, _p552. |
| 970 | 1 | 1 |
_tEconomic analysis of queing systems, _p553. |
| 970 | 1 | 1 |
_tProblem 2: the M/G/s model with blocked customers cleared (WATS lines), _p555. |
| 970 | 1 | 1 |
_tProblem 3: the repairperson problem, _p558. |
| 970 | 1 | 1 |
_tThe role of the exponential distribution, _p560. |
| 970 | 1 | 1 |
_tQueue discipline, _p562. |
| 970 | 1 | 1 |
_tNotes on implementation, _p562. |
| 970 | 0 | 1 |
_aSummary, _p563. |
| 970 | 0 | 1 |
_aKey terms, _p564. |
| 970 | 0 | 1 |
_aMajor concepts quiz, _p564. |
| 970 | 0 | 1 |
_aProblems, _p565. |
| 970 | 0 | 1 | _aComputer printout figure 13.11. |
| 970 | 1 | 2 |
_tChapter 14 simulation, _p569. |
| 970 | 1 | 2 |
_tApplication capsule: naval ship production, _p569. |
| 970 | 1 | 2 |
_tApplication capsule: planning to get the lead out, _p570. |
| 970 | 0 | 1 |
_aIntroduction, _p571. |
| 970 | 1 | 1 |
_tSimulation and random events, _p575. |
| 970 | 1 | 1 |
_tAn inventory control example: wiles' housewares, _p577. |
| 970 | 1 | 1 |
_tGenerating random events, _p581. |
| 970 | 1 | 1 |
_tComputer simulation of wiles' problem, _p584. |
| 970 | 1 | 1 |
_tA simulation study: inventory control at PROTRAC, _p586. |
| 970 | 1 | 1 |
_tNotes on implementation, _p590. |
| 970 | 0 | 1 |
_aSummary, _p593. |
| 970 | 0 | 1 |
_aKey terms, _p594. |
| 970 | 0 | 1 |
_aMajor concepts quiz, _p594. |
| 970 | 0 | 1 |
_aProblems, _p596. |
| 970 | 0 | 1 |
_aAppendix 14.1 a spreadsheet application to simulation, _p601. |
| 970 | 0 | 1 |
_aDiagnostic assignment: scheduling tanker arrivals, _p604. |
| 970 | 0 | 1 | _aComputer printouts: figures 14.14, 14.26, 14.27, 14.29. |
| 970 | 1 | 2 |
_tChapter 15 decision theory and decision trees, _p607. |
| 970 | 1 | 2 |
_tApplication capsule: designing a complex interconnected system, _p607. |
| 970 | 0 | 1 |
_aIntroduction, _p608. |
| 970 | 1 | 1 |
_tThree classes of decision problems, _p609. |
| 970 | 1 | 1 |
_tThe expected value of perfect infromation: newsboy problem under risk, _p617. |
| 970 | 1 | 1 |
_tUtilities and decisions under risk, _p619. |
| 970 | 1 | 1 |
_tA mid-chapter summary, _p623. |
| 970 | 1 | 1 |
_tDecision trees: marketing home and garden tractors, _p624. |
| 970 | 1 | 1 |
_tSensitivity analysis, _p628. |
| 970 | 1 | 1 |
_tDecision trees: incorporating new information, _p630. |
| 970 | 1 | 1 |
_tSequential decisions: to test or not to test, _p639. |
| 970 | 1 | 1 |
_tManagement and decision theory, _p640. |
| 970 | 1 | 1 |
_tNotes on implementation, _p643. |
| 970 | 0 | 1 |
_aSummary, _p644. |
| 970 | 0 | 1 |
_aKey terms, _p645. |
| 970 | 0 | 1 |
_aMajor concepts quiz, _p645. |
| 970 | 0 | 1 |
_aProblems, _p647. |
| 970 | 0 | 1 |
_aAppendix 15.1 A PC approach to decision trees, _p658. |
| 970 | 1 | 1 |
_tCase: to drill or not to drill, _p660. |
| 970 | 1 | 1 |
_tDignostic assignment: Johnson's metal, _p662. |
| 970 | 1 | 2 |
_tChapter 16 forecasting, _p663. |
| 970 | 0 | 1 |
_aIntroduction, _p663. |
| 970 | 1 | 1 |
_tQuantative forecasting, _p664. |
| 970 | 1 | 1 |
_tCausal forecasting models, _p665. |
| 970 | 1 | 1 |
_tTime-series forecasting models, _p675. |
| 970 | 1 | 1 |
_tThe role of historical data: divide and conquer, _p689. |
| 970 | 1 | 1 |
_tQualitative forecasting, _p690. |
| 970 | 1 | 1 |
_tNotes on implementation, _p692. |
| 970 | 0 | 1 |
_aKey terms, _p694. |
| 970 | 0 | 1 |
_aMajor concepts quiz, _p694. |
| 970 | 0 | 1 |
_aProblems, _p696. |
| 970 | 0 | 1 |
_aAppendix 16.1 fitting forecasting models, the data table spreadsheet command, _p699. |
| 970 | 0 | 1 | _aComputer printouts: figures 16.26, 16.27. |
| 970 | 1 | 2 |
_tChapter 17 heuristics, multiple objectives, and goal programming, _p701. |
| 970 | 1 | 2 |
_tApplication capsule: national center for drug analysis, _p701. |
| 970 | 1 | 2 |
_tApplication capsule: management of college student recruting activities, _p702. |
| 970 | 0 | 1 |
_aIntroduction, _p703. |
| 970 | 1 | 1 |
_tFacilty scheduling (sequencing computer jobs), _p704. |
| 970 | 1 | 1 |
_tScheduling with limited resources (workload smoothing), _p707. |
| 970 | 1 | 1 |
_tMultiple objectives, _p713. |
| 970 | 1 | 1 |
_tNotes on implementation, _p727. |
| 970 | 0 | 1 |
_aKey terms, _p728. |
| 970 | 0 | 1 |
_aMajor concepts, _p728. |
| 970 | 0 | 1 |
_aProblems, _p730. |
| 970 | 0 | 1 | _aComputer printouts: figures 17.16, 17.18, 17.19, 17.20, 17.21, 17.22. |
| 970 | 1 | 2 |
_tChapter 18 calculus-based optimization and an introduction to nonlinear programming, _p736. |
| 970 | 0 | 1 |
_aIntroduction, _p736. |
| 970 | 1 | 1 |
_tUnconstrained optimization in two decision variables, _p737. |
| 970 | 1 | 1 |
_tUnconstrained optimization in n decision variables: the computer approach, _p740. |
| 970 | 1 | 1 |
_tNonlinear optimization with constraints: a descriptive geometric introduction, _p741. |
| 970 | 1 | 1 |
_tEquality-constrained models and lagrange multipliers, _p745. |
| 970 | 1 | 1 |
_tModels with inequality constraints and GINO, _p753. |
| 970 | 1 | 1 |
_tDifferent types of NLP problems and solvability, _p761. |
| 970 | 1 | 1 |
_tNotes on implementation, _p766. |
| 970 | 0 | 1 |
_aMajor concepts quiz, _p767. |
| 970 | 0 | 1 |
_aProblems, _p769. |
| 970 | 0 | 1 | _aComputer printouts: figures 18.5, 18.9, 18.10, 18.11. |
| 970 | 0 | 1 |
_aAnswers to odd-numbered problems, _p773. |
| 970 | 1 | 1 |
_tSelected answers to major concept quizes, _p817. |
| 970 | 1 | 1 |
_tTable T.1 areas for the standard normal distribution, _p822. |
| 970 | 0 | 1 |
_aIndex, _p823. |
| 970 | 0 | 1 | _aVIGNETTES AND APPLICATIONS. |
| 970 | 0 | 1 | _aChapter 1. |
| 970 | 1 | 1 |
_tSupply and demand: break-even analysis, _p3. |
| 970 | 1 | 1 |
_tSpreadsheet models (oak products production), _p9. |
| 970 | 0 | 1 | _aChapter 2. |
| 970 | 1 | 2 |
_tApplication capsule: allocating a scare resource, _p30. |
| 970 | 1 | 1 |
_tPROTRAC, Inc. (formulation), _p33. |
| 970 | 1 | 1 |
_tA spreadsheet representation of PROTRAC E&F, _p39. |
| 970 | 1 | 1 |
_tA spreadsheet parametric analysis for PROTRAC E&F, _p44. |
| 970 | 1 | 1 |
_tCrawler tread (formulation), _p49. |
| 970 | 1 | 1 |
_tAstro and cosmo (product mix), _p55. |
| 970 | 1 | 1 |
_tBlending gruel (blending problem), _p56. |
| 970 | 1 | 1 |
_tSecurity force scheduling (scheduling problem and spreadsheet application), _p57. |
| 970 | 1 | 1 |
_tTransportation model, _p60. |
| 970 | 1 | 1 |
_tWinston-salem development corporation (financial planning), _p61. |
| 970 | 1 | 1 |
_tLonger boats yacht company (constrained break-even analysis), _p63. |
| 970 | 1 | 1 |
_tBumles, Inc, . minicase-production and inventory control (a spreadsheet application), _p68. |
| 970 | 1 | 2 |
_tCase: foreign exchange markets (a spreadsheet application), _p96. |
| 970 | 0 | 1 | _aChapter 3. |
| 970 | 1 | 1 |
_tPROTRAC, Inc. (graphical analysis), _p107. |
| 970 | 0 | 1 | _aChapter 4. |
| 970 | 1 | 1 |
_tPROTRAC, Inc. (graphical analysis), _p133. |
| 970 | 0 | 1 | _aChapter 5. |
| 970 | 1 | 2 |
_tApplication capsule: an inventory of trucks, _p151. |
| 970 | 1 | 1 |
_tPROTRAC, Inc. (computer output), _p161. |
| 970 | 1 | 1 |
_tCrawler Tread (computer output and sensivity analysis, _p172. |
| 970 | 1 | 1 |
_tTwo plants and liquidation prices, _p181. |
| 970 | 1 | 2 |
_tAppendix 5.2: red brand canners case (computer analysis), _p204. |
| 970 | 1 | 2 |
_tCase: saw mill river feed and grain company, _p205. |
| 970 | 1 | 2 |
_tCase: kiwi computer, _p207. |
| 970 | 1 | 2 |
_tCase: production planning at burnies, _p210. |
| 970 | 1 | 2 |
_tDiagnostic assignment: crawler tread and a new angle, _p213. |
| 970 | 0 | 1 | _aChapter 6. |
| 970 | 1 | 1 |
_tAstro/cosmo (simplex application), _p217. |
| 970 | 1 | 1 |
_tPROTRAC, Inc. (simplex application), _p242. |
| 970 | 0 | 1 | _aChapter 7. |
| 970 | 1 | 1 |
_tPROTRAC distribution (diesels from harbors to plants, _p274. |
| 970 | 1 | 1 |
_tPROTRAC-Europe's auditing problem, _p300. |
| 970 | 1 | 1 |
_tThe auditing problem reconsidered, _p307. |
| 970 | 1 | 1 |
_tFinancial planning, _p313. |
| 970 | 1 | 1 |
_tMedia selection, _p317. |
| 970 | 0 | 1 | _aChapter 8. |
| 970 | 1 | 2 |
_tApplication capsule: scheduling trainning at American Airlines, _p329. |
| 970 | 1 | 1 |
_tPROTRAC, Inc. (integer program), _p332. |
| 970 | 1 | 1 |
_tCapital budgetting at PROTRAC (expansion decision), _p337. |
| 970 | 1 | 1 |
_tAn IP vignette: Steco's warehouse location problem, _p342. |
| 970 | 1 | 1 |
_tThe assignment problem and a social theorem, _p346. |
| 970 | 1 | 1 |
_tKelly-springerfield, _p357. |
| 970 | 1 | 1 |
_tFlying tiger line, _p357. |
| 970 | 1 | 1 |
_tHunt-wesson foods, _p357. |
| 970 | 1 | 2 |
_tCase: municipal bond underwriting, _p384. |
| 970 | 1 | 2 |
_tCase: cash flow matching, _p387. |
| 970 | 1 | 2 |
_tDiagnostic assignment: assigning sales representatives, _p390. |
| 970 | 0 | 1 | _aChapter 9. |
| 970 | 1 | 2 |
_tApplication capsule: air products and chemicals, Inc., _p393. |
| 970 | 1 | 1 |
_tSeymour miles (capacited transshipment), _p394. |
| 970 | 1 | 1 |
_tAaron drunner's delivery service: equipment replacement, _p399. |
| 970 | 1 | 1 |
_tCommunication link project, _p405. |
| 970 | 1 | 1 |
_tUrban development planning commission, _p410. |
| 970 | 0 | 1 | _aChapter 10. |
| 970 | 1 | 1 |
_tGlobal oil credit card operation, _p430. |
| 970 | 1 | 1 |
_tMeeting the board's deadline, _p435. |
| 970 | 1 | 1 |
_tFinancial analysis for retail marketing, _p449. |
| 970 | 1 | 1 |
_tPlanning costs for global oil, _p455. |
| 970 | 1 | 2 |
_tApplication capsule: coordinating decision for increased profits, _p479. |
| 970 | 1 | 1 |
_tSteco wholesaling, _p482. |
| 970 | 1 | 1 |
_tQuantatity discounts and steco's overall optimum, _p496. |
| 970 | 1 | 1 |
_tSelling styrofoam, _p499. |
| 970 | 1 | 1 |
_tMaterial requirements planning: Farmcraft manufacturing Co., _p506. |
| 970 | 0 | 1 | _tChapter 11. |
| 970 | 1 | 1 |
_tQuantatity discounts and Steco's overall optimum, _p496. |
| 970 | 1 | 1 |
_tMaterial requirements planning: farmcraft manufacturing Co., _p506. |
| 970 | 0 | 1 | _tChapter 12. |
| 970 | 1 | 1 |
_tAppliance angles, _p520. |
| 970 | 1 | 1 |
_tWiles housewares, _p528. |
| 970 | 1 | 1 |
_tTwo bin system, _p532. |
| 970 | 1 | 2 |
_tDiagnostic assignment: inventory turns per year, _p540. |
| 970 | 0 | 1 | _aChapter 13. |
| 970 | 1 | 1 |
_tSt. Luke's hametology lab, _p542. |
| 970 | 1 | 1 |
_tBuying WATS lines, _p542. |
| 970 | 1 | 1 |
_tHiring repairpeople, _p542. |
| 970 | 1 | 1 |
_tHematology lab, _p550. |
| 970 | 1 | 1 |
_tWATS lines, _p555. |
| 970 | 1 | 1 |
_tRepairing problem, _p558. |
| 970 | 0 | 1 | _aChapter 14. |
| 970 | 1 | 1 |
_tDocking facilities, _p575. |
| 970 | 1 | 1 |
_tInventory control; scheduling, _p575. |
| 970 | 1 | 1 |
_tWiles' housewares (inventory control), _p577. |
| 970 | 1 | 1 |
_tWiles' problem (computer simulation), _p584. |
| 970 | 1 | 1 |
_tInventory control at PROTRAC, _p586. |
| 970 | 1 | 2 |
_tDiagnostic assignment: scheduling tanker arrivals, _p604. |
| 970 | 0 | 1 | _aChapter 15. |
| 970 | 1 | 1 |
_tThe expected value of perfect information: newsboy problem under risk, _p617. |
| 970 | 1 | 1 |
_tMarketing home and garden tractors, _p624. |
| 970 | 1 | 1 |
_tMarketing research example, _p630. |
| 970 | 1 | 2 |
_tCase: to drill or not to drill, _p680. |
| 970 | 1 | 2 |
_tDiagnostic assignment: Johnson's metal, _p662. |
| 970 | 0 | 1 | _aChapter 16. |
| 970 | 1 | 1 |
_tOil company expansion (curve fitting), _p665. |
| 970 | 1 | 1 |
_tQuick and dirty fits, _p665. |
| 970 | 1 | 1 |
_tApplication to futures prices, _p675. |
| 970 | 1 | 1 |
_tForecasting steco's strut sales (moving averages), _p675. |
| 970 | 1 | 1 |
_tApplication to stock prices, _p675. |
| 970 | 1 | 1 |
_tThe delphi method, _p690. |
| 970 | 0 | 1 | _aChapter 17. |
| 970 | 1 | 2 |
_tApplication capsule: management of college student recruiting, _p702. |
| 970 | 1 | 1 |
_tSequencing computer jobs, _p704. |
| 970 | 1 | 1 |
_tWork-load smoothing, _p707. |
| 970 | 1 | 1 |
_tSwenson's media selection problem (absolute priorities), _p713. |
| 970 | 0 | 1 | _aChapter 18. |
| 970 | 1 | 1 |
_tImporting coconut oil (profit maximization), _p737. |
| 970 | 1 | 1 |
_tOptimal marketin expenditures, _p745. |
| 970 | 1 | 1 |
_tAstro/cosmo problem with price sensitive demand gulf coast oil (nonlinear pooling), _p753. |
| 999 |
_c4914 _d4914 |
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| 003 | KOHA | ||