| 000 | 25596cam a2205761Ii 4500 | ||
|---|---|---|---|
| 001 | 161100 | ||
| 008 | 090828s20112011mnua b 001 0 eng | ||
| 020 | _a053847565x (paperback) | ||
| 020 | _a0538475676 (Student CD) | ||
| 020 | _a9780538475655 (paperback) | ||
| 040 |
_aMEF _beng _erda |
||
| 049 | _aTR-IsMEF | ||
| 050 | 0 | 0 |
_aHD30.25 _b.A53 2011 |
| 245 | 0 | 3 |
_aAn introduction to management science : _bquantitative approaches to decision making / _cDavid R. Anderson, University of Cincinnati, Dennis J. Sweeney, University of Cincinnati, Thomas A. Williams, Rochester Institute of Technology, Kipp Martin, University of Chicago. |
| 250 | _aThirteenth edition, International edition. | ||
| 264 | 1 |
_aMason, OH : _bCengage South-Western, _c2011. |
|
| 264 | 4 | _a©2011 | |
| 300 |
_axxx, 816 pages : _billustrations ; _c27 cm. + _e1 CD-ROM (4 3/4 in.) |
||
| 336 |
_atext _2rdacontent |
||
| 336 |
_atwo-dimensional moving image _2rdacontent |
||
| 337 |
_aunmediated _2rdamedia |
||
| 337 |
_acomputer _2rdamedia |
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| 338 |
_avolume _2rdacarrier |
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| 338 |
_acomputer disc _2rdacarrier |
||
| 504 | _aIncludes bibliographical references and index. | ||
| 650 | 0 | _aManagement science. | |
| 700 | 1 |
_aAnderson, David R. _q(David Ray), _d1941-, _eauthor. |
|
| 700 | 1 |
_aSweeney, Dennis J., _eauthor. |
|
| 700 | 1 |
_aWilliams, Thomas A., _eauthor. |
|
| 700 | 1 |
_aMartin, Kipp, _eauthor. |
|
| 900 | _aMEF Üniversitesi Kütüphane katalog kayıtları RDA standartlarına uygun olarak üretilmektedir / MEF University Library Catalogue Records are Produced Compatible by RDA Rules | ||
| 910 | _aPandora | ||
| 942 |
_2lcc _cBKS |
||
| 970 | 0 | 1 |
_aPreface, _pxxii. |
| 970 | 0 | 1 |
_aAbout the authors, _pxxix. |
| 970 | 0 | 1 |
_aIntroduction, _p1. |
| 970 | 1 | 1 |
_tProblem solving and decision making, _p3. |
| 970 | 1 | 1 |
_tQuantative analysis and decision making, _p4. |
| 970 | 1 | 1 |
_tQuantative analysis, _p6. |
| 970 | 1 | 1 |
_tModel development, _p7. |
| 970 | 1 | 1 |
_tData preparation, _p10. |
| 970 | 1 | 1 |
_tModel solution, _p11. |
| 970 | 1 | 1 |
_tReport generation, _p12. |
| 970 | 1 | 1 |
_tA note regarding implementation, _p12. |
| 970 | 1 | 1 |
_tModels of cost, revenue, and profit, _p16. |
| 970 | 1 | 1 |
_tCost and volume models, _p14. |
| 970 | 1 | 1 |
_tRevenue and volume models, _p15. |
| 970 | 1 | 1 |
_tProfit and volume models, _p15. |
| 970 | 1 | 1 |
_tBreakeven analysis, _p16. |
| 970 | 1 | 1 |
_tManagement science techniques, _p16. |
| 970 | 1 | 1 |
_tMethods used most frequently, _p18. |
| 970 | 0 | 1 |
_aSummary, _p19. |
| 970 | 0 | 1 |
_aGlossary, _p19. |
| 970 | 0 | 1 |
_aProblems, _p20. |
| 970 | 1 | 1 |
_tCase problem scheduling a golf league, _p23. |
| 970 | 1 | 1 |
_tAppendix 1.1 the management scientist software, _p24. |
| 970 | 1 | 1 |
_tAppendix 1.2 using excel for breakeven analysis, _p26. |
| 970 | 1 | 2 |
_tAn introduction to linear programming, _p30. |
| 970 | 1 | 1 |
_tA simple maximization problem, _p32. |
| 970 | 1 | 1 |
_tA problem formulation, _p33. |
| 970 | 1 | 1 |
_tMathematical statement of the par, inc., problem, _p35. |
| 970 | 1 | 1 |
_tGraphical solution procedure, _p37. |
| 970 | 1 | 1 |
_tA note on graphing lines, _p46. |
| 970 | 1 | 1 |
_tSummary of the graphical solution procedure for maximization problems, _p48. |
| 970 | 1 | 1 |
_tSlack variables, _p49. |
| 970 | 1 | 1 |
_tExtreme points and the optimal solution, _p50. |
| 970 | 1 | 1 |
_tComputer solutin of the par, inc., problem, _p52. |
| 970 | 1 | 1 |
_tInterpretation of computer outtput, _p53. |
| 970 | 1 | 1 |
_tA simple minimization problem, _p55. |
| 970 | 1 | 1 |
_tSummary of the graphical solution procedure for minimization problems, _p57. |
| 970 | 1 | 1 |
_tSurplus variables, _p58. |
| 970 | 1 | 1 |
_tComputer solution of the M&D chemicals problem, _p59. |
| 970 | 1 | 1 |
_tSpecial cases, _p60. |
| 970 | 1 | 1 |
_tAlternative optimal solutions, _p60. |
| 970 | 1 | 1 |
_tInfeasibility, _p62. |
| 970 | 1 | 1 |
_tUnbounded, _p63. |
| 970 | 1 | 1 |
_tGeneral linear programming notation, _p65. |
| 970 | 0 | 1 |
_aSummary, _p66. |
| 970 | 0 | 1 |
_aGlossary, _p68. |
| 970 | 0 | 1 |
_aProblems, _p69. |
| 970 | 1 | 1 |
_tCase problem 1 workload balancing, _p84. |
| 970 | 1 | 1 |
_tCase problem 2 production strategy, _p85. |
| 970 | 1 | 1 |
_tCase problem 3 hart venture capital, _p86. |
| 970 | 1 | 1 |
_tAppendix 2.1 solving linear programs with the management scientist, _p87. |
| 970 | 1 | 1 |
_tAppendix 2.2 solving linear programs with LINGO, _p88. |
| 970 | 1 | 1 |
_tAppendix 2.3 solving linear programs with Excel, _p89. |
| 970 | 1 | 2 |
_tLinear programming: sensitivity analysis and interpretation of solution, _p94. |
| 970 | 1 | 1 |
_tIntroduction to sensitivity analysis, _p96. |
| 970 | 1 | 1 |
_tGraphical sensitivity analysis, _p97. |
| 970 | 1 | 1 |
_tObjective function coefficients, _p97. |
| 970 | 1 | 1 |
_tRight-hand sides, _p102. |
| 970 | 1 | 1 |
_tSensitivity analysis: computer solution, _p105. |
| 970 | 1 | 1 |
_tInterpretation of computer output, _p105. |
| 970 | 1 | 1 |
_tSimultaneous changes, _p108. |
| 970 | 1 | 1 |
_tInterpretation of computer output- a second example, _p110. |
| 970 | 1 | 1 |
_tCautionary note on the interpretation of dual prices, _p112. |
| 970 | 1 | 1 |
_tMore than two decision variables, _p113. |
| 970 | 1 | 1 |
_tThe modified par, inc., problem, _p113. |
| 970 | 1 | 1 |
_tThe bluegrass farms problem, _p118. |
| 970 | 1 | 1 |
_tFormulation of the bluegrass farms problem, _p118. |
| 970 | 1 | 1 |
_tComputer solution and interpretation for the bluegrass farms problem, _p120. |
| 970 | 1 | 1 |
_tThe electronic communication problem, _p123. |
| 970 | 1 | 1 |
_tProblem formulation, _p124. |
| 970 | 1 | 1 |
_tComputer solution and interpretation, _p125. |
| 970 | 0 | 1 |
_aSummary, _p128. |
| 970 | 0 | 1 |
_aGlossary, _p129. |
| 970 | 0 | 1 |
_aProblems, _p129. |
| 970 | 1 | 1 |
_tCase problems 1 product mix, _p151. |
| 970 | 1 | 1 |
_tCase problems 2 ivestment strategy, _p152. |
| 970 | 1 | 1 |
_tCase problems 3 truck leasing strategy, _p153. |
| 970 | 1 | 1 |
_tAppendix 3.1 sensitivity analysis with Excel, _p154. |
| 970 | 1 | 1 |
_tAppendix 3.2 sensitivity analysis with LINGO, _p157. |
| 970 | 1 | 2 |
_tLinear programming applications in marketing, finance and operations management, _p159. |
| 970 | 1 | 1 |
_tMarketing applications, _p160. |
| 970 | 1 | 1 |
_tMedia selection, _p161. |
| 970 | 1 | 1 |
_tMarketing research, _p164. |
| 970 | 1 | 1 |
_tFinancial applications, _p166. |
| 970 | 1 | 1 |
_tPortfolio selection, _p166. |
| 970 | 1 | 1 |
_tFinancial planning, _p170. |
| 970 | 1 | 1 |
_tOperations management applications, _p174. |
| 970 | 1 | 1 |
_tA make-or-buy decision, _p174. |
| 970 | 1 | 1 |
_tProduction scheduling, _p178. |
| 970 | 1 | 1 |
_tWorkforce assignment, _p183. |
| 970 | 1 | 1 |
_tBlending problems, _p189. |
| 970 | 0 | 1 |
_aSummary, _p194. |
| 970 | 0 | 1 |
_aProblems, _p194. |
| 970 | 1 | 1 |
_tCase problems 1 planning an advertising campaign, _p207. |
| 970 | 1 | 1 |
_tCase problems 2 phoenix computer, _p208. |
| 970 | 1 | 1 |
_tCase problems 3 textile mill scheduling, _p209. |
| 970 | 1 | 1 |
_tCase problems 4 workforce scheduling, _p210. |
| 970 | 1 | 1 |
_tCase problems 5 duke energy coal allocation, _p211. |
| 970 | 1 | 1 |
_tAppendix 4.1 excel solution of Hewlitt corporation financial planning problem, _p214. |
| 970 | 1 | 2 |
_tAdvanced linear programming applications, _p218. |
| 970 | 1 | 1 |
_tData envelopment analysis, _p219. |
| 970 | 1 | 1 |
_tEvaluating the performance of hospitals, _p220. |
| 970 | 1 | 1 |
_tOverview of the DEA Approach, _p220. |
| 970 | 1 | 1 |
_tDEA linear programming model, _p221. |
| 970 | 1 | 1 |
_tSummary of the DEA Approach, _p226. |
| 970 | 1 | 1 |
_tRevenue management, _p227. |
| 970 | 1 | 1 |
_tPortfolio models and asset allocation, _p233. |
| 970 | 1 | 1 |
_tA portfolio of mutual funds, _p233. |
| 970 | 1 | 1 |
_tConservation portfolio, _p234. |
| 970 | 1 | 1 |
_tModerate risk portfolio, _p237. |
| 970 | 1 | 1 |
_tGame theory, _p241. |
| 970 | 1 | 1 |
_tCompeting for market share, _p241. |
| 970 | 1 | 1 |
_tIdentifying a pure strategy solution, _p243. |
| 970 | 1 | 1 |
_tIdentifying a mixed strategy solution, _p244. |
| 970 | 0 | 1 |
_aSummary, _p252. |
| 970 | 0 | 1 |
_aGlossary, _p252. |
| 970 | 0 | 1 |
_aProblems, _p253. |
| 970 | 1 | 2 |
_tDistribution and network models, _p260. |
| 970 | 1 | 1 |
_tTransportation problem, _p261. |
| 970 | 1 | 1 |
_tProblem variations, _p264. |
| 970 | 1 | 1 |
_tA general linear programming model, _p266. |
| 970 | 1 | 1 |
_tAssignment problem, _p268. |
| 970 | 1 | 1 |
_tProblem variations, _p271. |
| 970 | 1 | 1 |
_tA general linear programming model, _p271. |
| 970 | 1 | 1 |
_tShortest-route problem, _p280. |
| 970 | 1 | 1 |
_tA general linear programming model, _p282. |
| 970 | 1 | 1 |
_tMaximal flow problem, _p283. |
| 970 | 1 | 1 |
_tA production and invertory application, _p287. |
| 970 | 0 | 1 |
_aSummary, _p290. |
| 970 | 0 | 1 |
_aGlossary, _p291. |
| 970 | 0 | 1 |
_aProblems, _p292. |
| 970 | 1 | 1 |
_tCase problem 1 solutions plus, _p308. |
| 970 | 1 | 1 |
_tCase problem 2 distribution system design, _p309. |
| 970 | 1 | 1 |
_tAppendix 6.1 Excel solution of transportation, assignment, and transshipment problems, _p311. |
| 970 | 1 | 2 |
_tInteger linear programming, _p318. |
| 970 | 1 | 1 |
_tTypes of integer linear programming models, _p320. |
| 970 | 1 | 1 |
_tGraphical and computer solutions for an all-integer linear program, _p322. |
| 970 | 1 | 1 |
_tGraphical solution of the LP relaxation, _p323. |
| 970 | 1 | 1 |
_tRounding to Obtain an integer solution, _p324. |
| 970 | 1 | 1 |
_tGraphical solution of the All-integer problem, _p324. |
| 970 | 1 | 1 |
_tUsing the LP relaxation to establish bounds, _p324. |
| 970 | 1 | 1 |
_tComputer solution, _p326. |
| 970 | 1 | 2 |
_tApplication involving 0-1 variables, _p326. |
| 970 | 1 | 1 |
_tCapital budgetting, _p327. |
| 970 | 1 | 1 |
_tFixed cost, _p328. |
| 970 | 1 | 1 |
_tDistribution system design, _p330. |
| 970 | 1 | 1 |
_tBank location, _p334. |
| 970 | 1 | 1 |
_tProduct design and market share optimization, _p338. |
| 970 | 1 | 1 |
_tModelling flexibility provided by 0-1 integer variables, _p343. |
| 970 | 1 | 1 |
_tMultiple-choice and mutually exclusive constraints, _p343. |
| 970 | 1 | 1 |
_tk out of n alternatives constraint, _p344. |
| 970 | 1 | 1 |
_tConditional and corequisite constraints, _p344. |
| 970 | 1 | 1 |
_tA cautionary note about sensitivity analysis, _p346. |
| 970 | 0 | 1 |
_aSummary, _p346. |
| 970 | 0 | 1 |
_aGlossary, _p347. |
| 970 | 1 | 1 |
_tCase problems 1 textbook publishing, _p359. |
| 970 | 1 | 1 |
_tCase problems 2 yeager national bank, _p360. |
| 970 | 1 | 1 |
_tCase problems 3 production scheduling with changeover costs, _p361. |
| 970 | 1 | 1 |
_tAppendix 7.1 Excel solution of integer linear programs, _p362. |
| 970 | 1 | 2 |
_tNonlinear optimization models, _p366. |
| 970 | 1 | 1 |
_tA production application-Par, Inc., revisited, _p368. |
| 970 | 1 | 1 |
_tModeling flexibility provided by 0-1 integer variables, _p368. |
| 970 | 1 | 1 |
_tAn unconstrained problem, _p368. |
| 970 | 1 | 1 |
_tA constrained problem, _p369. |
| 970 | 1 | 1 |
_tLocal and global optima, _p372. |
| 970 | 1 | 1 |
_tDual prices, _p375. |
| 970 | 1 | 1 |
_tConstructing an index fund, _p375. |
| 970 | 1 | 1 |
_tMarkowitz portfolio model, _p379. |
| 970 | 1 | 1 |
_tBlending: the pooling problem, _p382. |
| 970 | 1 | 1 |
_tForecasting adoption of a new product, _p387. |
| 970 | 0 | 1 |
_aSummary, _p392. |
| 970 | 0 | 1 |
_aGlossary, _p392. |
| 970 | 0 | 1 |
_aProblems, _p393. |
| 970 | 1 | 1 |
_tCase problem portfolio optimization with transaction consts, _p402. |
| 970 | 1 | 1 |
_tAppendix 8.1 solving nonlinear problems with LINGO, _p405. |
| 970 | 1 | 1 |
_tAppendix 8.2 solving nonlinear problems withExcel solver, _p407. |
| 970 | 1 | 2 |
_tProject scheduling: PERT/CPM, _p410. |
| 970 | 1 | 1 |
_tProject scheduling with known activity times, _p411. |
| 970 | 1 | 1 |
_tThe concept of a critical path, _p412. |
| 970 | 1 | 1 |
_tDetermining the critical path, _p414. |
| 970 | 1 | 1 |
_tContributions of PERT/CPM, _p418. |
| 970 | 1 | 1 |
_tSummary of the PERT/CPM critical path procedure, _p420. |
| 970 | 1 | 1 |
_tProject scheduling with uncertain activity times, _p421. |
| 970 | 1 | 1 |
_tThe daugherty porta-vac project, _p421. |
| 970 | 1 | 1 |
_tUncertain activity times, _p421. |
| 970 | 1 | 1 |
_tThe critical path, _p424. |
| 970 | 1 | 1 |
_tVariability in project completion time, _p429. |
| 970 | 1 | 1 |
_tConsidering time-cost trade-off's, _p429. |
| 970 | 1 | 1 |
_tCrashing activity times, _p429. |
| 970 | 1 | 1 |
_tLinear programming model for crashing, _p432. |
| 970 | 0 | 1 |
_aSummary, _p434. |
| 970 | 0 | 1 |
_aGlossary, _p435. |
| 970 | 0 | 1 |
_aProblems, _p435. |
| 970 | 1 | 1 |
_tCase problem R.C.C. Coleman, _p445. |
| 970 | 1 | 2 |
_tInventory models, _p447. |
| 970 | 1 | 1 |
_tEconomic order quantity (EOQ) model, _p448. |
| 970 | 1 | 1 |
_tThe how-much-to-order decision, _p453. |
| 970 | 1 | 1 |
_tThe when-to-order decision, _p454. |
| 970 | 1 | 1 |
_tSensitivity analysis for the EOQ model, _p455. |
| 970 | 1 | 1 |
_tExcel solution of the EOQ model, _p456. |
| 970 | 1 | 1 |
_tSummary of the EOQ model assumptions, _p457. |
| 970 | 1 | 1 |
_tEconomic production lot size model, _p458. |
| 970 | 1 | 1 |
_tTotal cost model, _p459. |
| 970 | 1 | 1 |
_tEconomic production lot size, _p461. |
| 970 | 1 | 1 |
_tInventory model with planned shortages, _p461. |
| 970 | 1 | 1 |
_tQuantity discounts for the EOQ model, _p466. |
| 970 | 1 | 1 |
_tSingle-period inventory model with probabilistic demand, _p468. |
| 970 | 1 | 1 |
_tJohnson shoe company, _p469. |
| 970 | 1 | 1 |
_tNationwide car rental, _p473. |
| 970 | 1 | 1 |
_tOrder-quantity, reorder point model with probabilistic demand, _p474. |
| 970 | 1 | 1 |
_tThe how-much-to-order decision, _p475. |
| 970 | 1 | 1 |
_tThe when-to-order decision, _p476. |
| 970 | 1 | 1 |
_tPeriodic review model with probabilistic demand, _p478. |
| 970 | 1 | 1 |
_tMore complex periodic review models, _p481. |
| 970 | 0 | 1 |
_aSummary, _p482. |
| 970 | 0 | 1 |
_aGlossary, _p483. |
| 970 | 0 | 1 |
_aProblems, _p484. |
| 970 | 1 | 1 |
_tCase problem 1 wagner fabricating company, _p492. |
| 970 | 1 | 1 |
_tCase problem 2 river city fire department, _p493. |
| 970 | 1 | 1 |
_tAppendix 10.1 development of the optimal order quantity (Q*) formula for the EOQ model, _p494. |
| 970 | 1 | 1 |
_tAppendix 10.2 development of the optimal lot size (Q*) formula for the production lot size model, _p495. |
| 970 | 1 | 2 |
_tWaiting line models, _p496. |
| 970 | 1 | 1 |
_tStructure of a waiting line system, _p498. |
| 970 | 1 | 1 |
_tSingle-channel waiting line, _p498. |
| 970 | 1 | 1 |
_tDistribution of arrivals, _p498. |
| 970 | 1 | 1 |
_tDistribution of service times, _p500. |
| 970 | 1 | 1 |
_tQueue discipline, _p501. |
| 970 | 1 | 1 |
_tSteady-state operation, _p501. |
| 970 | 1 | 1 |
_tSingle-channel waiting line model with poisson arrivals and exponential service time, _p502. |
| 970 | 1 | 1 |
_tOperating characteristics, _p502. |
| 970 | 1 | 1 |
_tOperating characteristics for the burger dome problem, _p503. |
| 970 | 1 | 1 |
_tManagers use of waiting line models, _p504. |
| 970 | 1 | 1 |
_tImproving the waiting line operation, _p504. |
| 970 | 1 | 1 |
_tExcel solution of waiting line model, _p505. |
| 970 | 1 | 1 |
_tMultiple-channel waiting line model with poisson arrivals and exponential service times, _p506. |
| 970 | 1 | 1 |
_tOperating characteristics, _p507. |
| 970 | 1 | 1 |
_tOperating characteristics for the burger dome problem, _p509. |
| 970 | 1 | 1 |
_tSome general relationships for waiting line models, _p511. |
| 970 | 1 | 1 |
_tEconomic analysis of waiting lines, _p513. |
| 970 | 1 | 1 |
_tOther waiting line models, _p514. |
| 970 | 1 | 1 |
_tSingle-channel waiting line model with poisson arrivals and arbitrary service times, _p515. |
| 970 | 1 | 1 |
_tOperating characteristics for the M/G/1 model, _p515. |
| 970 | 1 | 1 |
_tConstant service times, _p517. |
| 970 | 1 | 1 |
_tMultiple-channel model with poisson arrivals, arbitrary service times, and no waiting line, _p518. |
| 970 | 1 | 1 |
_tOperating charecteristics for the M/G/1 model with blocked customers cleared, _p518. |
| 970 | 1 | 1 |
_tCustomers cleared, _p518. |
| 970 | 1 | 1 |
_tWaiting line models with finite calling populations, _p520. |
| 970 | 1 | 1 |
_tOperating characteristics for the M/M/1 model with a finite calling population, _p521. |
| 970 | 0 | 1 |
_aSummary, _p523. |
| 970 | 0 | 1 |
_aGlossary, _p525. |
| 970 | 0 | 1 |
_aProblems, _p525. |
| 970 | 1 | 1 |
_tCase problem 1 regional airlines, _p533. |
| 970 | 1 | 1 |
_tCase problem 2 office equipment, Inc., _p534. |
| 970 | 1 | 2 |
_tSimulation, _p536. |
| 970 | 1 | 1 |
_tRisk analysis, _p539. |
| 970 | 1 | 1 |
_tWhat-if analysis, _p539. |
| 970 | 1 | 1 |
_tSimulation, _p541. |
| 970 | 1 | 1 |
_tSimulation of the portacom project, _p548. |
| 970 | 1 | 1 |
_tInventory simulation, _p552. |
| 970 | 1 | 1 |
_tButler inventory simulation, _p555. |
| 970 | 1 | 1 |
_tWaiting line simulation, _p557. |
| 970 | 1 | 1 |
_tHammodsport savings bank ATM waiting line, _p557. |
| 970 | 1 | 1 |
_tCustomer arrival times, _p558. |
| 970 | 1 | 1 |
_tCustomer service times, _p559. |
| 970 | 1 | 1 |
_tSimulation model, _p559. |
| 970 | 1 | 1 |
_tHammondsport savings bank atm simulation, _p563. |
| 970 | 1 | 1 |
_tSimulation with two ATMs, _p564. |
| 970 | 1 | 1 |
_tSimulation results with two ATMs, _p566. |
| 970 | 1 | 1 |
_tOther simulation issues, _p568. |
| 970 | 1 | 1 |
_tComputer implementation, _p568. |
| 970 | 1 | 1 |
_tVerification and validation, _p569. |
| 970 | 1 | 1 |
_tAdvantages and disadvantages of using simulation, _p569. |
| 970 | 0 | 1 |
_aSummary, _p570. |
| 970 | 0 | 1 |
_aGlossary, _p571. |
| 970 | 0 | 1 |
_aProblems, _p572. |
| 970 | 1 | 1 |
_tCase problem 1 tri-state corporation, _p579. |
| 970 | 1 | 1 |
_tCase problem 2 harbor dunes golf course, _p581. |
| 970 | 1 | 1 |
_tCase problem 3 country beverage drive-thru, _p582. |
| 970 | 1 | 1 |
_tAppendix 12.1 simulation with excel, _p584. |
| 970 | 1 | 1 |
_tAppendix 12.2 simulation using crystal ball, _p590. |
| 970 | 1 | 2 |
_tDecision analysis, _p595. |
| 970 | 1 | 1 |
_tProblem formulation, _p597. |
| 970 | 1 | 1 |
_tInfluence diagrams, _p598. |
| 970 | 1 | 1 |
_tPayoff tables, _p598. |
| 970 | 1 | 1 |
_tDecision trees, _p598. |
| 970 | 1 | 1 |
_tDecision making without probabilities, _p600. |
| 970 | 1 | 1 |
_tOptimistic approach, _p600. |
| 970 | 1 | 1 |
_tConservative approach, _p600. |
| 970 | 1 | 1 |
_tMinimax regret approach, _p601. |
| 970 | 1 | 1 |
_tDecision making with probabilities, _p602. |
| 970 | 1 | 1 |
_tExpected value of perfect information, _p605. |
| 970 | 1 | 1 |
_tRisk analysis and sensitivity analysis, _p607. |
| 970 | 1 | 1 |
_tRisk analysis, _p607. |
| 970 | 1 | 1 |
_tSensitivity analysis, _p607. |
| 970 | 1 | 1 |
_tDecision analysisi with sample information, _p612. |
| 970 | 1 | 1 |
_tInfluence diagram, _p612. |
| 970 | 1 | 1 |
_tDecision tree, _p613. |
| 970 | 1 | 1 |
_tDecision strategy, _p615. |
| 970 | 1 | 1 |
_tRisk profile, _p619. |
| 970 | 1 | 1 |
_tExpected value of sample information, _p621. |
| 970 | 1 | 1 |
_tEfficiency of sample information, _p622. |
| 970 | 1 | 1 |
_tComputing branch probabilities, _p622. |
| 970 | 0 | 1 |
_aSummary, _p626. |
| 970 | 0 | 1 |
_aGlossary, _p627. |
| 970 | 0 | 1 |
_aProblems, _p629. |
| 970 | 1 | 1 |
_tCase problem 1 property pruchase strategy, _p642. |
| 970 | 1 | 1 |
_tCase problem 2 lawsuit defense strategy, _p643. |
| 970 | 1 | 1 |
_tAppendix 13.1 decision analysis with treeplan, _p644. |
| 970 | 1 | 2 |
_tMulticriteria decisions, _p650. |
| 970 | 1 | 1 |
_tGoal programming: formulation and graphical solution, _p651. |
| 970 | 1 | 1 |
_tDeveloping the constraints and the goal equations, _p652. |
| 970 | 1 | 1 |
_tDeveloping on objective function with preemptive priorities, _p654. |
| 970 | 1 | 1 |
_tGraphical solution procedure, _p655. |
| 970 | 1 | 1 |
_tGoal programming model, _p658. |
| 970 | 1 | 1 |
_tGoal programmin: solving more complex problems, _p659. |
| 970 | 1 | 1 |
_tSuncoast office supplies problem, _p659. |
| 970 | 1 | 1 |
_tFormulating the gaol equations, _p660. |
| 970 | 1 | 1 |
_tFormulating the objective function, _p661. |
| 970 | 1 | 1 |
_tComputer solution, _p662. |
| 970 | 1 | 1 |
_tScoring models, _p665. |
| 970 | 1 | 1 |
_tAnalytic hierarchy process, _p670. |
| 970 | 1 | 1 |
_tDeveloping the hierarchy, _p671. |
| 970 | 1 | 1 |
_tEstablishing priorities using AHP, _p672. |
| 970 | 1 | 1 |
_tPairwise comparisons, _p672. |
| 970 | 1 | 1 |
_tPairwise comparisons matrix, _p674. |
| 970 | 1 | 1 |
_tSynthesization, _p675. |
| 970 | 1 | 1 |
_tConsistency, _p676. |
| 970 | 1 | 1 |
_tOther pairwise comparisons for the car selection problem, _p678. |
| 970 | 1 | 1 |
_tUsing AHP to develop an overall priority ranking, _p680. |
| 970 | 0 | 1 |
_aSummary, _p681. |
| 970 | 0 | 1 |
_aGlossary, _p682. |
| 970 | 0 | 1 |
_aProblems, _p683. |
| 970 | 1 | 1 |
_tCase problem EZ Trailers, Inc., _p692. |
| 970 | 1 | 1 |
_tAppendix 14.1 scoring models with Excel, _p693. |
| 970 | 1 | 2 |
_tForecasting, _p695. |
| 970 | 1 | 1 |
_tComponents of a time series, _p698. |
| 970 | 1 | 1 |
_tTrend component, _p698. |
| 970 | 1 | 1 |
_tCyclical component, _p698. |
| 970 | 1 | 1 |
_tSeasonal component, _p699. |
| 970 | 1 | 1 |
_tIrregular component, _p700. |
| 970 | 1 | 1 |
_tSmoothing methods, _p700. |
| 970 | 1 | 1 |
_tMoving averages, _p700. |
| 970 | 1 | 1 |
_tWeighted moving averages, _p703. |
| 970 | 1 | 1 |
_tExponential smoothing, _p704. |
| 970 | 1 | 1 |
_tTrend projection, _p709. |
| 970 | 1 | 1 |
_tTrend and seasonal components, _p712. |
| 970 | 1 | 1 |
_tMultiplicative model, _p713. |
| 970 | 1 | 1 |
_tCalculating the seasonal indexes, _p713. |
| 970 | 1 | 1 |
_tDeseasonalizing the time series, _p717. |
| 970 | 1 | 1 |
_tUsing deseasonalized time series to identify trend, _p718. |
| 970 | 1 | 1 |
_tSeasonal adjustments, _p720. |
| 970 | 1 | 1 |
_tModels based on mothly data, _p721. |
| 970 | 1 | 1 |
_tCyclical component, _p721. |
| 970 | 1 | 1 |
_tRegression analysis, _p722. |
| 970 | 1 | 1 |
_tUsing regression analysis as a causal forecasting method, _p722. |
| 970 | 1 | 1 |
_tUsing regression analysis with time series data, _p727. |
| 970 | 1 | 1 |
_tQualitative approaches, _p729. |
| 970 | 1 | 1 |
_tDelphi method, _p729. |
| 970 | 1 | 1 |
_tExpert judgment, _p729. |
| 970 | 1 | 1 |
_tScenario writing, _p729. |
| 970 | 1 | 1 |
_tIntutive approaches, _p730. |
| 970 | 0 | 1 |
_aSummary, _p730. |
| 970 | 0 | 1 |
_aGlossary, _p731. |
| 970 | 0 | 1 |
_aProblems, _p732. |
| 970 | 1 | 1 |
_tCase problem 1 forecasting sales, _p741. |
| 970 | 1 | 1 |
_tCase problem 2 forecasting lost sales, _p742. |
| 970 | 1 | 1 |
_tAppendix 15.1 using excel for forecasting, _p743. |
| 970 | 1 | 1 |
_tAppendix 15.2 using CB predicttor for forecasting, _p745. |
| 970 | 1 | 2 |
_tMarkov processes, _p748. |
| 970 | 1 | 1 |
_tMarket share analysis, _p750. |
| 970 | 1 | 1 |
_tAccounts receivable analysis, _p757. |
| 970 | 1 | 1 |
_tFundamental matrix and associated calculations, _p759. |
| 970 | 1 | 1 |
_tEstablishing the allowanve for doubtful accounts, _p760. |
| 970 | 0 | 1 |
_aSummary, _p762. |
| 970 | 0 | 1 |
_aGlossary, _p763. |
| 970 | 0 | 1 |
_aProblems, _p763. |
| 970 | 1 | 1 |
_tCase probem deater's absorting state probabilities in blackjack, _p767. |
| 970 | 1 | 1 |
_tAppendix 16.1 matrix notation and operations, _p768. |
| 970 | 1 | 1 |
_tAppendix 16.2 matrix inversion with Excel, _p772. |
| 970 | 1 | 2 | _tLinear programming: simplex method on CD. |
| 970 | 1 | 1 |
_tAn algebraic overview of the simplex method, _p17-2. |
| 970 | 1 | 1 |
_tAlgebraic properties of the simplex-medthod, _p17-3. |
| 970 | 1 | 1 |
_tDetermining a basic solution, _p17-3. |
| 970 | 1 | 1 |
_tBasic feasible solution, _p17-4. |
| 970 | 1 | 1 |
_tTableau form, _p17-5. |
| 970 | 1 | 1 |
_tSetting up the initial simplex tableau, _p17-7. |
| 970 | 1 | 1 |
_tImproving the solution, _p17-10. |
| 970 | 1 | 1 |
_tCalculating the next tableau, _p17-12. |
| 970 | 1 | 1 |
_tInterpreting the results of an iteration, _p17-15. |
| 970 | 1 | 1 |
_tMoving toward a better solution, _p17-15. |
| 970 | 1 | 1 |
_tInterpreting the optimal solution, _p17-18. |
| 970 | 1 | 1 |
_tSummary of the simplex method, _p17-19. |
| 970 | 1 | 1 |
_tTableau form: the general case, _p17-20. |
| 970 | 1 | 1 |
_tGreater-than-or-equal-to constraints, _p17-20. |
| 970 | 1 | 1 |
_tEquality constraints, _p17-24. |
| 970 | 1 | 1 |
_tEliminating negative right-hand-side values, _p17-25. |
| 970 | 1 | 1 |
_tSummary of the steps to create tableau form, _p17-26. |
| 970 | 1 | 1 |
_tSolving a minimization problem, _p17-27. |
| 970 | 1 | 1 |
_tSpecial cases, _p17-29. |
| 970 | 1 | 1 |
_tInfesability, _p17-29. |
| 970 | 1 | 1 |
_tUnboundedness, _p17-31. |
| 970 | 1 | 1 |
_tAlternative optimal solutions, _p17-32. |
| 970 | 0 | 1 |
_aSummary, _p17-35. |
| 970 | 0 | 1 |
_aGlossary, _p17-36. |
| 970 | 0 | 1 |
_aProblems, _p17-37. |
| 970 | 1 | 2 | _tSimplex-based sensitivity analysis and duality On CD. |
| 970 | 1 | 1 |
_tSensitivity analysis with the simplex tableau, _p18-2. |
| 970 | 1 | 1 |
_tObjective function coefficients, _p18-2. |
| 970 | 1 | 1 |
_tRight-hand-side values, _p18-6. |
| 970 | 1 | 1 |
_tSimultaneous changes, _p18-13. |
| 970 | 1 | 1 | _tDuality 18-14. |
| 970 | 1 | 1 |
_tEconomic interpretation of the dual variables, _p18-16. |
| 970 | 1 | 1 |
_tUsing the dual to identify the primal solution, _p18-18. |
| 970 | 1 | 1 |
_tFinding the dual of any primal problem, _p18-18. |
| 970 | 0 | 1 |
_aSummary, _p18-20. |
| 970 | 0 | 1 |
_aGlossary, _p18-21. |
| 970 | 0 | 1 |
_aProblems, _p18-21. |
| 970 | 1 | 2 | _tSolution procedures for transportation and assignment pronlems On CD. |
| 970 | 1 | 1 |
_tTransportation simplex method: a special-purpose solution procedure, _p19-2. |
| 970 | 1 | 1 |
_tPhase I: finding an initial feasible solution, _p19-2. |
| 970 | 1 | 1 |
_tPhase II: Iterating to the optimal solution, _p19-7. |
| 970 | 1 | 1 |
_tSummary of the transportation simplex method, _p19-17. |
| 970 | 1 | 1 |
_tProblem variations, _p19-17. |
| 970 | 1 | 1 |
_tAssignment problem: a special-purpose solution procedure, _p19-18. |
| 970 | 1 | 1 |
_tFinding the minimum number of lines, _p19-21. |
| 970 | 1 | 1 |
_tProblem variations, _p19-21. |
| 970 | 0 | 1 |
_aGlossary, _p19-25. |
| 970 | 0 | 1 |
_aProblems, _p19-26. |
| 970 | 1 | 2 | _tMinimal spanning tree ON CD. |
| 970 | 1 | 1 |
_tA minimal spanning tree algorithm, _p20-2. |
| 970 | 0 | 1 |
_aGlossary, _p20-5. |
| 970 | 0 | 1 |
_aProblems, _p20-5. |
| 970 | 1 | 2 | _tDynamic programming On CD. |
| 970 | 1 | 2 | _tDynamic programming On CD. |
| 970 | 1 | 1 |
_tA shortest-route problem, _p21-2. |
| 970 | 1 | 1 |
_tDynamic programming notation, _p21-6. |
| 970 | 1 | 1 |
_tThe knapsack problem, _p21-10. |
| 970 | 1 | 1 |
_tA production and inventory control problem, _p21-16. |
| 970 | 0 | 1 |
_aSummary, _p21-20. |
| 970 | 0 | 1 |
_aGlossary, _p21-21. |
| 970 | 0 | 1 |
_aProblems, _p21-22. |
| 970 | 1 | 1 |
_tCase problem process design, _p21-26. |
| 970 | 1 | 1 |
_tAppendixes, _p773. |
| 970 | 1 | 1 |
_tAppendix A areas for the standard normal distribution, _p774. |
| 970 | 1 | 1 |
_tAppendix B values of e⁻λ, _p775. |
| 970 | 1 | 1 |
_tAppendix C references and bibliography, _p776. |
| 970 | 1 | 1 |
_tAppendix D self-test solutions and answers to even-numbered problems, _p778. |
| 970 | 0 | 1 |
_aIndex, _p807. |
| 999 |
_c5601 _d5601 |
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| 003 | KOHA | ||