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Chapters 16-18 Multiple-Goal Decision Analysis II

Chapters 16-18 Multiple-Goal Decision Analysis II

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Chapters 16-18 Multiple-Goal Decision Analysis II

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  1. Chapters 16-18Multiple-Goal Decision Analysis II • Goals as constraints • TPS example • Constrained optimization • Linear programming • System Analysis • Dealing with unquantifiable goals • Preference table • Screening matrix • Presentation techniques for mixed criteria

  2. TPS Decision Problem 5 • Option A operating system: • If one processor fails, system fails • Processor reliability: 0.99/hour • Mean time to repair: 30 min. • System reliability for N processors: • Rel (N) = (0.99)N • System availability for N processors • (prob. of failure per time period)(Avg. down time) Length of time period AV(N) = 1 – = 1 – (1-.99N)(30Min)/(60min) = 1–½ (1-.99N)

  3. E(N) trans/sec Rel, Av 1.00 E(N) 2400 2000 0.99 DSC = (SC)(E(N))(Av(N)) 1600 0.98 Av(N) 1200 0.97 800 0.96 400 0.95 Rel(N) 0.94 1 2 3 4 5 6 Number of processors, N TPS Reliability, Availability, and Performance

  4. 2400 2340 2328 2000 2196 1892 1600 DSC(N) = E(N)*Av(N) 1426 1200 800 796 400 1 2 3 4 5 6 Number of processors, N TPS Delivered System Capability

  5. Goals as Constraints • Can’t afford availability <.98 • Choose N to maximize E(N) = 80N (11-N) subject to AV(N) > .98 • Can’t afford delivered capacity < 1800 TR/sec • Choose N to maximize AV(N) subject to E(N) > 1800 • Comm. Line limits E(N) to < 1500 TR/sec; value of TR/sec = $500 (a); $1000(b)

  6. E < 1500 E < E(N) E < E(N) Av > 0.98 NV = .5E – 40N – 450 = 200 Optimal Solution for TV = 0.5E E=TV 1680 1620 NV = 190 1560 1500 NV = 180 1440 NV = 150 NV = 44 1380 N C 2 530 3 570 4 610

  7. Optimal Solution for TV = E E < 1500 E < E(N) E < E(N) Av > 0.98 NV = E – 40N – 450 = 970 E=TV 1680 1620 1560 NV = 930 1500 1440 NV = 910 NV = 870 1380 N C 2 530 3 570 4 610

  8. General Optimal Decision Problem With Constraints • Choose values of the decision variables • X1, X2, …, Xn • So as to maximize the objective function • f(X1, X2, …, Xn) • Subject to the constraints • g1 (X1, X2, …, Xn) < b, • g2 (X1, X2, …, Xn) < b2 • … • gm (X1, X2, …, Xn) < bm

  9. Optimal Solution:Necessary and Sufficient Conditions • The optimal solution (X1, X2, …, Xn)max • And the optimal value Vmax • Are characterized by the necessary and sufficient conditions • (X1, X2, …, Xn)max is a feasible point on the isoquant f(X1, X2, …, Xn) = Vmax • If V> Vmax, then its isoquant f(X1, X2, …, Xn) = V does not contain any feasible points

  10. g1(x1, … , xn) = b1 xn Objective function isoquants (Decision variable) Decision space Optimal solution = v5 = v4 = vmax = v3 = v2 Feasible set f(x1, … , xn) = v1 g2(x1, … , xn) = b2 Feasible point x1 Infeasible point (Decision variable) g3(x1, … , xn) = b3 Geometric View

  11. The Linear Programming Problem • Choose X1, X2, …, Xn • So as to maximize • C1X1 + C2X2 + … + CnXn • Subject to the costraints • a11X1 + a12X2 + … + a1nXn < b1 • a21X1 + a22X2 + … + a2nXn < b2 • … • am1X1 + am2X2 + … + amnXn < bm • X1 > 0, X2 > 0, …, Xn > 0

  12. Universal Software, Inc. • 16 analysts, 24 programmers, 15 hr/day computer • Text-processing systems • 2 analysts, 6 prog’rs, 3 hr/day comp $20K profit • Process control systems • 4 analysts, 2 prog’rs, 3 hr/day comp $30K profit • How many of each should Universal develop to maximize profit?

  13. Solution Steps • What objective are we trying to optimize? • What decisions do we control which affect the objective? • What items dictate constraints on our range of choices? • How are the values of the objective function related to the values of the decision variables? • What decision provides us with the optimal value of the objective function?

  14. Feasible Set: Universal Software X2 6 4 Feasible Set 2 6 8 2 4 X1 6x1 + 2x2 3x1 + 3x2 2x1 + 4x2

  15. Optimal Solution: Universal Software Optimal solution 4 20x1 + 30x2 = 150 3 20x1 + 30x2 = 120 20x1 + 30x2 = 130 2 Feasible set 20x1 + 30x2 = 60 1 1 2 3 4 5 6

  16. Mathematical Optimization System Analysis [Quade68] Formulation What objectives are we trying to optimize or satisfy? Search What decisions do we control which affect our objectives? What items dictate constraints on our range of choices? Evaluation What criteria should we use to evaluate the alternatives? How are the values of the criterion function related to the values of the decision variables which define the alternatives? What choice provides us with the best criterion value? Interpretation How sensitive is the decision to assumptions made during the analysis? Are there alternative decisions providing satisfactory results with less sensitivity to these assumptions? Formulation Clarifying the objectives, defining the issues of concern, limiting the problem. Search Looking for data and relationships, as well as alternative programs of action that have some chance of solving the problem. Evaluation Building various models, using them to predict the consequences that are likely to follow from each choice of alternatives, and then comparing the alternatives in terms of these consequences. Interpretation Using the predictions obtained from the models, and whatever other information or insight is relevant, to compare the alternatives further, derive conclusions about them, and indicate a course of action. Iteration Iteration

  17. TPS Decision Problem 6 • Cost of option B OS with switchover, restart: $150K • Cost of option B OS from vendor: $135K • Which should we choose? • Key personnel availability • Staff morale and growth • Controllability • Ease of maintenance

  18. Presentation Techniques • Unquantifiable criteria • Criterion summaries • Preference table • Screening matrix • Mixed Criteria • Tabular methods • Cost vs. capability graph • Polar graph • Bar charts

  19. Summary – Goals As Constraints II • Adding constraints can simplify multiple-goal decision problems • System analysis approach very similar to constrained optimization • 6-step approach • Sensitivity analysis • Satisficing • Unquantifiable goals require subjective resolution • But effective presentation techniques can help decision process