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References

  1. R.S. Barr and B.L. Hickman (1993). Reporting Computational Experiments with Parallel Algorithms: Issues, Measures and Experts’ Opinions. ORSA Jl. Computing 5, 2–18.

    Google Scholar 

  2. D.P. Bertsekas and J. Tsitsiklis (1989). Parallel and Distributed Computation: Numerical Methods. Prentice-Hall, Englewood Cliffs, New Jersey.

    Google Scholar 

  3. J. Eckstein (1993). Large–Scale Parallel Computing, Optimization, and Operations Research: A Survey. ORSA Computer Science Technical Section Newsletter 14(2), 1, 8–12.

    Google Scholar 

  4. M.J. Flynn (1972). Some Computer Organizations and their Effectiveness. IEEE Transactions Computers C-21, 948–960.

    Google Scholar 

  5. G.A.P. Kindervater and J.K. Lenstra (1988). Parallel Computing in Combinatorial Optimization. Annals Operations Research 14, 245–289.

    Google Scholar 

  6. V. Kumar and A. Gupta (1994). Analyzing Scalability of Parallel Algorithms and Architectures. Jl. Parallel and Distributed Computing.

    Google Scholar 

  7. F.T. Leighton (1991). Introduction to Parallel Algorithms and Architectures: Arrays Trees, and Hypercubes. Morgan Kaufmann, San Mateo, California.

    Google Scholar 

  8. M. Metcalf and J. Reid (1990). Fortran 90 Explained. Oxford University Press, Oxford, United Kingdom.

    Google Scholar 

  9. S.A. Zenios (1989). Parallel Numerical Optimization: Current Status and an Annotated Bibliography. ORSA Jl Computing 1, 20–43.

    Google Scholar 

  10. S.A. Zenios (1994). Parallel and Supercomputing in the Practice of Management Science. Interfaces, 24, 122–140.

    Google Scholar 

  11. Bank, B., J. Guddat, D. Klatte, B. Kummer, and T. Tammer (1982). Non-linear parametric optimization. Akademie-Verlag, Berlin.

    Google Scholar 

  12. Bradley, S.P., A.C. Hax, and T.L. Magnanti (1977). Applied Mathematical Programming. Addison-Wesley, Reading, Massachusetts.

    Google Scholar 

  13. Dinkelbach, W. (1969). Sensitivitsdnalysen und parametrische Programmierung. Springer Verlag, Berlin.

    Google Scholar 

  14. Gal, T. (1973). Betriebliche Entscheidungsprob-leme, Sensitivitsdnalyse und parametrische Programmierung. W. de Gruyter, Berlin.

    Google Scholar 

  15. Gal, T. (1979). Postoptimal analyses, parametric programming and related topics. McGraw Hill, New York.

    Google Scholar 

  16. Gal, T. (1980). “A ‘historiogramme’ of parametric programming.” Jl. Operational Research Society, 31, 449–451.

    Google Scholar 

  17. Gal, T. (1983). “A note on the history of parametric programming”. Jl. Operational Research Society, 34, 162–163.

    Google Scholar 

  18. Gal, T. (1992). “Putting the LP survey into perspective.” OR/MS Today, December, 93.

    Google Scholar 

  19. Gal, T. (1994a). “Selected Bibhography on Degeneracy.” Annals Operations Research.

    Google Scholar 

  20. Gal, T. (1994b). Postoptimal analyses and parametric programming. Revised and updated edition. W. de Gruyter, Berlin.

    Google Scholar 

  21. Gass, S.I. (1985). Linear Programming, 5th ed. McGraw-Hill, New York.

    Google Scholar 

  22. Gass, S.I. and T. L. Saaty (1955). “The parametric objective function.” Naval Research Logistics Quarterly, 2, 39–45.

    Google Scholar 

  23. Guddat, J., F. Guerra Vazquez, H. Th. Jongen (1991). Parametric optimization: singularities, pathfollowing and jumps. B.G. Teubner, Stuttgart, and John Wiley, New York.

    Google Scholar 

  24. Kruse, H.-J. (1986). Degeneracy graphs and the neighborhood problem. Lecture Notes in economics and mathematical systems No. 260. Springer Verlag, Berlin.

    Google Scholar 

  25. Manne, A.S. (1953). “Notes on Parametric Linear Programming.” RAND Report P-468. The Rand Corporation, Santa Monica, California.

    Google Scholar 

  26. Saaty, T.L., and S.I. Gass (1954). “The Parametric Objective Function, Part I.” Operations Research, 2, 316–319.

    Google Scholar 

  27. Steuer, R.E. (1986). Multiple criteria optimization: Theory, computation, and application. John Wiley, New York.

    Google Scholar 

  28. Wendell, R.E. (1985). “The Tolerance Approach to Sensitivity Analysis in Linear Programming.” Management Science, 31, 564–578.

    Google Scholar 

  29. Baker, T.E. and L.S. Lasdon (1985). “Successive Linear Programming at Exxon,” Management Science, 31, 264–274.

    Google Scholar 

  30. Baker, T.E. (1994). “An Integrated Approach to Planning and Scheduling,” Foundations of Computer-Aided Process Operations, D.W.T. Rippin, ed., CACHE, Austin, Texas, 237–251.

    Google Scholar 

  31. Bammi, D. (1990). “Northern Border Pipeline Logistics Simulation,” Interfaces, 20 (3), 1–13.

    Google Scholar 

  32. Beale, E.M.L. (1978). “Nonlinear Programming Using a General Mathematical Programming System,” in Design and Implementation of Optimization Software, H.J. Greenberg, ed., Sijthoff and Noordhoff, The Netherlands, 259–279.

    Google Scholar 

  33. Bodington, C.E. and T.E. Baker (1990). “A History of Mathematical Programming in the Petroleum Industry,” Interfaces, 20 (4), 117–127.

    Google Scholar 

  34. Bodington, C.E. (1995). Planning, Scheduling and Control Integration in the Process Industries, McGraw-Hill, New York.

    Google Scholar 

  35. Brown, G.G. et al (1987). “Real-Time, Wide Area Dispatch of Mobil Tank Trucks,” Interfaces, 17 (1), 107–120.

    Google Scholar 

  36. Charnes, A., W.W. Cooper and B. Mellon (1952). “Blending Aviation Gasoline–A Study in Programming Interdependent Activities in an Integrated Oil Company,” Econometrica, 20 (2), 135–139.

    Google Scholar 

  37. Ciriani, T.A. and R.C. Leachman (1993). Optimization in Industry, John Wiley, New York.

    Google Scholar 

  38. de Geus, A.P. (1988). “Planning As Learning,” Harvard Business Review, 88 (2), 70–77.

    Google Scholar 

  39. Edgar, T.F. and D.M. Himmelblau (1988). Optimization of Chemical Processes, McGraw-Hill, New York.

    Google Scholar 

  40. Findlay, P.L. et al. (1989). “Optimization of the Daily Production Rates for an Offshore Oilfield,” Jl Operational Research Society, 40, 1079–1088.

    Google Scholar 

  41. Griffith, R.E. and R.A. Stewart (1961). “A Nonlinear Programming Technique for the Optimization of Continuous Processing Systems,” Management Science, 1, 379–392.

    Google Scholar 

  42. Hansen, P. et al (1992). “Location and Sizing of Offshore Platforms for Oil Exploration,” European Journal Operational Research, 58 (2), 202–214.

    Google Scholar 

  43. Higgins, J.G. (1993). “Planning for Risk and Uncertainty in Oil Exploration,” Long Range Planning, 26 (1), 111–122.

    Google Scholar 

  44. Klingman, D. et al (1987). “The Successful Deployment of Management Science Throughout Citgo Petroleum Corporation,” Interfaces, 17 (1), 4–25.

    Google Scholar 

  45. Lasdon, L.S. and A.D. Waren (1980). “A Survey of Nonlinear Programming Applications,” Operations Research, 28, 102–1073.

    Google Scholar 

  46. Manne, A. (1958). “A Linear Programming Model of the US Petroleum Refining Industry,” Econometrica, 26 (1), 67–106.

    Google Scholar 

  47. Miller, D. et al (1994). “A Modular System for Scheduling Chemical Plant Production,” Foundations of Computer-Aided Process Operations, D.W.T. Rippin ed., CACHE, Austin, Texas, 355– 372.

    Google Scholar 

  48. Miller, D. (1987). “An Interactive, Computer-Aided Ship Scheduling System,” European Jl Operational Research, 32 (3), 363–379.

    Google Scholar 

  49. Palmer, K.H. et al (1984). A Model-Management Framework for Mathematical Programming, John Wiley, New York.

    Google Scholar 

  50. Power, M. (1992). “Simulating Natural Gas Discoveries,” Interfaces, 22 (2), 38–51.

    Google Scholar 

  51. Symonds, G.H. (1955). Linear Programming — The Solution of Refinery Problems, Esso Standard Oil Company, New York.

    Google Scholar 

  52. Asmussen, S. (1992), “Phase–type representations in random walk and queueing problems,” Annals Probability, 20, 772–789.

    Google Scholar 

  53. Asmussen, S., Haggstrom, O., and Nerman, O. (1992), “EMPHT — A program for fitting phase-type distributions,” in Studies in Statistical Quality Control and Reliability, Mathematical Statistics, Chalmers University and University of Gteborg, Sweden.

    Google Scholar 

  54. O’Cinneide, C.A. (1990), “Characterization of phase-type distributions,” Stochastic Models, 6, 1– 57.

    Google Scholar 

  55. Johnson, M.A. (1993), “Selecting parameters of phase distributions: Combining nonlinear programming, heuristics, and Erlang distributions,” ORSA Jl Computing, 5, 69–83.

    Google Scholar 

  56. Johnson, M.A. (1993), “An empirical study of queueing approximations based on phase–type distributions,” Stochastic Models, 9, 531–561.

    Google Scholar 

  57. Neuts, M.F. (1981), Matrix–Geometric Solutions in Stochastic Models: An Algorithmic Approach. The Johns Hopkins University Press, Baltimore. Reprinted by Dover Publications, 1994.

    Google Scholar 

  58. Pagano, M.E. and Neuts, M.F. (1981), “Generating Random Variates from a Distribution of Phase Type,” 1981 Winter Simulation Conference Proceedings, T.I. Oren, C.M. Delfosse, C.M. Shub (eds.), 381–387.

    Google Scholar 

  59. Schmickler, L. (1992), “MEDA: Mixed Erlang distributions as phase–type representations of empirical distribution functions,” Stochastic Models, 8, 131–156.

    Google Scholar 

  60. Cox, D.R. and V. Isham (1980). Point Processes, Chapman and Hall, New York.

    Google Scholar 

  61. Franken, P., D. Knig, U. Arndt, and V. Schmidt (1981). Queues and Point Processes, Akademie-Verlag, Berlin.

    Google Scholar 

  62. Gnedenko, B.V., Yu.K. Belyaev, and A.D. Solovyev (1969). Mathematical Methods of Reliability Theory, Academic Press, New York.

    Google Scholar 

  63. Grigelionis, B.I. (1964). “Limit Theorems for Sums of Renewal Processes,” in Cybernetics in the Service of Communism, vol. 2: Reliability Theory and Queueing Theory, A.I. Berg, N.G. Bruevich, and B.V. Gnedenko, eds. Energiya, Moscow, 246–266.

    Google Scholar 

  64. Khintchine, A.Ya. (1960). Mathematical Methods in the Theory of Queueing, Charles Griffin, London.

    Google Scholar 

  65. Osokov, G.A. (1956). “A Limit Theorem for Flows of Similar Events,” Theory Probability & Its Applies. 1, 246–255.

    Google Scholar 

  66. Baker v. Carr, 369 U.S. 186 (1962).

    Google Scholar 

  67. Balinski, M.L. and H.P. Young (1982), Fair Representation, Meeting the Ideal of One Man, One Vote, Yale University Press, New Haven, Connecticut.

    Google Scholar 

  68. Barkan, J.D. and J.E. Bruno (1972), “Operations Research in Planning Political Campaign Strategies,” Operations Research, 20, 925–941.

    Google Scholar 

  69. Browdy, M.H. (1990), “Computer Models and Vost-Bandemer Redistricting,” Yale Law Journal, 99, 1379–1398.

    Google Scholar 

  70. Ernst, L.R. (1994), “Apportionment Methods for the House of Representatives and the Court Challenges,” Management Science, 40, 1207–1227.

    Google Scholar 

  71. Fishburn, P.C. and J.D.C. Little (1988), “An Experiment in Approval Voting,” Management Science, 34, 555–568.

    Google Scholar 

  72. Garfinkel, R.S. and G.L. Nemhauser (1969), “Optimal Political Districting by Implicit Enumeration Techniques,” Management Science, 16, B495–B508.

    Google Scholar 

  73. Hess, S.W. (1971), “One–Man One–Vote and County Political Integrity: Apportion to Satisfy Both,” Jurimetrics Journal, 11, 123–141.

    Google Scholar 

  74. Hess, S.W., J.B. Weaver, H.J. Seigfeldt, J.N. Whelan, and P.A. Zitlau (1965), “Nonpartisan Political Redistricting by Computer,” Operations Research, 13, 998–1006.

    Google Scholar 

  75. Miniter, R. (1992), “Running Against the Computer; Stephen Solarz and the Technician–Designed Congressional District,” The Washington Post, September 20, C5.

    Google Scholar 

  76. Nygreen, B. (1988), “European Assembly Constituencies for Wales–Comparing of Methods for Solving a Political Districting Problem,” Mathematical Programming, 42, 159–169.

    Google Scholar 

  77. Savas, E.S., H. Lipton, and L. Burkholz (1972), “Implementation of an OR Approach for Forming Efficient Districts,” Operations Research, 20, 46– 48.

    Google Scholar 

  78. Van Biema, D. (1993), “Snakes or Ladders,” Time, July 12, 30–33.

    Google Scholar 

  79. Webster’s New Collegiate Dictionary, (1951). “Politics,” p. 654, Mirriam, New York.

    Google Scholar 

  80. Aneja Y.P., Chandra R. and Gunay E. (1989), “A Portfolio Approach to Estimating the Average Correlation Coefficient for the Constant Correlation Model,” Jl Finance, 44, 1435–1438.

    Google Scholar 

  81. Bawa, V.S., Brown S.J., and Klein R.W. (1979), Estimation Risk and Optimal Portfolio Choice, North Holland, Amsterdam.

    Google Scholar 

  82. Board, J.L.G. and Sutcliffe C.M.S. (1988), “Forced Diversification,” Quarterly Review Economics and Business, 28 (3), 43–52.

    Google Scholar 

  83. Board, J.L.G. and Sutcliffe C.M.S. (1994), “Estimation Methods in Portfolio Selection and the Effectiveness of Short Sales Restrictions: UK Evidence,” Management Science, 40, 516–534.

    Google Scholar 

  84. Borch, K. (1969), “A Note on Uncertainty and Indifference Curves,” Review Economic Studies, 36, 1–4.

    Google Scholar 

  85. Chopra V.K. and Ziemba W.T. (1993), “The Effect of Errors in Means Variances and Covariances on Optimal Portfolio Choice,” Jl. Portfolio Management, 19, No. 2, 6–13.

    Google Scholar 

  86. Cohen K.J. and Pogue J.A. (1967), “An Empirical Evaluation of Alternative Portfolio Selection Models,” Jl Business, 40, 166–193.

    Google Scholar 

  87. Constantinides G. and Malliaris G. (1995), “Portfolio Theory,” in Handbook of Finance, Jarrow, Maksimovic and Ziemba, eds., North-Holland, Amsterdam.

    Google Scholar 

  88. Efron B. and Morris C. (1975), “Data Analysis Using Stein’s Estimator and its Generalizations,” JL American Statistical Assoc., 70, 311– 319.

    Google Scholar 

  89. Efron B. and Morris C. (1977), “Stein’s Paradox in Statistics,” Scientific American, 236, No. 5, 119–127.

    Google Scholar 

  90. Elton E.J. and Gruber M.J. (1973), “Estimating the Dependence Structure of Share Prices Implications for Portfolio Selection,” Jl. Finance, 28, 1203–1232.

    Google Scholar 

  91. Elton, E.J., Gruber M.J., and Urich T.J. (1978), “Are Betas Best?,” Jl. Finance, 33, 1375–1384.

    Google Scholar 

  92. Fama E. (1971), “Risk, Return and Equilibrium,” Jl. Political Economy, 79, 30–55.

    Google Scholar 

  93. Fama E.F. (1976), Foundations of Finance, Basil Blackwell, Oxford.

    Google Scholar 

  94. Feldstein M. (1969), “Mean Variance Analysis in the Theory of Liquidity Preference and Portfolio Selection,” Review Economic Studies, 36, 5–12.

    Google Scholar 

  95. Ferson W. (1995), “Theory and Testing of Asset Pricing Models,” in Handbook of Finance, Jarrow, Maksimovic, and Ziemba, eds., North-Holland, Amsterdam.

    Google Scholar 

  96. Hodges S.D. (1976), “Problems in the Application of Portfolio Selection,” Omega, 4, 699–709.

    Google Scholar 

  97. Hodges S.D. and Brealey R.A. (1973), “Portfolio Selection in a Dynamic and Uncertain World,” Financial Analysts Jl, 29, March, 50–65.

    Google Scholar 

  98. Huang C.F. and Litzenberger R.H. (1988), Foundations for Financial Economics, North-Holland, Amsterdam.

    Google Scholar 

  99. Ingersoll J. (1987), Theory of Financial Decision Making, Rowman & Littlefield.

    Google Scholar 

  100. Jarrow R., Maksimovic V., and Ziemba W.T., eds. (1995), Finance, North-Holland, Amsterdam.

    Google Scholar 

  101. Jobson J.D. and Korkie B. (1981), “Putting Markowitz Theory to Work,” Jl. Portfolio Management, Summer, 70–74.

    Google Scholar 

  102. Jobson, J.D., Korkie B., and Ratti V. (1979), “Improved Estimation for Markowitz Portfolios Using James-Stein Type Estimators,” Proceedings Business Economics Statistics Section, American Statisticial Association, 279–284.

    Google Scholar 

  103. Jorion P. (1985), “International Portfolio Diversification with Estimation Error,” Jl Business, 58, 259–278.

    Google Scholar 

  104. Jorion P. (1986), “Bayes-Stein Estimation for Portfolio Analysis,” Jl Financial and Quantitative Analysis, 21, 279–292.

    Google Scholar 

  105. Jorion P. (1991), “Bayesian and CAPM Estimators of the Means: Implications for Portfolio Selection,” Jl Banking and Finance, 15, 717– 727.

    Google Scholar 

  106. Judge G.G. and Bock M.E. (1978), The Statistical Implications of Pre–Test and Stein-Rule Estimators in Econometrics, North-Holland.

    Google Scholar 

  107. Kallberg J.G. and Ziemba W.T. (1979), “On the Robustness of the Arrow-Pratt Risk Aversion Measure,” Economics Letters, 2, 21–26.

    Google Scholar 

  108. Kallberg J.G. and Ziemba W.T. (1983), “Comparison of Alternative Utility Functions in Portfolio Selection,” Management Science, 29, 1257–1276.

    Google Scholar 

  109. Kallberg J.G. and Ziemba W.T. (1984), “Mis-specification in Portfolio Selection Problems,” in G. Bamberg and K. Spremann (eds), Risk and Capital: Lecture Notes in Economics and Mathematical Systems, Springer-Verlag, New York.

    Google Scholar 

  110. Kraus A. and Litzenburger R.F. (1976), “Skew-ness Preference and the Valuation of Risk Assets,” Jl Finance, 31, 1085–1100.

    Google Scholar 

  111. Lancaster K. (1971), Consumer Demand: A New Approach, Columbia University Press.

    Google Scholar 

  112. Levy H. (1969), “A Utility Function Depending on the First Three Moments,” Jl. Finance, 24, 715–719.

    Google Scholar 

  113. Levy H. and Markowitz H (1979), “Approximating Exected Utility by a Function of Mean and Variance,” American Economic Review, 69, 308– 317.

    Google Scholar 

  114. Markowitz H.M. (1952), “Portfolio Selection,” Jl Finance, 7, No 1, March, 77–91.

    Google Scholar 

  115. Markowitz H.M. (1959), Portfolio Selection: Efficient Diversification of Investments, Yale University Press.

    Google Scholar 

  116. Markowitz H.M. (1983), “Nonnegative or Not Nonnegative: a Question about CAPMs,” JL Finance, 38, 283–295.

    Google Scholar 

  117. Markowitz H.M. (1987), Mean-Variance in Portfolio Choice and Capital Markets, Blackwell.

    Google Scholar 

  118. Merton R.C. (1972), “An Analytic Derivation of the Efficient Portfolio Frontier,” JL Financial and Quantitative Analysis, 7, 1851–1872.

    Google Scholar 

  119. Morris C. (1983), “Parametric Empirical Bayes Inference: Theory and Applications,” JL American Statistical Assoc., 78, 47–55.

    Google Scholar 

  120. Ohlson J. (1975), “Asymptotic Validity of Quadratic Utility as the Trading Interval Approaches Zero,” in Ziemba and Vickson, op. cit.

    Google Scholar 

  121. Roll R. (1977), “A Critique of the Asset Pricing Theory’s Tests,” JL Financial Economics, 4, 129– 176.

    Google Scholar 

  122. Samuelson P. (1970), “The Fundamental Approximation Theorem of Portfolio Analysis in Terms of Means Variances and Higher Moments,” Review Economic Studies, 37, 537–542.

    Google Scholar 

  123. Sharpe, W.F. (1963), “A Simplified Model for Portfolio Analysis,” Management Science, 9, 277– 293.

    Google Scholar 

  124. Sharpe, W.F. (1966), “Mutual Fund Performance,” JL Business, 39, 119–138.

    Google Scholar 

  125. Sharpe, W.F. (1970), Portfolio Theory and Capital Markets, McGraw–Hill, New York.

    Google Scholar 

  126. Sharpe, W.F. (1994), The Sharpe Ratio, Technical Report, Stanford University, California.

    Google Scholar 

  127. Szego, G.P. (1980), Portfolio Theory, with Application to Bank Asset Management, Academic Press, New York.

    Google Scholar 

  128. Tobin, J. (1958), “Liquidity Preference as Behaviour Towards Risk,” Review Economic Studies, 26, 65–86.

    Google Scholar 

  129. Tobin, J. (1965), “The Theory of Portfolio Selection,” in The Theory of Interest Rates, F. Brechling, ed.

    Google Scholar 

  130. Tsiang, S. (1972), “The Rationale of the Mean Standard Deviation Analysis, Skewness Preference and the Demand for Money,” American Economic Review, 62, 354–371.

    Google Scholar 

  131. Tsiang, S. (1973), “Risk, Return and Portfolio Analysis: Comment,” JL Political Economy, 81, 748–751.

    Google Scholar 

  132. Ziemba, W.T. (1994), “World Wide Security Markey Regularities,” European JL Operational Research, 74 (2).

    Google Scholar 

  133. Ziemba, W.T. and R.G. Vickson, eds. (1975), Stochastic Optimization Models in Finance, Academic Press, New York.

    Google Scholar 

  134. Assad, A.A., E.A. Wasil and G.L. Lilien (1992). Excellence in Management Science Practice: A Readings Book. Prentice Hall, New Jersey.

    Google Scholar 

  135. Boothroyd, H. (1978). Articulate Intervention. Taylor and Francis, London.

    Google Scholar 

  136. Kemeny, J.G. (1959). A Philosopher Looks at Science. Van Nostrand Reinhold, New York.

    Google Scholar 

  137. Miser, H.J. (1985). “The Practice of Systems Analysis.” In Miser and Quade ( 1985 ), 287–326.

    Google Scholar 

  138. Miser, H.J. (1993). “A Foundational Concept of Science Appropriate for Validation in Operational Research.” European Jl. Operational Research 66, 204–215.

    Google Scholar 

  139. Miser, H.J. (1994). “Systems Analysis as Dialogue: An Overview.” Technological Forecasting and Social Change 45, 299–306.

    Google Scholar 

  140. Miser, H.J. and E.S. Quade, eds. (1985). Handbook of Systems Analysis: Overview of Uses, Procedures, Applications, and Practice. Wiley, Chichester, United Kingdom.

    Google Scholar 

  141. Miser, H.J. and E.S. Quade, eds. (1988). Handbook of Systems Analysis: Craft Issues and Procedural Choices. Wiley, Chichester, United Kingdom.

    Google Scholar 

  142. Ravetz, J.R. (1971). Scientific Knowledge and its Social Problems. Oxford University Press, Oxford.

    Google Scholar 

  143. Schön, D.H. (1983). The Reflective Practitioner: How Professionals Think in Action. Basic Books, New York.

    Google Scholar 

  144. Tomlinson, R., E.S. Quade and H.J. Miser (1985). “Implementation.” In Miser and Quade ( 1985 ), 249–280.

    Google Scholar 

  145. Dyer, J.S. and R.K. Sarin (1979). “Measurable Multi-attribute Value Functions,” Operations Research, 27, 810–822.

    Google Scholar 

  146. Fishburn, P.C. (1970). Utility Theory for Decision Making. Wiley, New York.

    Google Scholar 

  147. Fishburn, P.C. (1988). Nonlinear Preference and Utility Theory. The Johns Hopkins University Press, Baltimore, Maryland.

    Google Scholar 

  148. Kahneman, D.H. and Tversky, A. (1979). “Prospect Theory: An Analysis of Decision under Risk,” Econometrica, 47, 263–290.

    Google Scholar 

  149. Keeney, R.L. and H. Raiffa (1976). Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Wiley, New York.

    Google Scholar 

  150. Krantz, D.H., R.D. Luce, P. Suppes, and A. Tversky (1971). Foundations of Measurement. Academic Press, San Diego.

    Google Scholar 

  151. Kreps, D.M. (1990). A Course in Microeconomics Theory. Princeton University Press, New Jersey.

    Google Scholar 

  152. Savage, L.J. (1954). The Foundations of Statistics. Wiley, New York.

    Google Scholar 

  153. von Neumann, J. and O. Morgenstern (1947). Theory of Games and Economic Behavior. Princeton University Press, New Jersey.

    Google Scholar 

  154. Ackoff, R.L. (1981). “The art and science of mess management,” Interfaces, 11, 20–26.

    Google Scholar 

  155. Checkland, P. and Scholes, J. (1990). Soft Systems Methodology in Practice. Wiley, Chichester, UK.

    Google Scholar 

  156. Eden, C., Jones, S. and Sims, D. (1983). Messing About in Problems. Pergamon, Oxford.

    Google Scholar 

  157. Flood, R.L. and Jackson, M.C. (1991). Creative Problem Solving: Total Systems Intervention. Wiley, Chichester, UK.

    Google Scholar 

  158. Friend, J.K. and Hickling, A. (1987). Planning Under Pressure. Pergamon, Oxford.

    Google Scholar 

  159. Greenberger, M., Crenson, M.A. and Crissey, B.L. (1976). Models in the Policy Process. Russell Sage, New York.

    Google Scholar 

  160. Nelson, R.R. (1974). “Intellectualizing about the moon–ghetto metaphor: a study of the current malaise of rational analysis of social problems,” Policy Science, 5, 375–414.

    Google Scholar 

  161. Ravetz, J.R. (1971). Scientific Knowledge and Its Social Problems. Oxford University Press, Oxford.

    Google Scholar 

  162. Rittel, H.W.J, and Webber, M.M. (1973). “Dilemmas in a general theory of planning,” Policy Science, 4, 155–169.

    Google Scholar 

  163. Rosenhead, J. (1981). “Operational research in urban planning,” Omega, 9, 345–364.

    Google Scholar 

  164. Rosenhead, J., ed. (1989). Rational Analysis for a Problematic World: Problem Structuring Methods for Complexity, Uncertainty and Conflict. Wiley, Chichester, UK.

    Google Scholar 

  165. Schon, D.A. (1987). Educating the Reflective Practitioner: Toward a New Design for Teaching and Learning in the Professions. Jossey-Bass, San Francisco.

    Google Scholar 

  166. Bitran, G.R. and S. Dasu (1992), “A Review of Open Queueing Network Models of Manufacturing Systems,” Queueing Systems, 12, 95–134.

    Google Scholar 

  167. Bitran, G.R. and D. Tirupati (1989), “Trade–off Curves, Targeting and Balancing in Manufacturing Networks,” Oper. Res., 37, 547–564.

    Google Scholar 

  168. Bitran, G.R. and D. Tirupati (1993), “Hierarchical Production Planning,” in Logistics of Production and Inventory, Handbooks in O.R. and M.S, Vol 4, Edited by S.C. Graves, A.H.G. Rinnooy Kan and P. Zipkin, Elsevier Science Publishers, Amsterdam.

    Google Scholar 

  169. Bitran G.R. and H.H. Yanasse (1982), “Computational Complexity of Capacitated Lot Sizing Problem,” Mgmt. Sci., 28, 1174–1186.

    Google Scholar 

  170. Burbridge, J.L. (1979), Group Technology in the Engineering Industry, Mechanical Engineering Publications, London.

    Google Scholar 

  171. Conway, R.W., W.L. Maxwell, and L.W. Miller (1967), Theory of Scheduling, Addison-Wesley, Reading, Massachusetts.

    Google Scholar 

  172. Conway, R.W., W. Maxwell, J.O. McClain, and L. J. Thomas (1988), “The Role of Work-in-process Inventory in Serial Production Lines,” Oper. Res., 36, 229–241.

    Google Scholar 

  173. Erlenkotter, D. (1978), “A Dual–based Procedure for Uncapacitated Facility Location,” Oper. Res., 26, 992–1005.

    Google Scholar 

  174. Federgruen, A. and P. Zipkin (1984), “Approximation of Dynamic Multi-location Production and Inventory Problems,” Mgmt. Sci., 30, 69–84.

    Google Scholar 

  175. French, S. (1985), Sequencing and Scheduling, An Introduction to the Mathematics of the Joh-Shop, John Wiley and Sons, New York.

    Google Scholar 

  176. Garey, M.R. and D.S. Johnson (1979), Computers and Intractability: A Guide to the Theory of N.P. Completeness, Freeman, San Francisco.

    Google Scholar 

  177. Graves, S.C. (1981), “A Review of Production Scheduling,” Oper. Res., 29, 646–675.

    Google Scholar 

  178. Graves, S.C. (1986), “A Tactical Planning Model for a Job Shop,” Oper. Res., 34, 522–533.

    Google Scholar 

  179. Hax, A.C. and D. Candea (1984), Production and Inventory Management, Prentice–Hall, New Jersey.

    Google Scholar 

  180. Lenstra J.K., A.H.G. Rinnooy Kan and P. Brucker (1977), “Complexity of Machine Scheduling Problems,” Ann. Discr. Math., 1, 343–362.

    Google Scholar 

  181. O’hEigeartaigh, M., J.K. Lenstra, and A.H.G. Rinnooy Kan (1985), Combinatorial Optimization — Annotated Bibliographies, John Wiley, New York.

    Google Scholar 

  182. Panwalker, S.S. and W. Iskander (1977), “A Survey of Scheduling Rules,” Oper. Res., 25, 45–61.

    Google Scholar 

  183. Porteus, E.L. (1985), “Investing in Reduced Setups in the EOQ Model,” Mgmt. Sci., 31, 998– 1010.

    Google Scholar 

  184. Roundy, R. (1986), “A 98% Effective Lot-sizing Rule for a Multi-product, Multi-stage Production/Inventory System,” Math. Oper. Res., 11, 699–727.

    Google Scholar 

  185. Silver, E.A. (1993), “Modeling in Support of Continuous Improvements Towards Achieving World Class Operations,” in Perspectives in Operations Management: Essays in Honor of Elwood S. Buffa, R. Sarin, ed., Kluwer, Boston.

    Google Scholar 

  186. Wein, L.M. (1990), “Optimal Control of a Two-station Brownian Network,” Math. Oper. Res., 15, 215–242.

    Google Scholar 

  187. Cicarelli, V.G. et al (1969). The Impact of Head Start. Westinghouse Learning Corporation and Ohio University, Athens, Ohio.

    Google Scholar 

  188. Cook, T.D. and D.T. Campbell (1979). Quasi-Experimentation: Design and Analysis Issues for Field Settings. Houghton Mifflin, Boston.

    Google Scholar 

  189. Kaplan, E.H. and E. O’Keefe (1993). “Let the Needles Do the Talking! Evaluating the New Haven Needle Exchange,” Interfaces 23, 7–26.

    Google Scholar 

  190. Kelling, G.L. et al (1974). The Kansas City Preventive Patrol Experiment: Summary Report. The Police Foundation, Washington, D.C.

    Google Scholar 

  191. Larson, R.C. (1975). “What happened to patrol operations in Kansas City? A review of the Kansas City Preventive Patrol Experiment,” Jl. Criminal Justice 3, 267–297.

    Google Scholar 

  192. Larson, R.C. and E.H. Kaplan (1981). “Decision– Oriented Approaches to Program Evaluation,” New Directions for Program Evaluation 10, 49–68.

    Google Scholar 

  193. Rossi, P.H. and H.E. Freeman (1993). Evaluation: A Systematic Approach, 5th ed. Sage Publications, Newbury Park, California.

    Google Scholar 

  194. Struyk, R.J. and M. Bendick, Jr. (1981). Housing Vouchers for the Poor: Lessons from a National Experiment. Urban Institute, Washington, D.C.

    Google Scholar 

  195. Adair, J . (1988). Effective Time Management. Pan Books, New York.

    Google Scholar 

  196. Belbin, RM . (1981). Management Teams: Why They Succeed or Fail. Heinemann, London.

    Google Scholar 

  197. Boddy, D. and D. Buchanan (1992). Take the Lead: Interpersonal Skills for Project Managers. Prentice Hall, Hemel Hempstead, UK.

    Google Scholar 

  198. Eilon, S . (1993). Time management. Omega 21, 255–259.

    Google Scholar 

  199. Handy, C . (1993). Understanding Organizations. Penguin, London.

    Google Scholar 

  200. Kennedy, G . (1982). Everything Is Negotiable. Business Books, London.

    Google Scholar 

  201. Woolcott, LA. and WR. Unwin (1983). Mastering Business Communication. Macmillan, Basingstoke, UK.

    Google Scholar 

  202. Drake, A.W., R.L. Keeney, and P.M. Morse, eds. (1972), Analysis of Public Systems, MIT Press, Cambridge, Massachusetts.

    Google Scholar 

  203. Findeisen, W. and E.S. Quade (1985), “The Methodology of Systems Analysis: An Introduction and Overview,” Chapter 4 in H.J. Miser and E.S. Quade, eds., Handbook of Systems Analysis: Overview of Uses, Procedures, Applications, and Practice, Elsevier, New York.

    Google Scholar 

  204. House, P.W. (1982), The Art of Public Policy Analysis, Sage Library of Social Research Vol. 135, Sage Publications, Beverly Hills, California.

    Google Scholar 

  205. Majone, G. (1985), “Systems Analysis: A Genetic Approach,” Chapter 2 in H.J. Miser and E.S. Quade, eds.. Handbook of Systems Analysis: Overview of Uses, Procedures, Applications, and Practice, Elsevier, New York.

    Google Scholar 

  206. Miser, H.J. (1980), “Operations Research and Systems Analysis,” Science, 209, 4 July, 139–146.

    Google Scholar 

  207. Miser, H.J. and E.S. Quade, eds. (1985), Handbook of Systems Analysis: Overview of Uses, Procedures, Applications, and Practice, Elsevier, New York.

    Google Scholar 

  208. Mood, A.M. (1983), Introduction to Policy Analysis, North-Holland, New York.

    Google Scholar 

  209. Quade, E.S. (1989), Analysis for Public Decisions, Elsevier, New York.

    Google Scholar 

  210. Schlesinger, J.R. (1967), On Relating Non-Technical Elements to System Studies, P–3545, The RAND Corporation, Santa Monica, California.

    Google Scholar 

  211. Simon, H.A. (1969), The Sciences of the Artificial, MIT Press, Cambridge, Massachusetts.

    Google Scholar 

  212. Walker, W.E. (1988), “Generating and Screening Alternatives,” Chapter 6 in Miser, H.J. and E.S. Quade, eds.. Handbook of Systems Analysis: Craft Issues and Procedural Choices, Elsevier, New York.

    Google Scholar 

  213. Walker, W.E., J.M. Chaiken, and E.J. Ignall, eds. (1979), Fire Department Deployment Analysis: A Public Policy Analysis Case Study, Elsevier North Holland, New York.

    Google Scholar 

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Gass, S.I., Harris, C.M. (1996). P. In: Gass, S.I., Harris, C.M. (eds) Encyclopedia of Operations Research and Management Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0459-3_16

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