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The Four-Level Model of Planning and Decision Making

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Combinatorial Optimization Problems in Planning and Decision Making

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 173))

Abstract

We provide an overview of known models, methods and software for scheduling and operational planning for objects with a network representation of technological processes and limited resources. The problem is an operational plan construction to produce a potential order portfolio which is the best in terms of criteria defined by the customer. We make the conclusion that an immediate solution of this problem (multi-stage network scheduling problem) is inefficient. The result of analysis is the four-level model of planning (including operational) and decision making, in which we formalize formal procedures both for obtaining an operational schedule and for its operative adjustment. The four-level model includes the combinatorial optimization problems presented in Chaps. 2–7 as well as the Decision Making Unit, a subsystem that performs decision making functions in case if various events appear during planning. In the Decision Making Unit we use our modified Analytic Hierarchy Process which is based on the research of empirical pairwise comparisons matrices with the help of combinatorial optimization models with weighted components of the additive functional.

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Notes

  1. 1.

    Logistics is the discipline dedicated to the organization of a rational process of goods and services promoting, from raw material suppliers to consumers. It also studies the products and services circulation sphere, stocks and provisions management, and the creation of goods circulation infrastructure. A wider definition of logistics treats it as a teaching about planning, management, and control of material, informational and financial resources movement in different systems.

  2. 2.

    Here and after, \( \overline{a,b} \) denotes the interval of integer numbers from a to b, that is, \( \overline{a,b} \) = \( {\mathbb{Z}} \cap \left[ {a,b} \right] \) = a, a + 1, …, b.

References

  1. Zgurovsky, M.Z., Pavlov, A.A.: Prinyatie Resheniy v Setevyh Sistemah s Ogranichennymi Resursami (Принятие решений в сетевых системах с ограниченными ресурсами; Decision Making in Network Systems with Limited Resources). Naukova dumka, Kyiv (2010) (in Russian)

    Google Scholar 

  2. Kovács, A.: Novel models and algorithms for integrated production planning and scheduling. Dissertation. Computer and Automation Research Institute, Budapest (2005)

    Google Scholar 

  3. Emelianov, S.V. (ed.).: Upravlenie gibkimi proizvodstvennymi sistemami. Modeli i algoritmy (Управление гибкими производственными системами. Модели и алгоритмы; Flexible Production Systems Management. Models and Algorithms). Mashinostroenie, Moscow; Technik, Berlin (1987) (in Russian)

    Google Scholar 

  4. Sokolicyn, S.A. (ed.): Mnogourovnevaya Sistema Operativnogo Upravleniya GPS v Mashinostroenii (Многоуровневая система оперативного управления ГПС в машиностроении; Multi-level System of Operational Control Over FPS in Engineering). Politehnika, Saint Petersburg (1991) (in Russian)

    Google Scholar 

  5. Petrov, V.A., Maslennikov, A.N., Osipov, L.A.: Planirovanie gibkih proizvodstvennyh sistem (Планирование гибких производственных систем; Flexible production systems planning). Mashinostroenie, Leningrad (1985) (in Russian)

    Google Scholar 

  6. Pavlov, A.A., Telenik, S.F.: Informacionnye tehnologii i algoritmizaciya v upravlenii (Информационные технологии и алгоритмизация в управлении; Information technologies and algorithmization in management). Tehnika, Kyiv (2002) (in Russian)

    Google Scholar 

  7. Ohno, T.: Toyota Production System: Beyond Large-Scale Production. Productivity Press, Cambridge (1988)

    Google Scholar 

  8. Bitran, G.R., Tirupati, D.: Hierarchical production planning. In: Graves, S.C., Rinnooy Kan, A.H.G., Zipkin, P.H. (eds.) Logistics of Production and Inventory. Handbooks in Operations Research and Management Science, vol. 4, pp. 523–568. Elsevier Science Publishers B. V., Amsterdam (1993). https://doi.org/10.1016/s0927-0507(05)80190-2

    Chapter  Google Scholar 

  9. Graves, S.C.: Manufacturing planning and control. In: Pardalos P., Resende, M. (eds.) Handbook of Applied Optimization, pp. 728–746. Oxford University Press, New York (2002)

    Google Scholar 

  10. Pervin, Y.A., Portugal, V.M., Semenov, A.I.: Planirovanie melkoseriynogo proizvodstva v ASUP (Планирование мелкосерийного производства в АСУП; Small-series production planning in automated control systems). Nauka, Moscow (1973) (in Russian)

    Google Scholar 

  11. Efetova, K.F., Podchasova, T.P., Portugal, V.M., Trinchuk, B.E.: Planirovanie proizvodstva v usloviyah ASU (Планирование производства в условиях АСУ; Production planning in an automated control system conditions). Tehnіka, Kyiv (1984) (in Russian)

    Google Scholar 

  12. Pavlov, A.A., Pavlova, L.A.: Osnovy metodologii proektirovaniya PDS-algoritmov dlya trudnoreshaemyh kombinatornyh zadach (Основы методологии проектирования ПДС-алгоритмов для труднорешаемых комбинаторных задач; Fundamentals of PDC-algorithms design methodology for intractable combinatorial problems). Problemy informatiki i upravleniya 4, 135–141 (1995) (in Russian)

    Google Scholar 

  13. Bitran, G.R., Haas, E.A., Hax, A.C.: Hierarchical production planning: a single stage system. Oper. Res. 29,717–743 (1981). https://doi.org/10.1287/opre.29.4.717

    Article  MathSciNet  Google Scholar 

  14. Bitran, G.R., Hax, A.C.: Disaggregation and resource allocation using convex knapsack problems with bounded variables. Manag. Sci. 27, 431–441 (1981). https://doi.org/10.1287/mnsc.27.4.431

    Article  MathSciNet  Google Scholar 

  15. Bitran, G.R., Hax, A.C.: On the design of hierarchical production planning systems. Decis. Sci. 8, 28–55 (1977). https://doi.org/10.1111/j.1540-5915.1977.tb01066.x

    Article  Google Scholar 

  16. Hax, A.C., Meal, H.C.: Hierarchical integration of production planning and scheduling. In: Geisler, M.A. (ed.) Logistics. Studies in Management Sciences, vol. 1, pp. 53–69. North Holland/American Elsevier, New York (1975)

    Google Scholar 

  17. Jain, A.S., Meeran, S.: Deterministic job-shop scheduling: past, present and future. Eur. J. Oper. Res. 113, 390–434 (1999). https://doi.org/10.1016/s0377-2217(98)00113-1

    Article  Google Scholar 

  18. Cook, S.A.: The complexity of theorem-proving procedures. In: Proceedings of the 3rd Annual ACM Symposium on Theory of Computing STOC ‘71, Shaker Heights, Ohio, 03–05 May 1971, pp. 151–158. ACM, New York (1971). https://doi.org/10.1145/800157.805047

  19. Karp, R.M.: Reducibility among combinatorial problems. In: Miller, R.E., Thather, J.W. (eds.) Complexity of Computer Computations, pp. 85–103. Plenum press, New York (1972). https://doi.org/10.1007/978-1-4684-2001-2_9

    Chapter  Google Scholar 

  20. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Co, San Francisco (1979). https://doi.org/10.1137/1024022

    Article  Google Scholar 

  21. Johnson S.M.: Optimal two- and three-stage production schedules with set-up times included. Naval Res. Logist. Quart 1(1), 61–68 (1954). https://doi.org/10.1002/nav.3800010110

    Article  Google Scholar 

  22. Akers Jr., S.B.: Letter to the editor—a graphical approach to production scheduling problems. Oper. Res. 4(2), 244–245 (1956). https://doi.org/10.1287/opre.4.2.244

    Article  Google Scholar 

  23. Hefetz, N., Adiri, I.: An efficient optimal algorithm for the two-machines unit-time job-shop schedule-length problem. Math. Oper. Res. 7(3), 354–360 (1982). https://doi.org/10.1287/moor.7.3.354

    Article  Google Scholar 

  24. Jackson, J.R.: An extension of Johnson’s result on job lot scheduling. Naval Res. Logistics Q. 3(3), 201–203 (1956). https://doi.org/10.1002/nav.3800030307

    Article  Google Scholar 

  25. Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-26580-3

    Book  Google Scholar 

  26. Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., Shmoys, D.B.: Sequencing and scheduling: Algorithms and complexity. In: Graves, S.C., Rinnooy Kan, A.H.G., Zipkin, P.H. (eds.) Logistics of Production and Inventory. Handbook in Operations Research and Management Science, vol. 4, pp. 445–522. North-Holland, Amsterdam (1993). https://doi.org/10.1016/s0927-0507(05)80189-6

    Chapter  Google Scholar 

  27. Fisher, H., Thompson, G.L. Probabilistic learning combinations of local job-shop scheduling rules. In: Muth, J.F., Thompson, G.L. (eds.) Industrial Scheduling, pp. 225–251. Prentice-Hall, Englewood Cliffs (1963)

    Google Scholar 

  28. Lawrence, S.: Supplement to resource constrained project scheduling: An experimental investigation of heuristic scheduling techniques. GSIA, Carnegie-Mellon University, Pittsburgh (1984)

    Google Scholar 

  29. Panwalkar, S.S., Iskander, W.: A survey of scheduling rules. Oper. Res. 25(1), 45–61 (1977). https://doi.org/10.1287/opre.25.1.45

    Article  MathSciNet  Google Scholar 

  30. Grabot, B., Geneste, L.: Dispatching rules in scheduling: A fuzzy approach. Int. J. Prod. Res. 32(4), 903–915 (1994). https://doi.org/10.1080/00207549408956978

    Article  Google Scholar 

  31. Werner, F., Winkler, A.: Insertion techniques for the heuristic solution of the job-shop problem. Discr. Appl. Math. 58(2), 191–211 (1995). https://doi.org/10.1016/0166-218x(93)e0127-k

    Article  MathSciNet  Google Scholar 

  32. Morton, T.E., Pentico, D.W.: Heuristic Scheduling Systems: With Applications to Production Systems and Project Management. Wiley, New York (1993)

    Google Scholar 

  33. Sabuncuoglu, I., Bayiz, M.: A beam search based algorithm for the job shop scheduling problem. Research Report IEOR-9705. Bilkent University, Bilkent (1997)

    Google Scholar 

  34. Fisher, M.L., Rinnooy Kan, A.H.G.: The design, analysis and implementation of heuristics. Manag. Sci. 34(3), 263–265 (1988). https://doi.org/10.1287/mnsc.34.3.263

    Article  MathSciNet  Google Scholar 

  35. Glover, F., Greenberg, H.J.: New approaches for heuristic search: A bilateral linkage with artificial intelligence. Eur. J. Oper. Res. 39(2), 119–130 (1989). https://doi.org/10.1016/0377-2217(89)90185-9

    Article  MathSciNet  Google Scholar 

  36. Rodammer, F.A., White, K.P., Jr.: A recent survey of production scheduling. IEEE Trans. Syst. Man Cybern. 18(6), 841–851 (1988). https://doi.org/10.1109/21.23085

    Article  MathSciNet  Google Scholar 

  37. Sergienko, I.V., Kapitonova, Y.V., Lebedeva, T.T.: Informatika v Ukraine: Stanovlenie, Razvitie, Problemy (Информатика в Украине: становление, развитие, проблемы. Informatics in Ukraine: Formation, Development, Problems). Naukova dumka, Kyiv (1999) (in Russian)

    Google Scholar 

  38. Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? J. Comput. Syst. Sci. 37(1), 79–100 (1988). https://doi.org/10.1016/0022-0000(88)90046-3

    Article  MathSciNet  Google Scholar 

  39. Yannakakis, M.: The analysis of local search problems and their heuristics. In: Choffrut, C., Lengauer, T. (eds.) STACS 90. Lecture Notes in Computer Science, vol. 415, pp. 298–311. Springer, Berlin (1990). https://doi.org/10.1007/3-540-52282-4_52

    Chapter  Google Scholar 

  40. Sergienko, I.V.: O primenenii metoda vektora spada dlya resheniya zadach optimizacii kombinatornogo tipa (О применении метода вектора спада для решения задач оптимизации комбинаторного типа; On the application of the recession vector method to solve optimization problems of combinatorial type). Upravlyayuschie sistemy i mashiny 2, 86–94 (1975) (in Russian)

    Google Scholar 

  41. Bykov, A.Y., Artamonova, A.Y.: Modifikaciya metoda vektora spada dlya optimizacionno-imitacionnogo podhoda k zadacham proektirovaniya sistem zaschity informacii (Модификация метода вектора спада для оптимизационно-имитационного подхода к задачам проектирования систем защиты информации; A modified recession vector method based on the optimization-simulation approach to design problems of information security systems). Nauka i Obrazovanie of Bauman MSTU 1, 158–175 (2015). https://doi.org/10.7463/0115.0754845 (in Russian)

  42. Shilo, V.P.: Metod globalnogo ravnovesnogo poiska (Метод глобального равновесного поиска; Global equilibrium search method). Cybern. Syst. Anal. 35(1), 74–81 (1999) (in Russian)

    Google Scholar 

  43. Adams, J., Balas, E., Zawack, D.: The shifting bottleneck procedure for job-shop scheduling. Manag. Sci. 34(3), 391–401 (1988). https://doi.org/10.1287/mnsc.34.3.391

    Article  MathSciNet  Google Scholar 

  44. Balas, E., Lancia, G., Serafini, P., et al.: Job-shop scheduling with deadlines. J. Comb. Optim. 1(4), 329–353 (1998). https://doi.org/10.1023/a:1009750409895

    Article  MathSciNet  Google Scholar 

  45. Balas, E., Vazacopoulos, A.: Guided local search with shifting bottleneck for job-shop scheduling. Manag. Sci. 44(2), 262–275 (1998). https://doi.org/10.1287/mnsc.44.2.262

    Article  Google Scholar 

  46. Caseau, Y., Laburthe, F.: Disjunctive scheduling with task intervals. LIENS Technical Report 95-25, Laboratoire d’Informatique de l’ Ecole Normale Superieure, Paris (1995)

    Google Scholar 

  47. Demirkol, E., Mehta, S., Uzsoy, R.: A computational study of shifting bottleneck procedures for shop scheduling problems. J. Heuristics 3(2), 111–137 (1997). https://doi.org/10.1023/a:1009627429878

    Article  Google Scholar 

  48. Yamada, T., Nakano, R.: Job-shop scheduling by simulated annealing combined with deterministic local search. In: Osman, I.H., Kelly, J.P. (eds.) Meta-Heuristics: Theory and Applications, pp. 237–248. Springer, Boston (1996). https://doi.org/10.1007/978-1-4613-1361-8_15

    Chapter  Google Scholar 

  49. Falkenauer, E., Bouffouix, S.: A genetic algorithm for job-shop. In: Proceedings of the IEEE International Conference on Robotics and Automation, Sacramento, 9–11 Apr 1991. https://doi.org/10.1109/robot.1991.131689

  50. Nakano, R., Yamada, T.: Conventional genetic algorithm for job-shop problems. In: Kenneth, M.K., Booker, L.B. (eds.) Proceedings of the 4th International Conference on Genetic Algorithms and their Applications, San Diego (1991)

    Google Scholar 

  51. Aarts, E.H.L., Van Laarhooven, P.J.M., Ulder, N.L.J.: Local search based algorithms for job-shop scheduling. Working Paper. University of Technology, Eindhoven (1991)

    Google Scholar 

  52. Dorndorf, U., Pesch, E.: Evolution based learning in a job-shop scheduling environment. Comput. Oper. Res. 22(1), 25–40 (1995). https://doi.org/10.1016/0305-0548(93)e0016-m

    Article  Google Scholar 

  53. Grefenstette, J.J.: Incorporating problem specific knowledge into genetic algorithms. In: Davis, L. (ed.) Genetic Algorithms and Simulated Annealing, pp. 42–60. Pitman, London (1987)

    Google Scholar 

  54. Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. C3P Report 826: Caltech Concurrent Computation Program, Caltech (1989)

    Google Scholar 

  55. Ulder, N.L.J., Aarts, E.H.L., Bandelt, H.-J., et al.: Genetic local search algorithms for the travelling salesman problem. Lect. Notes Comput. Sci. 496, 109–116 (1991). https://doi.org/10.1007/bfb0029740

    Chapter  Google Scholar 

  56. Fox, M.S.: Constraint-directed search: a case study of job shop scheduling. Dissertation, Carnegy Mellon University, Pittsburgh (1983)

    Google Scholar 

  57. Nuijten, W.P.M., Le Pape, C.: Constraint-based job-shop scheduling with ILOG SCHEDULER. J. Heuristics 3(4), 271–286 (1998). https://doi.org/10.1023/a:1009687210594

    Article  Google Scholar 

  58. Pesch, E., Tetzlaff, U.A.W.: Constraint propagation based scheduling of job shops. INFORMS J. Comput. 8(2), 144–157 (1996). https://doi.org/10.1287/ijoc.8.2.144

    Article  Google Scholar 

  59. Sadeh, N.: Look-ahead techniques for micro-opportunistic job shop scheduling. Dissertation, Carnegie Mellon University, Pittsburgh (1991)

    Google Scholar 

  60. Foo, S.Y., Takefuji, Y.: Integer linear programming neural networks for job-shop scheduling. In: IEEE International Conference on Neural Networks, San Diego, 24–27 July 1988. https://doi.org/10.1109/icnn.1988.23946

  61. Foo, S.Y., Takefuji, Y.: Stochastic neural networks for solving job-shop scheduling: part 1. Problem representation. In: IEEE International Conference on Neural Networks, San Diego, 24–27 July 1988. https://doi.org/10.1109/icnn.1988.23939

  62. Foo, S.Y., Takefuji, Y.: Stochastic neural networks for solving job-shop scheduling: part 2. Architecture and simulations. In: IEEE International Conference on Neural Networks, San Diego, 24–27 July 1988. https://doi.org/10.1109/icnn.1988.23940

  63. Sabuncuoglu, I., Gurgun, B.: A neural network model for scheduling problems. Eur. J. Oper. Res. 93(2), 288–299 (1996). https://doi.org/10.1016/0377-2217(96)00041-0

    Article  Google Scholar 

  64. Zhou, D.N., Cherkassky, V., Baldwin, T.R., et al.: Scaling neural networks for job-shop scheduling. In: Proceedings of International Joint Conference on Neural Networks (IJCNN’90), San Diego, 17–21 June 1990, pp. 889–894. https://doi.org/10.1109/ijcnn.1990.137947

  65. Zhou, D.N., Cherkassky, V., Baldwin, T.R., et al.: A neural network approach to job-shop scheduling. IEEE Trans. Neural Netw. 2(1), 175–179 (1991). https://doi.org/10.1109/72.80311

    Article  Google Scholar 

  66. Dorigo, M.: Optimization, learning and natural algorithms. Dissertation, Politecnico di Milano (1992)

    Google Scholar 

  67. Donati, A.V., Darley, V., Ramachandran, B.: An ant-bidding algorithm for multistage flowshop scheduling problem: optimization and phase transitions. In: Siarry P., Michalewicz Z. (eds.) Advances in Metaheuristics for Hard Optimization, pp.111–136. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-72960-0_6

  68. Prabhakar, B., Dektar, K.N., Gordon, D.M.: The regulation of ant colony foraging activity without spatial information. PLoS Comput. Biol. 8(8), e1002670 (2012). https://doi.org/10.1371/journal.pcbi.1002670

    Article  MathSciNet  Google Scholar 

  69. Brucker, P., Hurink, J., Werner, F.: Improving local search heuristics for some scheduling problems—I. Discr. Appl. Math. 65(1–3), 97–122 (1996). https://doi.org/10.1016/0166-218x(95)00030-u

    Article  MathSciNet  Google Scholar 

  70. Brucker, P., Hurink, J., Werner F.: Improving local search heuristics for some scheduling problems. Part II. Discr. Appl. Math. 72(1–2), 47–69 (1997). https://doi.org/10.1016/s0166-218x(96)00036-4

    Article  MathSciNet  Google Scholar 

  71. Lourenco, H.R.D.: A computational study of the job-shop and the flow-shop scheduling problems. Dissertation, Cornell University (1993)

    Google Scholar 

  72. Lourenco, H.R.D.: Job-shop scheduling: computational study of local search and large-step optimization methods. Eur. J. Oper. Res. 83(2), 347–364 (1995). https://doi.org/10.1016/0377-2217(95)00012-f

    Article  Google Scholar 

  73. Lourenco, H.R.D., Zwijnenburg, M.: Combining the large-step optimization with tabu-search: application to the job-shop scheduling problem. In: Osman, I.H., Kelly, J.P. (eds.) Meta-Heuristics: Theory and Applications, pp. 219–236. Springer, Boston (1996). https://doi.org/10.1007/978-1-4613-1361-8_14

    Chapter  Google Scholar 

  74. Martin, O., Otto, S.W., Felten, E.W.: Large-step Markov chains for traveling salesman problem. Complex Syst. 5(3), 299–326 (1989)

    Google Scholar 

  75. Martin, O., Otto, S.W., Felten, E.W.: Large-step Markov chains for TSP incorporating local search heuristics. Oper. Res. Lett. 11(4), 219–224 (1992). https://doi.org/10.1016/0167-6377(92)90028-2

    Article  MathSciNet  Google Scholar 

  76. Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13(5), 533–549 (1986). https://doi.org/10.1016/0305-0548(86)90048-1

    Article  MathSciNet  Google Scholar 

  77. Glover, F.: Heuristics for integer programming using surrogate constraints. Decis. Sci. 8(1), 156–166 (1977). https://doi.org/10.1111/j.1540-5915.1977.tb01074.x

    Article  Google Scholar 

  78. Glover, F.: Tabu search—part I. ORSA J. Comput. 1(3), 190–206 (1989). https://doi.org/10.1287/ijoc.1.3.190

    Article  Google Scholar 

  79. Glover, F.: Tabu search—part II. ORSA J. Comput. 2(1), 4–32 (1990). https://doi.org/10.1287/ijoc.2.1.4

    Article  Google Scholar 

  80. Glover, F., Laguna, M.: Tabu Search. Springer, Boston (1997). https://doi.org/10.1007/978-1-4615-6089-0

    Book  Google Scholar 

  81. Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job-shop problem. Manag. Sci. 42(6), 797–813 (1996). https://doi.org/10.1287/mnsc.42.6.797

    Article  Google Scholar 

  82. Taillard, E.: Parallel taboo search technique for the job-shop scheduling problem. Internal Research Report ORWP89/11. Ecole Polytechnique Federale de Lausanne, Lausanne (1989)

    Google Scholar 

  83. Edelkamp, S., Schrödl, S.: Heuristic Search: Theory and Applications. Morgan Kaufmann Publishers, Waltham, MA (2012). https://doi.org/10.1016/c2009-0-16511-x

  84. Resende, M.G.C.: A GRASP for job shop scheduling. In: INFORMS National Meeting, pp. 23–31, San Diego, CA, 4–7 May 1997

    Google Scholar 

  85. Matsuo, H., Suh, C.J., Sullivan, R.S.: A controlled search simulated annealing method for the general job-shop scheduling problem. Working Paper. University of Texas, Austin (1988)

    Google Scholar 

  86. Van Laarhoven, P.J.M., Aarts, E.H.L., Lenstra, J.K.: Job-shop scheduling by simulated annealing. Report OS-R8809. Centrum voor Wiskunde en Informatica, Amsterdam (1988)

    Google Scholar 

  87. Dechter, R.: Constraint Processing. Morgan Kaufmann, San Francisco (2003)

    Google Scholar 

  88. Michel, L., Hentenryck, P.V.: Activity-based search for black-box constraint programming solvers. In: Beldiceanu, N., Jussien, N., Pinson, É. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2012. Lecture Notes in Computer Science, vol. 7298, pp. 228–243. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-29828-8_15

    Chapter  Google Scholar 

  89. Schulte, C., Tack, G., Lagerkvist, M.Z.: Modeling and programming with Gecode. http://www.gecode.org/doc-latest/MPG.pdf (2018). Accessed 02 Apr 2018

  90. Flajolet, P., Sedgewick, R.: Analytic Combinatorics. Cambridge University Press, Cambridge (2009). https://doi.org/10.1017/cbo9780511801655

  91. Zgurovsky, M.Z., Pavlov, O.A., Misura, E.B. PDS-algoritmy i trudnoreshaemye zadachi kombinatornoi optimizacii (ПДС-алгоритмы и труднорешаемые задачи комбинаторной оптимизации; PDC-algorithms and intractable combinatorial optimization problems). Syst. Res. Inform. Technol. 2009(4), 14–31 (2009) (in Russian)

    Google Scholar 

  92. Kolisch, R., Hartmann, S.: Heuristic algorithms for the resource-constrained project scheduling problem: Classification and computational analysis. In: Węglarz, J. (ed.) Project Scheduling. International Series in Operations Research & Management Science, vol. 14, pp. 147–178. Springer, New York (1999). https://doi.org/10.1007/978-1-4615-5533-9_7

    Chapter  Google Scholar 

  93. Baptiste, P., Le Pape, C., Nuijten, W.: Constraint-based Scheduling: Applying Constraint Programming to Scheduling Problems Series. International Series in Operations Research & Management Science, vol. 39. Springer, Boston (2001). https://doi.org/10.1007/978-1-4615-1479-4

    Book  Google Scholar 

  94. Fox, M.S., Smith, S.F, Allen, B.P., et al.: ISIS: A constraint-directed reasoning approach to job-shop scheduling. In: Proceedings of the IEEE Trends and Applications Conference, pp. 76–81. Washington, May 1983

    Google Scholar 

  95. Wallace, M.G.: Practical applications of constraint programming. Constraints 1(1–2), 139–168 (1996). https://doi.org/10.1007/bf00143881

    Article  MathSciNet  Google Scholar 

  96. Vollmann, T.E., Berry, W.L., Whybark, D.C.: Manufacturing Planning and Control Systems. Irwin, McGraw-Hill (1997)

    Google Scholar 

  97. Vernikov, G.: Osnovy sistem klassa MRP-MRPII (Основы систем класса MRP-MRPII; Fundamentals of MRP-MRP II class systems). https://www.cfin.ru/vernikov/mrp/mrpmine.shtml (1999). Accessed 02 Apr 2018 (in Russian)

  98. MRP i MRP II (MRP и MRP II; MRP & MRP II). Planeta KIS. http://bigc.ru/publications/other/logistics/mpr_and_mpr2.php (1999). Accessed 02 Apr 2018 (in Russian)

  99. Schonberger, R.J.: Japanese Manufacturing Techniques: Nine Hidden Lessons in Simplicity. The Free Press, New York (1982)

    Google Scholar 

  100. Chaya, V.T., Chupahina, N.I.: Konceptualnye osnovy razvitiya upravlencheskogo ucheta (Концептуальные основы развития управленческого учета; Conceptual bases of management accounting development). Ekonomicheskii analiz: teoriya i praktika 2009(3), 25–31 (2009) (in Russian)

    Google Scholar 

  101. Hans, E.W.: Resource loading by branch-and-price techniques. Dissertation, Twente University, Enschede (2001)

    Google Scholar 

  102. Kis, T.: A branch-and-cut algorithm for scheduling of projects with variable-intensity activities. Math. Progr. 103(3), 515–539 (2005) https://doi.org/10.1007/s10107-004-0551-6

    Article  MathSciNet  Google Scholar 

  103. Leachman, R.C., Dincerler, A., Kim, S.: Resource constrained scheduling of projects with variable intensity activities. IIE Trans. 22(1), 31–40 (1990). https://doi.org/10.1080/07408179008964155

    Article  Google Scholar 

  104. Vasil’ev, K.A., Gorohov, M.M.: Sistemy klassov MRP i MRPII (Системы классов MRP и MRPII; Systems of MRP & MRPII class). In: Proceedings of the 10th International Scientific-Practical Conference “Problemy Effektivnogo Ispolzovaniya Nauchnogo Potenciala Obschestva”, Chelyabinsk, 10 December 2017, vol. 4, pp. 14–18. Aeterna, Ufa (2017) (in Russian)

    Google Scholar 

  105. Turdyshov, D.H.: Osobennosti postroeniya informacionnyh sistem upravleniya (Особенности построения информационных систем управления; Specifics of information management systems building). Sovremennye problemy nauki i obrazovaniya 2013(1). https://www.science-education.ru/ru/article/view?id=8187 (2013). Accessed 02 Apr 2018 (in Russian)

  106. Gastek, D.M.: Managing MRP-II. Manag. Autom. 1, 39–41 (1986)

    Google Scholar 

  107. Fedyaev, A.A., Fedyaeva, E.M.: K voprosu o razvitii sovremennyh ERP-sistem (К вопросу о развитии современных ERP-систем; On the development of modern ERP systems). Molodoi uchenyi 17, 26–30. https://moluch.ru/archive/97/21837/ (2015). Accessed 02 Apr 2018 (in Russian)

  108. Sheina, Y.V.: Informacionnaya komponenta reinjiniringa logisticheskih processov krupnoformatnyh torgovyh organizacii (Информационная компонента реинжиниринга логистических процессов крупноформатных торговых организаций; Information component of logistics processes reengineering for large-format trading organizations). Ekonominfo 8, 66–69 (2007) (in Russian)

    Google Scholar 

  109. Krylovich, A.: ERP-sistemy pozvolyayut planirovat v rynochnyh usloviyah (ERP-системы позволяют планировать в рыночных условиях; ERP-systems allow you to plan in market conditions). BOSS 5. http://bigc.ru/publications/other/logistics/erp_systems_planir_v_rinochn_uclov.php (2000). Accessed 02 Apr 2018 (in Russian)

  110. Ultimate humanless enterprises CIS: Ultima Solutions. https://www.ultimatebusinessware.ru/solutions/ (2018). Accessed 02 Apr 2018 (in Russian)

  111. SAP Website CIS. http://www.sap.com/cis (2018) Accessed 02 Apr 2018 (in Russian)

  112. 2011 Guide to ERP Systems and Vendors. An Independent Research Report. Panorama Consulting. https://www.webcitation.org/65BhgKyzp?url=http://panorama-consulting.com/Documents/2011-Guide-to-ERP-Systems-and-Vendors.pdf (2011). Accessed 04.04.2018

  113. ERP: Wikipedia Russian. https://ru.wikipedia.org/wiki/ERP (2017). Accessed 02 Apr 2018 (in Russian)

  114. Smirnov, N.: ERP bez kompleksov (ERP без комплексов; ERP without complexes). Computerworld Russia 17. http://www.osp.ru/cw/2013/17/13036445/ (2013). Accessed 02 Apr 2018 (in Russian)

  115. Mescheryakov, V.: Rossiiskii rynok ERP: 1S rastet bystree vseh (Российский рынок ERP: 1С растет быстрее всех; Russian ERP market: 1C grows faster than all). CNews. RosBusinessConsulting. http://www.cnews.ru/news/top/index.shtml?2011/09/19/455890 (2011). Accessed 02 Apr 2018 (in Russian)

  116. Information Technologies Website. IT-Enterprise. http://www.it.ua (2018) Accessed 01 Aug 2012 (in Russian)

  117. ERP-rynok glazami veterana (ERP-рынок глазами ветерана; ERP-market through the eyes of a veteran). PC Week Ukrainian Edition. SK-Press. http://www.pcweek.ua/themes/detail.php?ID=135600 (2011). Accessed 02 Apr 2018 (in Russian)

  118. Jelvicky, D.: Razrushenie stereotipov (Разрушение стереотипов; Stereotypes destruction). Computerworld Russia 17. http://www.osp.ru/cw/2013/17/13036460/ (2013). Accessed 02 Apr 2018 (in Russian)

  119. Lastovirya, V.N., Gladkov, E.A., Konovalov, A.V.: Optimizaciya v Avtomatizirovannom Proektirovanii Svarochnogo Proizvodstva (Оптимизация в Автоматизированном Проектировании Сварочного Производства; Optimization in the Automated Design of Welding Production). MSIU, Moscow (2008) (in Russian)

    Google Scholar 

  120. Zagidullin, R.R.: Planirovanie Mashinostroitel’nogo Proizvodstva (Планирование машиностроительного производства; Engineering Production Planning). TNT, Staryi Oskol (2015) (in Russian)

    Google Scholar 

  121. Zagidullin, R.R.: Upravlenie Mashinostroitel’nym Proizvodstvom s Pomosch’yu Sistem MES, APS, ERP (Управление машиностроительным производством с помощью систем MES, APS, ERP; Engineering Production Management with the Help of MES, APS, ERP Systems). TNT, Staryi Oskol (2011) (in Russian)

    Google Scholar 

  122. Advanced Planning & Scheduling: Wikipedia Russian. https://ru.wikipedia.org/wiki/Advanced_Planning_&_Scheduling (2018). Accessed 02 Apr 2018 (in Russian)

  123. Aldzhanov, V.: IT-arhitektura. Prakticheskoe Rukovodstvo ot A do Ya (ИТ-архитектура. Практическое руководство от А до Я; IT Architecture. A Practical Guide from A to Z). Litres, Moscow (2018)

    Google Scholar 

  124. Werner, L., Yong, M.S.: A survey of commercial production scheduling software. In: Lean Aerospace Initiative. Plenary Workshop. Massachusetts Institute of Technology, Cambridge (1999)

    Google Scholar 

  125. Yong, M.S.: Simulation of real-time scheduling policies in multi-product, make-to-order semiconductor fabrication facilities. Master thesis, Massachusetts Institute of Technology, Cambridge (2001)

    Google Scholar 

  126. Saver, J.: Knowledge-based design of scheduling systems. Intell. Autom. Soft Comput. 7(1), 55–62 (2001). https://doi.org/10.1080/10798587.2001.10642804

    Article  Google Scholar 

  127. Dorn, J., Froeschl, K.A.: Scheduling of Production Processes. Ellis Horwood, Saddle River (1993)

    Google Scholar 

  128. Sauer, J., Bruns, R.: Knowledge-based scheduling systems in industry and medicine. IЕЕЕ Ехреrt 12(1), 24–31 (1997). https://doi.org/10.1109/64.577410

    Article  Google Scholar 

  129. Smith, S.F.: Knowledge-based production management: approaches, results and prospects. Prod. Plan. Control 3(4), 350–380 (1992). https://doi.org/10.1080/09537289208919407

    Article  Google Scholar 

  130. Zweben, M., Fox, M.: Intelligent Scheduling. Morgan Kaufmann Publishers, San Francisco (1994)

    Google Scholar 

  131. Kempf, K.G., Le Pape, C, Smith, S.F., Fox, B.R.: Issues in the design of AI-based schedulers: a workshop report. AI Magazine 11(5), 37–46 (1991)

    Google Scholar 

  132. Oleynik, P.P.: Korporativnye Informacionnye Sistemy (Корпоративные Информационные Системы; Corporate Information Systems). Piter Publishing House, Saint Petersburg (2012)

    Google Scholar 

  133. Pavlov, A.A. (ed.): Konstruktivnye Polinomialnye Algoritmy Resheniya Individualnyh Zadach iz Klassa NP (Конструктивные полиномиальные алгоритмы решения индивидуальных задач из класса NP; Constructive Polynomial Algorithms for Solving Individual Problems from the Class NP). Tehnika, Kyiv (1993) (in Russian)

    Google Scholar 

  134. Zgurovsky, M.Z., Pavlov, A.A.: Ierarhicheskoe planirovanie v sistemah, imeyuschih setevoe predstavlenie tehnologicheskih processov i ogranichennye resursy, kak zadacha prinyatiya resheniy (Иерархическое планирование в системах, имеющих сетевое представление технологических процессов и ограниченные ресурсы, как задача принятия решений; Hierarchical planning in systems that have a network representation of technological processes and limited resources, as a decision making problem). Syst. Res. Inform. Technol. 2009(3), 70–75 (2009) (In Russian)

    Google Scholar 

  135. Saaty, T.L.: The Analytic Hierarchy Process. McGraw Hill, New York (1980)

    Google Scholar 

  136. Pavlov, A.A., Kalashnik, V.V.: Rekomendacii po vyboru zony provedeniya aktivnogo eksperimenta dlya odnomernogo polinomialnogo regressionnogo analiza (Рекомендации по выбору зоны проведения активного эксперимента для одномерного полиномиального регрессионного анализа; Recommendations for choosing an active experiment area for univariate polynomial regression analysis). Visnyk NTUU KPI Inform. Oper. Comput. Sci. 60, 41–45 (2014) (in Russian)

    Google Scholar 

  137. Pavlov, A.A., Kalashnik, V.V., Kovalenko, D.A.: Postroenie mnogomernoi polinomialnoi regressii. Regressiya s povtoryayuschimisya argumentami vo vhodnyh dannyh (Построение многомерной полиномиальной регрессии. Регрессия с повторяющимися аргументами во входных данных; Multivariate polynomial regression construction. Regression with repeated arguments in the input data). Visnyk NTUU KPI Inform. Oper. Comput. Sci. 62, 57–61 (2015) (in Russian)

    Google Scholar 

  138. Pavlov, A.A., Ivanova, A.A.: Models and methods of project selection in terms of non-formalized global target. In: Management of the Distribute Projects and Programmes Resources: Monograph, pp. 122–150. Torubara V.V., Nikolayev (2015)

    Google Scholar 

  139. Saaty, T.L. Kearns, K.: Analytical Planning: The Organization of Systems. Pergamon Press, Oxford (1985). https://doi.org/10.1016/c2013-0-03782-6

  140. Andreychikov, A.V., Andreychikova, O.N.: Analiz, Sintez, Planirovanie Resheniy v Ekonomike (Анализ, Синтез, Планирование Решений в Экономике; Analysis, Synthesis, Planning of the Economy Decisions). Finansy i Statistika, Moscow (2000)

    Google Scholar 

  141. Saaty, T.L.: Decision Making with Dependence and Feedback: The Analytic Network Process. RWS Publications, Pittsburgh (1996)

    Google Scholar 

  142. Hudson, D.J.: Lectures on Elementary Statistics and Probability, vol. 1. CERN Reports 63(29). CERN, Geneva (1963). https://doi.org/10.5170/cern-1963-029

  143. Hudson, D.J.: Statistics Lectures, Vol. 2: Maximum Likelihood and Least Squares Theory. CERN Reports 64(18). CERN, Geneva (1964). https://doi.org/10.5170/cern-1964-018

  144. Draper, N.R., Smith H.: Applied Regression Analysis, 3rd edn. Wiley, New York (1998). https://doi.org/10.1002/9781118625590

    MATH  Google Scholar 

  145. Flom, P.L., Cassell, D.L.: Stopping stepwise: why stepwise and similar selection methods are bad, and what you should use. In: NESUG 2007 Proceedings. NorthEast SAS Users Group (2007)

    Google Scholar 

  146. Harrell Jr., F.E.: Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Springer International Publishing Switzerland (2015). https://doi.org/10.1007/978-3-319-19425-7

  147. Foster, D.P., George, E.I.: The risk inflation criterion for multiple regression. Ann. Stat. 22(4), 1947–1975 (1994). https://doi.org/10.1214/aos/1176325766

    Article  MathSciNet  Google Scholar 

  148. Jonathan, M., Goldberg, M.: Multiple regression analysis and mass assessment: a review of the issues. Appraisal J. 56(1), 89–109 (1988)

    Google Scholar 

  149. Ivahnenko, A.G.: Induktivnyi Metod Samoorganizacii Modelei Slojnyh Sistem (Индуктивный метод самоорганизации моделей сложных систем; Inductive Method of Self-Organization of Complex System Models). Naukova dumka, Kyiv (1982)

    Google Scholar 

  150. Ivahnenko, A.G., Stepashko, V.S.: Pomehoustoichivost Modelirovaniya (Помехоустойчивость моделирования; Simulation’s Immunity to Interference). Naukova dumka, Kyiv (1985)

    Google Scholar 

  151. Meyer, C.D.: Matrix Analysis and Applied Linear Algebra Book and Solutions Manual. Society for Industrial and Applied Mathematics, Philadelphia (2000). https://doi.org/10.1137/1.9780898719512

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  1. Kyiv Polytechnic Institute, National Technical University of Ukraine, Kyiv, Ukraine

    Michael Z. Zgurovsky

  2. Faculty of Informatics and Computer Science, National Technical University of Ukraine, Kyiv, Ukraine

    Alexander A. Pavlov

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Zgurovsky, M.Z., Pavlov, A.A. (2019). The Four-Level Model of Planning and Decision Making. In: Combinatorial Optimization Problems in Planning and Decision Making. Studies in Systems, Decision and Control, vol 173. Springer, Cham. https://doi.org/10.1007/978-3-319-98977-8_8

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