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Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 108))

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Abstract

As it is known from Sect. 8.7, the general methodology for solving any DCSP assumes firstly, determining feasible sequences of tasks on machines, and secondly, determining optimal allocation of continuous resource to these sequences.

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References

  1. Józefowska, J., Węglarz, J.: On a methodology for discrete-continuous scheduling. Eur. J. Oper. Res. 107(2), 338–353 (1998)

    Article  MATH  Google Scholar 

  2. Gorczyca, M., Janiak, A.: New approach to resource allocation in the problems of scheduling of tasks described with concave dynamic models. In: Kaszyński, R. (ed.) Proceedings of the 13-th IEEE/IFAC International Conference on Methods and Models in Automation and Robotics, Szczecin, pp. 1189–1192 (2007)

    Google Scholar 

  3. Gorczyca, M., Janiak, A.: Methods for the optimal resource allocation in the problems of scheduling of tasks described with concave dynamic model. In: 14th IFAC Conference on Methods and Models in Automation and Robotics, IFAC Proceedings, vol. 42(13), pp. 250–255 (2009)

    Google Scholar 

  4. Gorczyca, M.: Resource allocation and task scheduling algorithms for the selected problems with dynamic task models and parallel processors. Ph.D. thesis (in Polish), Wrocław University of Technology (2008)

    Google Scholar 

  5. Gorczyca, M., Janiak, A.: Resource level minimization in the discrete–continuous scheduling. Eur. J. Oper. Res. 203, 32–41 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  6. Schittowski, K.: NLQPL: a FORTRAN-subroutine solving constrained nonlinear programming problems. Ann. Oper. Res. 5, 485–500 (1985)

    Article  Google Scholar 

  7. Gorczyca, M., Janiak, A.: Dominance properties in the discrete-continuous scheduling problems, International Conference on System Science, Wrocław, pp. 96–106 (2007)

    Google Scholar 

  8. Gorczyca, M., Janiak, A., Janiak, W.: The discrete part of the discrete-continuous scheduling problems—new properties. In: 14th IFAC Conference on Methods and Models in Automation and Robotics, IFAC Proceedigns, vol. 42(13), pp 244–249 (2009)

    Google Scholar 

  9. Józefowska, J., Mika, M., Różycki, R., Waligóra, G., Węglarz, J.: Solving the discrete-continuous project scheduling problem via its discretization. Math. Methods Oper. Res. 52(3), 489–499 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  10. Różycki, R.: Zastosowanie algorytmu genetycznego do rozwiązywania dyskretno-ciągłych problemów szeregowania. Ph.D. dissertation, Poznań University of Technology, Poland (2000)

    Google Scholar 

  11. Józefowska, J., Mika, M., Różycki, R., Waligóra, G., Węglarz, J.: A heuristic approach to allocating the continuous resource in discrete–continuous scheduling problems to minimize the makespan. J. Sched. 5(6), 487–499 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  12. Józefowska, J., Waligóra, G.: Heuristic procedures for allocating the continuous resource in discrete-continuous scheduling problems. Found. Comput. Decis. Sci. 29(4), 315–328 (2004)

    MATH  MathSciNet  Google Scholar 

  13. Waligóra, G.: Tabu search for discrete-continuous scheduling problems with heuristic continuous resource allocation. Eur. J. Oper. Res. 193(3), 849–856 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  14. Waligóra, G.: Heuristic approaches to discrete-continuous project scheduling problems to minimize the makespan. Comput. Optim. Appl. 48(2), 399–421 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  15. Jędrzejowicz, P., Skakovski, A.: An Island-Based Evolution Algorithm for Discrete-Continuous Scheduling with Continuous Resource Discretisation, Proceedings of the 2nd IEEE International Conference on Computational Cybernetics ICCC 2004, Aug 30–Sept 1, 2004, Vienna University of Technology, Austria (2004)

    Google Scholar 

  16. Jędrzejowicz, P., Skakovski, A.: A population learning algorithm for discrete-continuous scheduling with continuous resource discretisation. In: Chen, Y., Abraham, A., Jinan, B. (eds.) Proceedings of 6th International Conference on Intelligent Systems Design and Applications (ISDA 2006), vol. 2, spec. sess.: Nature Imitation Methods Theory and practice (NIM’06), Peoples Republic of China, pp. 1153–1158 (2006)

    Google Scholar 

  17. Jędrzejowicz, P., Skakovski, A.: A cross-entropy based population learning algorithm for discrete-continuous scheduling with continuous resource discretisation. Neurocomputing 73(4–6), Special Issue: SI, 655–660 (2010)

    Google Scholar 

  18. Jędrzejowicz, P., Skakovski, A.: Structure versus efficiency of the cross-entropy based population learning algorithm for discrete-continuous scheduling with continuous resource discretisation. In: Czarnowski, I., Jędrzejowicz, P., Kacprzyk, J. (eds.) Studies in Computational Intelligence. Agent-Based Optimization, vol. 456, pp. 77–102 (2013)

    Google Scholar 

  19. Jędrzejowicz, P., Skakovski, A.: Population learning with differential evolution for the discrete-continuous scheduling with continuous resource discretisation. In: IEEE International Conference on Cybernetics (CYBCONF) Lausanne, Switzerland, 13–15 June, pp. 92–97 (2013)

    Google Scholar 

  20. Jędrzejowicz, P., Skakovski, A.: Island-based differential evolution algorithm for the discrete-continuous scheduling with continuous resource discretisation. Procedia Comput. Sci. 35, 111–117 (2014)

    Article  Google Scholar 

  21. Jędrzejowicz, P., Skakovski, A.: Improving performance of the differential evolution algorithm using cyclic decloning and changeable population size. In: Nguyen, N.T., Czarnowski, I., Hwang, D. (eds.), Journal of Universal Computer Science (J.UCS), Special Issue—Computational Intelligence Tools for Processing Collective Data (CITPCD 15), vol. 22 (6), pp. 874–893 (2016)

    Google Scholar 

  22. Jędrzejowicz, P., Skakovski, A.: Properties of the Island-Based and single population differential evolution algorithms applied to discrete-continuous scheduling. In: Czarnowski, I. et al. (eds.) Intelligent Decision Technologies 2016, Proceedings of the 8th KES International Conference on Intelligent Decision Technologies (KES-IDT 2016)—Part I, Smart Innovation, Systems and Technologies, vol. 56, pp. 349–359 (2016)

    Google Scholar 

  23. Słowiński, R.: Algorytmy sterowania rozdziałem zasobów różnych kategorii w kompleksie operacji. Wydawnictwo Politechniki Poznańskiej, seria Rozprawy Nr 114, Poznań (1980)

    Google Scholar 

  24. Drexl, A., Gruenewald, J.: Nonpreemptive multi-mode resource-constrained project scheduling. IIE Trans. 25(5), 74–81 (1993)

    Article  Google Scholar 

  25. Hartmann, S.: Project scheduling with multiple modes: a genetic algorithm (in English). Manuskripte aus den Instituten für Betriebswirtschaftslehre Nr. 435, the University of Kiel, Germany (1997)

    Google Scholar 

  26. Hartmann, S., Briskorn, D.: A survey of variants and extensions of the resource-constrained project scheduling problem. Eur. J. Oper. Res. 207(1), 1–14 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  27. Bartusch, M., Rolf, H.M., Radermacher, F.J.: Scheduling project networks with resource constraints and time windows. Ann. Oper. Res. 16, 201–240 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  28. Józefowska, J., Mika, M., Węglarz, J.: A simulated annealing algorithm for some class of discrete-continuous scheduling problems. Computational Methods in Science and Technology, vol. 2(1), pp. 73–85. Scientific Publishers OWN, Poznan (1996)

    Google Scholar 

  29. Blazewicz, J., Kubiak, W., Szwarcfiter, J.: Scheduling independent fixed-type tasks. In: Słowiński, R., Węglarz, J. (eds.) Advances in Project Scheduling. Elsevier, Amsterdam (1989)

    Google Scholar 

  30. Józefowska, J., Mika, M., Różycki, R., Waligóra, G., Węglarz, J.: Discrete-continuous scheduling to minimize the makespan for power processing rates of jobs. Discret. Appl. Math. 94, 263–285 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  31. Lawrence, C., Zhou, J.L., Tits, A.L.: Users guide for CFSQP Version 2.3. Available by e-mail: andre@ eng.umd.edu (1995)

    Google Scholar 

  32. Lawrence, C., Zhou, J.L., Tits, A.L.: Users guide for CFSQP Version 2.5. Available by email: andre@eng.umd.edu (1997)

    Google Scholar 

  33. Józefowska, J., Różycki, R., Waligóra, G., Węglarz, J.: Local search metaheuristics for discrete-continuous scheduling problems. Eur. J Oper. Res. 107(2), 354–370 (1998)

    Article  MATH  Google Scholar 

  34. Józefowska, J., Mika, M., Różycki, R., Waligóra, G., Węglarz, J.: Discrete-Continuous scheduling to minimize the mean flow time—computational experiments. Comput. Methods Sci Technol. 3(1), 25–37 (1997)

    Article  MATH  Google Scholar 

  35. Józefowska, J., Mika, M., Różycki, R., Waligóra, G., Węglarz, J.: Discrete-continuous scheduling to minimize maximum lateness. In: Proceedings of the Fourth International Symposium on Methods and Models in Automation and Robotics MMAR’97, Międzyzdroje 26–29 Aug 1997, pp. 947–952 (1997)

    Google Scholar 

  36. Józefowska, J., Mika, M., Różycki, R., Waligóra, G., Węglarz, J.: Solving discrete-continuous scheduling problems by Tabu Search. In: 4th Metaheuristics International Conference MIC’2001, Porto, Portugal, 16–20 July 2001, pp. 667–671 (2001)

    Google Scholar 

  37. Józefowska, J., Waligóra, G., Węglarz, J.: Tabu list management methods for a discrete–continuous scheduling problem. Eur. J. Oper. Res. 137, 288–302 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  38. Skorin-Kapov, J.: Tabu search applied to the quadratic assignment problem. ORSA J. Comput. 2, 33–45 (1990)

    Article  MATH  Google Scholar 

  39. Glover, F.: Tabu search- Part 1. ORSA J. Comput. 1, 190–206 (1989)

    Article  MATH  Google Scholar 

  40. Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Norwell (1997)

    Book  MATH  Google Scholar 

  41. Józefowska, J., Waligóra, G., Węglarz, J.: A Performance Analysis of Tabu Search for Discrete-Continuous Scheduling Problems. Metaheuristics: Computer Decision-Making, pp. 385–404. Kluwer Academic Publishers B. V. (2003)

    Google Scholar 

  42. Janiak, A.: Minimization of the blooming mill standstills—mathematical model. Suboptimal algorithms. Zesz. Nauk. AGH, s. Mechanika 8(2), 37–49 (1989)

    MathSciNet  Google Scholar 

  43. Kurts, D.S., Swartz, C.W.: Theories of Integration. World Scientific (2004)

    Google Scholar 

  44. Różycki, R., Węglarz, J.: On job models in power management problems. Bull. Pol. Acad. Sci. Tech. Sci. 57(2), 147–151 (2009)

    Google Scholar 

  45. Li, M., Yao, A.C., Yao, F.F.: Discrete and continuous min-energy schedules for variable voltage processors. In: Proceedings of the National Academy of Sciences of the USA, vol. 103 (11), pp. 3983–3987 (2006)

    Google Scholar 

  46. Yao, F., Demers, A., Shenker, S.: A scheduling model for reduced CPU energy. In: Proceedings of the 36th IEEE Conference on the Foundations of Computer Science (FOCS) (IEEE, New York), pp. 374–382 (1995)

    Google Scholar 

  47. Kwon, W., Kim, T.: Optimal voltage allocation techniques for dynamically variable voltage processors. ACM Trans. Embed. Comput. Syst. 4(1), 211–230 (2005)

    Article  Google Scholar 

  48. Li, M., Yao, F.F.: An efficient algorithm for computing optimal discrete voltage schedules. SIAM J. Comput. 35(3), 658–671 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  49. Brinkmann, A., Kling, P., Meyer auf der Heide, F., Nagel, L., Riechers, S., Süß, T.: Scheduling shared continuous resources on many-cores. In: Proceedings of the 26th ACM Symposium on Parallelism in Algorithms and Architectures SPAA ‘14, Prague, Czech Republic, June 23–25, pp. 128–137 (2014)

    Google Scholar 

  50. Pandey, H.M., Chaudharyb, A., Mehrotra, D.: A comparative review of approaches to prevent premature convergence in GA. Appl. Soft Comput. 24, 1047–1077 (2014)

    Article  Google Scholar 

  51. Alba, E., Troya, J.: Analysis of synchronous and asynchronous parallel distributed genetic algorithms with structured and panmictic Islands. In: Rolim, J., et al. (eds.) Proceedings of the 10th Symposium on Parallel and Distributed Processing. San Juan, Puerto Rico, USA, 12–16 Aprl, pp. 248–256 (1999)

    Google Scholar 

  52. Belding, T.C.: The distributed genetic algorithm revisited. In: Eshelman, L.J. (ed.) Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 114–121. Morgan Kaufmann, San Francisco CA (1995)

    Google Scholar 

  53. Cantu-Paz, E.: Migration policies, selection pressure, and parallel evolutionary algorithms. J. Heuristics 7(4), 31–334 (2001)

    Article  MATH  Google Scholar 

  54. Cantu-Paz, E., Goldberg, D.E.: Are multiple runs of genetic algorithms better than one? In: Proceedings of the Genetic and Evolutionary Computation Conference (2003)

    Google Scholar 

  55. Muhlenbein, H.: Evolution in time and space: the parallel genetic algorithm. In: Rawlins, G. (ed.) FOGA-1,. pp. 316–337. Morgan Kaufman (1991)

    Google Scholar 

  56. Whitley, D., Starkweather, T.: GENITOR II: a distributed genetic algorithm. J. Exp. Theor. Artif. Intell. 2(3), 33–47 (1990)

    Article  Google Scholar 

  57. Wright, S.: Evolution in mendelian populations. Genetics 16, 97–159 (1931)

    Google Scholar 

  58. Wright, S.: Isolation by distance. Genetics 28, 114–138 (1943)

    Google Scholar 

  59. Tanese, R.: Parallel genetic algorithms for a hypercube. In: Grefenstette, J.J. (ed.) Hillsdale, pp. 177–183. Lawrence Erlbaum, NJ (1987)

    Google Scholar 

  60. Whitley, D., Rana, S., Heckendorn, R.B.: The island model genetic algorithm: on separability, population size and convergence. J. Comput. Inf. Technol. 7(1), 33–47 (1999)

    Google Scholar 

  61. Hart, W.E., Baden, S., Belew, R.K., Kohn, S.: Analysis of the numerical effects of parallelism on a parallel genetic algorithm. In: IEEE (ed.): CD-ROM IPPS97 (1997)

    Google Scholar 

  62. Sekaj, I.: Robust parallel genetic algorithms with re-initialisation. In: Proceedings of Parallel Problem Solving from Nature—PPSN VIII, 8th International Conference, Birmingham, UK, Sept 18–22, LNCS, vol. 3242, pp. 411–419. Springer (2004)

    Google Scholar 

  63. Prime, B., Hendtlass, T.: Evolutionary Computation Using Island Populations in Time. Innovations in Applied Artificial Intelligence, LNCS 3029, pp. 573–582 (2004)

    Google Scholar 

  64. Skolicki, Z., Kenneth, D.J.: The influence of migration sizes and intervals on island models. In: Proceedings of GECCO’05, June 25–29, Washington, DC, USA, pp. 1295–1302 (2005)

    Google Scholar 

  65. de Vega, F.F., Tomassini, M., Punch III, W.F., Sanchez-Prez, J.M.: Experimental study of multipopulation parallel genetic programming. In: Proceedings of the European Conference on Genetic Programming, Lecture Notes in Computer Science, vol. 1802, pp. 283–293. Springer (2000)

    Google Scholar 

  66. Morrison, R. W.: Designing evolutionary algorithms for dynamic environments. Natural Computing Series. Springer (2004)

    Google Scholar 

  67. Tomassini, M.: Spatially structured EAs. In: GECCO’04 Tutorials, June 2004

    Google Scholar 

  68. Skolicki, Z.: An analysis of Island models in evolutionary computation. In: Proceedings of GECCO’05, June 25–29, Washington, DC, USA, pp. 386–389 (2005)

    Google Scholar 

  69. Skolicki, Z., Kenneth, D.J.: Improving evolutionary algorithms with multi-representation island models. In: Parallel Problem Solving from Nature—PPSN VIII, LNCS 3242, pp. 420–429 (2004)

    Google Scholar 

  70. Berntsson, J., Tang, M.: Adaptive sizing of populations and number of Islands in distributed genetic algorithms. In: Proceedings of 2005 Genetic and Evolutionary Computation Conference GECCO’05, ACM, pp. 1575–1576 (2005)

    Google Scholar 

  71. Gupta, D., Ghafir, S.: An overview of methods maintaining diversity in genetic algorithms. Int. J. Emer. Technol. Adv. Eng. 2, 5 (2012). https://www.ijetae.com

  72. Friedrich, T., Oliveto, P.S., Sudholt, D., Witt, C.: Analysis of diversity-preserving mechanisms for global exploration. Evol. Comput. 17(4), 455–476 (2009)

    Article  Google Scholar 

  73. Oliveto, P.S., Zarges, C.: Analysis of diversity mechanisms for optimisation in dynamic environments with low frequencies of change. Theor. Comput. Sci. 561(A), pp. 37–56 (2015)

    Google Scholar 

  74. Kureichick, V.M., Melikhov, A.N., Miaghick, V.V., Savelev, O.V., Topchy, A.P.: Some new features in the genetic solution of the traveling salesman problem. In: Proceedings of ACEDC’96, Plymouth (1996)

    Google Scholar 

  75. Rocha, M., Neves, J.: Preventing Premature Convergence to Local Optima in Genetic Algorithms via Random Offspring Generation; LNAI (Lecture Notes in Artificial Intelligence), vol. 1611, pp. 127–136 (1999)

    Google Scholar 

  76. Storch, T., Wegener, I.: Real royal road functions for constant population size. Theoret. Comput. Sci. 320(1), 123–134 (2004)

    Article  MATH  MathSciNet  Google Scholar 

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Skakovski, A. (2018). State-of-the-Art Review. In: Population-Based Approaches to the Resource-Constrained and Discrete-Continuous Scheduling. Studies in Systems, Decision and Control, vol 108. Springer, Cham. https://doi.org/10.1007/978-3-319-62893-6_9

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