Advertisement

Metaheuristic search techniques for multi-objective and stochastic problems: a history of the inventions of Walter J. Gutjahr in the past 22 years

  • Karl F. Doerner
  • Vittorio Maniezzo
Original Paper

Abstract

This paper is a survey of the research contributions made by Walter J. Gutjahr during his career so far, and provides a classification of his areas of research, along with a discussion of the results presented in his most significant publications. Although works are divided into theoretical and application-oriented contributions, linkages among these subsets are also identified.

Keywords

Stochastic optimization Multi-objective optimization Metaheuristics Software testing Scheduling Location 

References

  1. Aarts E, Korst J (1989) Simulated annealing and Boltzmann machines. Wiley, New YorkGoogle Scholar
  2. Brailsford S, Gutjahr WJ, Rauner M, Zeppelzauer W (2007) Combined discrete-event simulation and ant colony optimisation approach for selecting optimal screening policies for diabetic retinopthy. CMS 4:59–83CrossRefGoogle Scholar
  3. Bullnheimer B, Hartl RF, Strauss C (1999) An improved ant system algorithm for vehicle routing problems. Ann Oper Res 89:319–328CrossRefGoogle Scholar
  4. Czarnowski I, Gutjahr WJ, Jedrzejowicz P, Ratajcak E, Skakowski A, Wierzbowska I (2003) Scheduling mulitprocessor tasks in presence of correlated failures. Central Eur J Oper Res Econ 11:163–182Google Scholar
  5. Czyzak P, Jaszkiewicz A (1998) Pareto simulated annealing: a metaheuristic technique for multiple-objective combinatorial optimization. J Multi-Criteria Decis Anal 7:34–47CrossRefGoogle Scholar
  6. Deb K (2002) A fast and elitist muliobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197CrossRefGoogle Scholar
  7. Deb K, Pratap A, Agarwal S, Meyarivan T (2000) A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197CrossRefGoogle Scholar
  8. Doerner K, Gutjahr WJ (2000) Representation and optimization of software usage models with non-Markovian state transitions. Inf Softw Technol 42:873–887CrossRefGoogle Scholar
  9. Doerner K, Gutjahr WJ (2003) Extracting test sequences from a Markov software usage model by ACO. In: Cantu-Paz E et al (eds) Proceedings of GECCO 2003, genetic and evolutionary computation, July 2003, Chicago USA, Springer Lecture Notes in Computer Sciences 2724. Springer, Berlin, pp 2465–2476Google Scholar
  10. Doerner K, Gutjahr WJ (2009) Multi-criteria location planning for public facilities in tsunami-prone coastal areas. OR Spectr 31:651–678CrossRefGoogle Scholar
  11. Doerner K, Gutjahr WJ, Hartl RF, Strauss C, Stummer C (2001) Ant colony optimization in multiobjective portfolio selection. In: Proceedings of MIC 2001, 4th metaheuristics international conference, Porto, Portugal, pp 243–248Google Scholar
  12. Doerner K, Gutjahr WJ, Hartl RF, Strauss C, Stummer C (2004) Pareto ant colony optimization: a metaheuristic approach to multiobjective portfolio selection. Ann Oper Res 131:79–99CrossRefGoogle Scholar
  13. Doerner K, Gutjahr WJ, Hartl RF, Karall M, Reimann M (2005) Heuristic solution for an extended double-coverage ambulance location problem for Austria. Central Eur J Oper Res 13:325–340Google Scholar
  14. Doerner K, Gutjahr WJ, Hartl RF, Strauss C, Stummer C (2006a) Pareto ant colony optimization with IP preprocessing in multiobjective project portfolio selection. Eur J Oper Res 171:830–841CrossRefGoogle Scholar
  15. Doerner K, Gutjahr WJ, Kotsis G, Polaschek M, Strauss C (2006b) Enriched workflow modelling and stochastic branch-and-bound. Eur J Oper Res 75(3):125–135Google Scholar
  16. Doerner K, Gutjahr WJ, Focke A (2007) Multicriteria tour planning for mobile healthcare facilities in a developing country. Eur J Oper Res 179(3):1078–1096CrossRefGoogle Scholar
  17. Doerner K, Gutjahr WJ, Hartl RF, Strauss C, Stummer C (2008) Nature-inspired metaheuristics for multiobjective activity crashing. Omega 36:1019–1037CrossRefGoogle Scholar
  18. Dorigo M, Stützle T (2004) Ant colony optimization. MIT, CambridgeGoogle Scholar
  19. Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern 26:29–41CrossRefGoogle Scholar
  20. Droste S, Jansen T, Wegener I (2002) On the analysis of the 1+1 evolutionary algorithm. Theoret Comput Sci 276:51–81CrossRefGoogle Scholar
  21. Felberbauer T, Gutjahr WJ, Doerner KF. Stochastic project management: multiple projects with multi-skilled human resources. SubmittedGoogle Scholar
  22. Felberbauer T, Doerner KF, Gutjahr WJ (2016) Hybrid metaheuristics for project scheduling and staffing considering interruptions between project periods and labor contracts. In: Dawid H, Doerner KF, Feichtinger G, Kort P, Seidl A (eds) Dynamic perspectives on managerial decision making, pp 349–377Google Scholar
  23. Fonseca CM, Fleming PJ (1993) Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In Forrest S (ed) Proceedings of the fifth international conference on genetic algorithms. University of Illinois at Urbana-Campaign, Morgan Kaufmann Publishers, pp 416–423Google Scholar
  24. Froeschl KA, Gutjahr WJ (2013) Project portfolio selection under uncertainty with outsourcing opportunities. Flex Serv Manuf J 25:255–281CrossRefGoogle Scholar
  25. Gendreau M, Laporte G, Semet F (1997) Solving an ambulance location model by tabu search. Location Sci 5:75–87CrossRefGoogle Scholar
  26. Gutjahr WJ (1995) Optimal test distributions for software failure cost estimation. IEEE Trans Softw Eng 21:219–228CrossRefGoogle Scholar
  27. Gutjahr WJ (1996) Design of reliable 3-state-device networks by genetic algorithms and simulated annealing. Central Eur J Oper Res Econ 3:257–284Google Scholar
  28. Gutjahr WJ (1997) Importance sampling of test cases in Markovian software usage models. Probab Eng Inf Sci 11:19–36CrossRefGoogle Scholar
  29. Gutjahr WJ (1999) Partition testing versus random testing: the influence of uncertainty. IEEE Trans Softw Eng 25:661–674CrossRefGoogle Scholar
  30. Gutjahr WJ (2000a) A Graph-based Ant System and its convergence. Future Gener Comput Syst 16:873–888CrossRefGoogle Scholar
  31. Gutjahr WJ (2000b) Software dependability evaluation based on Markov usage models. Performan Eval 40:199–222CrossRefGoogle Scholar
  32. Gutjahr WJ (2001) A reliability model for nonhomogeneous redundant software version with correlated failures. Comput Syst Sci Eng 16(6):361–370Google Scholar
  33. Gutjahr WJ (2002) ACO algorithms with guaranteed convergence to the optimal solution. Inf Process Lett 82:145–153CrossRefGoogle Scholar
  34. Gutjahr WJ (2003a) A generalized convergence result for the graph-based ant system metaheuristic. Probab Eng Inf Sci 17:545–569CrossRefGoogle Scholar
  35. Gutjahr WJ (2003b) A converging ACO algorithm for stochastic combinatorial optimization. In: Proceedings of SAGA 2003. Stochastic algorithms: foundations and applications. Springer LNCS vol 2827, pp 10–25Google Scholar
  36. Gutjahr WJ (2004) S-ACO: an ant-based approach to combinatorial optimization under uncertainty. In: Proceedings of ANTS 2004, 4th international workshop on ant colony optimization and swarm intelligence, BrusselsGoogle Scholar
  37. Gutjahr WJ (2005) Two metaheuristics for multiobjective stochastic combinatorial optimization. In: Proceedings SAGA 2005. Stochastic algorithms: foundations and applications, Springer Lecture Notes in Computer Science, vol 3777, pp 116–125Google Scholar
  38. Gutjahr WJ (2006) On the finite-time dynamics of ant colony optimization. Methodol Comput Appl Probab 8:105–133CrossRefGoogle Scholar
  39. Gutjahr WJ (2007) Mathematical runtime analysis of ACO algorithms: survey on an emerging issue. Swarm Intell 1:59–79CrossRefGoogle Scholar
  40. Gutjahr WJ (2008) First steps to the runtime complexity analysis of ant colony optimization. Comput Oper Res 35(9):2711–2727CrossRefGoogle Scholar
  41. Gutjahr WJ (2009a) Convergence analysis of metaheuristics. In: Maniezzo V, Stuetzle T, Voss S (eds) Annals of information systems 10, special issue on, Matheuristics: hybridizing metaheuristics and mathematical programming, pp 159–187Google Scholar
  42. Gutjahr WJ (2009b) A provably convergent heuristic for stochastic bicriteria integer programming. J Heuristics 15:227–258CrossRefGoogle Scholar
  43. Gutjahr WJ (2011a) Ant colony optimization: recent developments in theoretical analysis. In: Auger A, Doerr B (eds) Theory of randomized search heuristics. World Scientific, Singapore, pp 225–254Google Scholar
  44. Gutjahr WJ (2011b) Optimal dynamic portfolio selection for projects under a competence development model. OR Spectr 33:173–206CrossRefGoogle Scholar
  45. Gutjahr WJ (2012) Runtime analysis of an evolutionary algorithm for stochastic multi-objective combinatorial optimization. Evol Comput 20:395–421CrossRefGoogle Scholar
  46. Gutjahr WJ (2014a) In: Schwindt C, Zimmermann J (eds) Handbook on project management and scheduling. Springer, BerlinGoogle Scholar
  47. Gutjahr WJ (2014b) A three-objective optimization approach to cost effectiveness analysis under uncertainty. In: Operations research proceedings 2012, Selected Papers of the International Annual, Hannover, Germany, Sept. 5–7, 2012. Springer, Berlin, pp 239–246Google Scholar
  48. Gutjahr WJ (2015) Bi-objective multi-mode project scheduling under risk aversion. Eur J Oper Res 246:421–434CrossRefGoogle Scholar
  49. Gutjahr WJ, Dzubur N (2016) Bi-objective bilevel optimization of distribution center locations considering user equilibria. In: Stefan H et al (eds) Transportation research Part E, vol. 85, pp 1–22Google Scholar
  50. Gutjahr WJ, Pflug G (1996) Simulated annealing for noisy cost functions. J Global Optim 8:1–13CrossRefGoogle Scholar
  51. Gutjahr WJ, Rauner M (2007) An ACO algorithm for a dynamic regional nurse-scheduling problem in Austria. Comput Oper Res 34(3):642–666CrossRefGoogle Scholar
  52. Gutjahr WJ, Reiter P (2010) Bi-objective project portfolio selection and staff assignment under uncertainty. Optim J Math Program Oper Res 59:417–445Google Scholar
  53. Gutjahr WJ, Sebastiani G (2008) Runtime analysis of ant colony optimization with best-so-far reinforcement. Methodol Comput Appl Probab 10:409–433CrossRefGoogle Scholar
  54. Gutjahr WJ, Uchida G (2002) A branch-and-bound approach to the optimization of redundant software under failure correlation. Comput Oper Res 29:1773–1791CrossRefGoogle Scholar
  55. Gutjahr WJ, Hellmayr A, Pflug G (1999) Optimal stochastic single-machine tardiness scheduling by stochastic branch-and-bound. Eur J Oper Res 117:396–413CrossRefGoogle Scholar
  56. Gutjahr WJ, Strauss C, Wagner E (2000a) A stochastic branch-and-bound approach to activity crashing in project management. INFORMS J Comput 12:65–84CrossRefGoogle Scholar
  57. Gutjahr WJ, Strauss C, Toth M (2000b) Crashing of stochastic processes by sampling and optimization. Bus Process Manag J 6:65–84CrossRefGoogle Scholar
  58. Gutjahr W.J, Katzensteiner S, Reiter PA (2007) VNS algorithm for noisy problems and its application to project portfolio analysis. In: Hromkovic J et al (eds) Proceedings of SAGA 2007, stochastic algorithms: foundations and applications, Springer Lecture Notes in Computer Science 4665. Springer, Berlin, pp 93–104Google Scholar
  59. Gutjahr WJ, Katzensteiner S, Reiter P, Stummer C, Denk M (2008) Competence-driven project portfolio selection, scheduling and staff assignment. CEJOR 16:281–306CrossRefGoogle Scholar
  60. Gutjahr WJ, Katzensteiner S, Reiter P, Stummer C, Denk M (2010) Multi-objective decision analysis for competence-oriented project portfolio selection. Eur J Oper Res 205:670–679CrossRefGoogle Scholar
  61. Hajek B (1988) Cooling schedules for optimal annealing. Math OR 13:311–329CrossRefGoogle Scholar
  62. Heimerl C, Kolisch R (2012) An efficient metaheuristic for integrated scheduling and staffing its projects based on a generalized minimum cost flow network. Naval Res Logist 59:111–127CrossRefGoogle Scholar
  63. Kallel L, Naudts B, Reeves CR (1998) Properites of tness functions and search landscapes. In: Kallel L, Naudts B, Rogers A (eds) Theoretical aspects of evolutionary computing. Springer, Berlin, pp 174–206Google Scholar
  64. Laumanns M, Thiele L, Deb K, Zitzler E (2002a) Combining convergence and diversity in evolutionary multi-objective optimization. Evol Comput 10:263–282CrossRefGoogle Scholar
  65. Laumanns M, Thiele L, Zitzler E, Welzl E, Deb K (2002b) Running time analysis of multi-objective evolutionary algorithms on pseudo-boolean functions. In: Conference on parallel problem solving from nature PPSN VII, LNCS 2439. Springer, Berlin, pp 44–53Google Scholar
  66. Laumanns M, Thiele L, Zitzler E (2006) An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method. Eur J Oper Res 169(3):932–942CrossRefGoogle Scholar
  67. Maniezzo V, Stuetzle T, Voss S (eds) (2009a) Matheuristics: hybridizing metaheuristics and mathematical programming. In: Series: annals of information systems, vol 10. Springer, BerlinGoogle Scholar
  68. Maniezzo V, Voss S, Hansen P (eds) (2009b) Special Issue on mathematical contributions to metaheuristics. J Heurist 15(3): 197Google Scholar
  69. Maniezzo V, Boschetti MA, Gutjahr WJ (2016) Stochastic real world warehouse premarshalling. In: Proceedings of matheuristics 2016, pp 100–103Google Scholar
  70. Moder JJ, Philips CF, Davis EW (1983) Project management with CPM, PERT and precedence diagramming, 3rd edn. Nostrand, New YorkGoogle Scholar
  71. Norkin VI, Ermoliev YM, Ruszczynsky A (1998) On optimal allocation of indivisibles under uncertainty. Oper Res 46:381–395CrossRefGoogle Scholar
  72. Rath S, Gutjahr WJ (2014) A math-heuristic for the warehouse location routing problem in disaster relief. Comput Oper Res 42:25–39CrossRefGoogle Scholar
  73. Rath S, Gendreau M, Gutjahr WJ (2016) Bi-objective stochastic programming models for determining depot locations in disaster relief operations. Int Trans Oper Res 23:997–1023CrossRefGoogle Scholar
  74. Rauner M, Gutjahr WJ, Heidenberger K, Wagner J, Pasia J (2010) Dynamic policy modeling for chronic diseases: metaheuristic-based identification of pareto-optimal screening strategies. Oper Res 58:1269–1286CrossRefGoogle Scholar
  75. Reiter P, Gutjahr WJ (2012) Exact hybrid algorithms for solving a bi-objective vehicle routing problem. CEJOR 20:19–43CrossRefGoogle Scholar
  76. Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithm. In: Genetic algorithms and their applications, proceedings of the first international conference on genetic algorithms. Lawrence Erlbaum, Hillsdale, pp 93–100Google Scholar
  77. Stummer C, Kiesling E, Gutjahr WJ (2009) A multicriteria decision support system for competence-driven project portfolio selection. Int J Inf Technol Decis Mak 8:379–401CrossRefGoogle Scholar
  78. Tricoire F, Graf A, Gutjahr WJ (2012) The bi-objective stochastic covering tour problem. Comput Oper Res 39:1582–1592CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Business AdministrationUniversity of ViennaViennaAustria
  2. 2.Department of InformaticsUniversity of BolognaBolognaItaly

Personalised recommendations