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Interactive Model-Based Decision Making for Time-Critical Vehicle Routing

  • Subhashini Ganapathy
  • Sasanka Prabhala
  • S. Narayanan
  • Raymond R. Hill
  • Jennie J. Gallimore
Chapter

Abstract

Advances in technology, software algorithms, and operations research methods provide the opportunity for effectively coupling the human decision maker with optimization modelling algorithms in large-scale systems operating in dynamic and uncertain environments. In military applications, such as search and rescue/destroy missions or real-time route planning or re-planning provide time windows within which critical decisions need to be made. Using a specially constructed human-computer integrated routing application, an evaluation was conducted to compare the effects of interactive model-based solutions with respect to automated solutions generated by mathematical modelling algorithms in the context of unmanned aerial vehicle route planning. Results indicate that significantly more high priority targets were covered in the human integrated approach compared to the automated solution without any significant degradation with respect to all the other dependent measures including percentage of total targets covered, low priority targets covered, total targets covered in threat zone, high priority targets covered in threat zone, and low priority targets covered in threat zone.

Keywords

Human Operator Unmanned Aerial Vehicle Vehicle Rout Problem Vehicle Route Total Target 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

S. Narayanan, R. Hill, and S. Ganapathy were supported in part by Air Force Office of Scientific Research grant FA9550-04-1-0163.

References

  1. Ammons JC, Govindaraj T, Mitchell CM (1998) Decision models for aiding FMS scheduling and control. IEEE Trans Syst Man Cybern 18(5):744–756CrossRefGoogle Scholar
  2. Barnes MJ, Matz MF (1998) Crew simulation for unmanned aerial vehicle (UAV) applications: sustained effects, shift factors, interface issues, and crew size. In Proceedings of the human factors and ergonomics society 42nd annual meeting, pp 143–147Google Scholar
  3. Batez BW, Pas EI, Neebe AW (1990) Generating alternative solutions for dynamic programming based planning problem. Socio Econ Plan Sci 24(1):27–34CrossRefGoogle Scholar
  4. Bodin LD, Golden BL, Assad AA, Ball M (1983) The state of the art in the routing and scheduling of vehicles and crews. Comput Oper Res 10(2):163–212MathSciNetGoogle Scholar
  5. Cordeau JF, Gendreau M, Lapotr G, Potvin JY, Semet F (2002) A guide to vehicle routing heuristics. J Oper Res Soc 53(5):512–522MATHCrossRefGoogle Scholar
  6. Dantzig GB, Ramser RH (1959) The truck dispatching problem. Manag Sci 6:80–91MathSciNetMATHCrossRefGoogle Scholar
  7. Endsley MR (1996) Automation and situational awareness. In: Parasuraman R, Mouloua M (eds) Automation and human performance: theory and applications, pp 163–181Google Scholar
  8. Endsley MR, Kaber DB (1987) Level of automation effects on performance, situation awareness and workload in a dynamic control task. Ergonomics 42(3):462–492CrossRefGoogle Scholar
  9. Endsley MR, Kiris EO (1995) The out-of-the-loop performance problem and level of control in automation. Hum Factors 37(2):381–394CrossRefGoogle Scholar
  10. Entin EB, Entin EE, Millan J, Serfaty D (1995) Situational awareness and human performance in target recognition. In: IEEE international conference on systems, man and cybernetics.  pp 3833–3837Google Scholar
  11. Evans GW, Stuckman B, Mollaghasemi M (1991) Multicriteria optimization of simulation models. In: Winter simulation conference,  pp 894–900Google Scholar
  12. Fisher ML (1985) Interactive optimization. Ann Oper Res 5:541–556CrossRefGoogle Scholar
  13. Golden BL, Assad AA (1988) Vehicle routing: methods and studies. In: Golden BL, Assad AA (eds) Studies in management science and systems. Elsevier, NYGoogle Scholar
  14. Harder WR, Hill RR, Moore JT (2004) A Java™ universal vehicle router for routing unmanned aerial vehicle. Int Trans Oper Res 11:259–275MATHCrossRefGoogle Scholar
  15. Jentsch F, Bowers C (1996) Automation and crew performance: the importance of who and what. In Proceedings of the human factors and ergonomics society 40th annual meeting, pp 46–53Google Scholar
  16. Jones PM, Mitchell CM (1994) Model-based communicative acts: human-computer collaboration in supervisory control. Int J Hum-Comput Stud 41:527–551CrossRefGoogle Scholar
  17. Klau GW, Lesh NB, Marks JW, Mitzenmacher M (2002) Human-guided tabu search. In Proceedings of national conference on artificial intelligence (AAAI), pp 41–47Google Scholar
  18. Laporte G (1992) The vehicle routing problem: an overview of exact and approximate algorithms. Eur J Oper Res 59:345–358MATHCrossRefGoogle Scholar
  19. Li S (2000) The development of a hybrid intelligent system for developing marketing strategy. Decis Support Syst 27(4):395–409CrossRefGoogle Scholar
  20. Li S, Li JZ (2010) Agents international: integration of multiple agents, simulation, knowledge bases and fuzzy logic for international marketing decision making, expert systems with applications. Int J 37(3):2580–2587Google Scholar
  21. Massaglia and Ostanello (1989) N-tomic: a support system for multicriteria segmentation problems. In: Korhone P, Lewandowski A, Wallenious J (eds) Multiple criteria decision support. Springer-Verlag, Berlin, pp 167–174Google Scholar
  22. Mitchell CM (1987) GT-MSOCC: a domain for research on human-computer interaction and decision aiding in supervisory control systems. IEEE Trans Syst Man Cybern 17(4):553–572CrossRefGoogle Scholar
  23. Mosier KL, Skitka LJ, Heers S, Burdick M (1998) Automation bias: decision making and performance in high-tech cockpits. Int J Aviat Psychol 8(1):47–63CrossRefGoogle Scholar
  24. Norman DA (1986) Cognitive engineering. In: Norman DA, Draper SW (eds) User centered system design: new perspectives on human computer interaction. Lawrence Erlbaum Associates, NJ, pp 31–61Google Scholar
  25. Nulty WG, Ratliff HD (1991) Interactive optimization methodology for fleet scheduling. Nav Res Logist 38:669–677MATHCrossRefGoogle Scholar
  26. O’ Rourke KP, Bailey TG, Hill RR, Carlton WB (2001) Dynamic routing of unmanned aerial vehicles using reactive tabu search. Mil Oper Res 6(1):5–30Google Scholar
  27. Ombuki B, Ross BJ, Hanshar F (2006) Multi-objective genetic algorithms for vehicle routing problem with time windows. Appl Intell 24(1):17–30CrossRefGoogle Scholar
  28. Parasuraman R, Mouloua M, Hilburn H (1999) Adaptive aiding and adaptive task allocation enhance human–machine interaction. In: Scerbo M, Mouloua M (eds) Automated technology and human performance: current research and trend. Lawrence Erlbaum Associates, NJ, pp 119−123Google Scholar
  29. Prabhala S, Gallimore JJ (2004) Investigation of error rates when controlling multiple uninhabited combat aerial vehicles. In Proceedings of the winter simulation conference, pp 1026–1031Google Scholar
  30. Ruff HA, Narayanan S, Draper M (2002) Human interaction with levels of automation and decision aid fidelity in the supervisory control of multiple simulated teleoperated air vehicles. Presence Teleoper Virtual Environ 11(4):335–351CrossRefGoogle Scholar
  31. Schneider NL, Narayanan S, Patel C (2000) Integrating genetic algorithms and interactive simulations for airbase logistics planning. In: Suzuki Y, Roy R, Ovaska S, Furuhashi T, Dote Y (eds) Soft computing in industrial applications. Springer-verlag, NY, pp 309–318Google Scholar
  32. Scott SD, Lesh N, Klau WG (2002) Investigating human-computer optimization. In Proceedings of CHI conference, pp 155–162Google Scholar
  33. Sheridan TB (1997) Supervisory control. In: Salvendy G (ed) Handbook of human factors and ergonomics. John Wiley & Sons, NJ, pp 1295–1327Google Scholar
  34. Smith PJ, McCoy CE, Layton C (1997) Brittleness in the design of cooperative problem-solving systems: the effects on user performance. IEEE Trans Syst Man Cybern 27(3):360–371CrossRefGoogle Scholar
  35. Thackray RI, Touchstone RM (1989) Detection efficiency on an air traffic control monitoring task with and without computer aiding. Aviat Space Environ Med 60(8):744–748Google Scholar
  36. Toth P, Vigo D (2002) The vehicle routing problem. Society for Industrial and Applied Mathematics, PhiladelphiaMATHCrossRefGoogle Scholar
  37. Venugopal V, Narendra TT (1990) An interactive procedure for multiobjective optimization using Nash bargaining principle. Decis Support Syst 6(3):261–268CrossRefGoogle Scholar
  38. Wang HF, Shen HF (1989) Group decision support with MOLP applications. IEEE Trans Syst Man Cybern 19(1):143–153CrossRefGoogle Scholar
  39. Waters CDJ (1984) Interactive vehicle routing. J Oper Res Society 35(9):821–826Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Subhashini Ganapathy
    • 1
  • Sasanka Prabhala
    • 2
  • S. Narayanan
    • 3
  • Raymond R. Hill
    • 4
  • Jennie J. Gallimore
    • 3
  1. 1.User Experience Research GroupIntel CorporationHillsboroUSA
  2. 2.Interactions and Experience Research GroupIntel CorporationHillsboroUSA
  3. 3.Department of Biomedical, Industrial, and Human Factors EngineeringWright State UniversityDaytonUSA
  4. 4.Department of Operational SciencesAir Force Institute of TechnologyWPAFBUSA

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