A Genetic Algorithm Approach to the Fire Sequencing Problem

  • Kwon, O-Jeong (Office of the Analysis & Evaluation ROK Army Hq.)
  • Published : 2003.12.01

Abstract

A fire sequencing problem is considered. Fire sequencing problem is a kind of scheduling problem that seeks to minimize the overall time span under a result of weapon­target allocation problem. The assigned weapons should impact a target simultaneously and a weapon cannot transfer the firing against another target before all planned rounds are consumed. The computational complexity of the fire sequencing problem is strongly NP­complete even if the number of weapons is two, so it is difficult to get the optimal solution in a reasonable time by the mathematical programming approach. Therefore, a genetic algorithm is adopted as a solution method, in which the representation of the solution, crossover and mutation strategies are applied on a specific condition. Computational results using randomly generated data are presented. We compared the solutions given by CPLEX and the genetic algorithm. Above $7(weapon){\times}15(target)$ size problems, CPLEX could not solve the problem even if we take enough time to solve the problem since the required memory size increases dramatically as the number of nodes expands. On the other hand, genetic algorithm approach solves all experimental problems very quickly and gives good solution quality.

Keywords

References

  1. Ash, M., Flood's Assignment Model for Small Kill Level, Operations Research Vol. 7, pp.258-260, 1959 https://doi.org/10.1287/opre.7.2.258
  2. Baker, K., Introduction to Sequencing and Scheduling, Wiley, New York, 1974
  3. Biegal, J., Draven, J., Genetic Algorithm and job Shop Scheduling, Computers and Industrial Engineering Vol. 19, pp.81-91, 1990 https://doi.org/10.1016/0360-8352(90)90082-W
  4. Bracken, J., Layered Defense of Deceptively Based ICBMs, Naval Research Logistics Quarterly, Vol. 31, pp.653-670, 1984 https://doi.org/10.1002/nav.3800310414
  5. Bracken, J., McCormick, G., Selected Application of Non-linear Programming, John Wiley & Sons, New York, 1980
  6. Charles, J., A Genetic Algorithm for Service Level Based Vehicle Scheduling, European Journal of Operations Research, Vol. 93, pp.121-134, 1996 https://doi.org/10.1016/0377-2217(95)00185-9
  7. Chen, C., Vempati, V., Aljaber, N., An Application of Genetic Algorithm for Flow Shop Problems, European Journal of Operations Research, Vol. 80, pp. 389-396, 1995 https://doi.org/10.1016/0377-2217(93)E0228-P
  8. David, G., Genetic Algorithm in Search. Optimization. and Machine Learning, Addison-Wesley, Reading MA, 1992
  9. Day, H., Allocating Weapons to Target Complexes by means of Nonlinear Programming, Operations Research, Vol. 14, pp.992-1013, 1966 https://doi.org/10.1287/opre.14.6.992
  10. Dobson, G., Kamarkar, D., 1989. Simultaneous Resource Scheduling to Minimize Weighted Flow Times, Operations Research, 37, 592-600 https://doi.org/10.1287/opre.37.4.592
  11. Firstman, I., An Approximating Algorithm for an Optimum Aim-Points Problem, Naval Research Logistics Quarterly, Vol. 7, pp.151-167, 1960 https://doi.org/10.1002/nav.3800070205
  12. Furman, G., Greenberg, J., Optimal Weapon Allocation with Overlapping Area Defenses, Operations Research, Vol. 21, pp.1291-1308, 1973 https://doi.org/10.1287/opre.21.6.1291
  13. Garey, M., Johnson, D., Computers and Intractability, Bell Telephone Laboratories Incorporated, New Jersey, 1979
  14. Gratte, J. H., A Targeting Model that Minimizes Collateral Damage, Naval Research Logistics, Vol. 25, pp.315-322, 1978 https://doi.org/10.1002/nav.3800250212
  15. Ho, J., Chang, Y., A New Heuristic for the n-jobs, m-machine flow-shop problem, European Journal of Operations Research, Vol. 52, pp.194-202, 1991 https://doi.org/10.1016/0377-2217(91)90080-F
  16. Kendall, E., Rhonda, K., Ramesh. S., O.R/MS Today, Vol. 8, 1992
  17. Kwon, O., Kang, D., Lee K., Park, S., Park, K., Lagrangian Relaxation Approach to the Targeting Problem, Naval Research Logistics, Vol. 46, pp.640-653, 1999 https://doi.org/10.1002/(SICI)1520-6750(199909)46:6<640::AID-NAV3>3.0.CO;2-Q
  18. Kwon, O., Lee, K., Park, S., Targeting and Scheduling Problem for Field Artillery, Computers and Industrial Engineering, Vol. 33, pp.693-696, 1997 https://doi.org/10.1016/S0360-8352(97)00224-6
  19. Lee, K., Kwon O., Park, S., Park, K., Complexity of the Fire Sequencing Problem, International journal of Management Science, Vol. 5, pp.55-59, 1999
  20. Manne, A., A Target Assignment Problem, Operations Research, Vol. 5, pp.346-351, 1958 https://doi.org/10.1287/opre.6.3.346
  21. Miercort, F., Soland, R., Optimal Allocation of Missiles against Area and Point Defenses, Operations Research, Vol.19, pp.605-617, 1970 https://doi.org/10.1287/opre.19.3.605
  22. Moutaz, K., Zbigniew, M., Michael, W., The Use of Genetic Algorithm to Solve the Economic Lot Size Scheduling Problem, European Journal of Operations Research, Vol. 110, pp.509-524, 1998 https://doi.org/10.1016/S0377-2217(97)00270-1
  23. Orin, D., Optimal Weapons Allocation against Layered Defenses, Naval Research Logistics, Vol. 34, pp.605-617, 1987 https://doi.org/10.1002/1520-6750(198710)34:5<605::AID-NAV3220340502>3.0.CO;2-L
  24. Pinedo, M., Scheduling, Theory. Algorithms and Systems, Prentice Hall, New Jersey, 1995
  25. Sangit, C., Cecilia, C., Lucy, L., Genetic Algorithms and Traveling Salesman Problems, European Journal of Operations Research, Vol. 93, pp.490-510, 1996 https://doi.org/10.1016/0377-2217(95)00077-1
  26. Sherali, H., Kim, S., Parrish, E., Probabilistic Partial Set Covering Problem, Naval Research Logistics, Vol.38, pp.41-51, 1991 https://doi.org/10.1002/1520-6750(199102)38:1<41::AID-NAV3220380106>3.0.CO;2-L
  27. Taillard, E., Some Efficient Heuristic methods for the Flow Shop Sequencing Problem, European Journal of Operations Research, Vol. 47, pp.65-74, 1990 https://doi.org/10.1016/0377-2217(90)90090-X