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The Robust Artillery Locating Radar Deployment Model Against Enemy' s Attack Scenarios

적 공격시나리오 기반 대포병 표적탐지레이더 배치모형

  • Lee, Seung-Ryul (Department of Operation Research, Korea National Defense University) ;
  • Lee, Moon-Gul (Department of Operation Research, Korea National Defense University)
  • Received : 2020.12.17
  • Accepted : 2020.12.22
  • Published : 2020.12.31

Abstract

The ROK Army must detect the enemy's location and the type of artillery weapon to respond effectively at wartime. This paper proposes a radar positioning model by applying a scenario-based robust optimization method i.e., binary integer programming. The model consists of the different types of radar, its available quantity and specification. Input data is a combination of target, weapon types and enemy position in enemy's attack scenarios. In this scenario, as the components increase by one unit, the total number increases exponentially, making it difficult to use all scenarios. Therefore, we use partial scenarios to see if they produce results similar to those of the total scenario, and then apply them to case studies. The goal of this model is to deploy an artillery locating radar that maximizes the detection probability at a given candidate site, based on the probability of all possible attack scenarios at an expected enemy artillery position. The results of various experiments including real case study show the appropriateness and practicality of our proposed model. In addition, the validity of the model is reviewed by comparing the case study results with the detection rate of the currently available radar deployment positions of Corps. We are looking forward to enhance Korea Artillery force combat capability through our research.

Keywords

References

  1. Bayram, V. and Yaman, H., Shelter Location and Evacuation Route Assignment Under Uncertainty : A Benders Decomposition Approach, Transportation Science, 2018, Vol. 52, No. 2, pp. 416-436. https://doi.org/10.1287/trsc.2017.0762
  2. Feng, Y. and Ryan, S.M., Scenario construction and reduction applied to stochastic power generation expansion planning, Computers & Operation Research, 2013, Vol. 40, No. 1, pp. 9-23. https://doi.org/10.1016/j.cor.2012.05.005
  3. Gu, J.M., Zhou, Y., Das, A., Moon, I.K., and Lee, G.M., Medical relief shelter location problem with patient severity under a limited relief budget, Computers & Industrial Engineering, 2018, Vol. 125, pp. 720-728. https://doi.org/10.1016/j.cie.2018.03.027
  4. Headquarter of US Army, Field Manual 3-09.12 : Tactics, Techniques, and Procedures for Field Artillery Target Acquisition, 2015.
  5. Headquarter of ROK Army, Field Manual 32-12 : Battalion of Observatory, 2012.
  6. Headquarter of ROK Army, Training Circular 17-4-3 : Operation of Target Detection Radar II, 2017.
  7. Hwang, S.M. and Song, S.H., Optimization Methodology for Sales and Operations Planning by Stochastic Programming under Uncertainty : A Case Study in Service Industry, Journal of Society of Korea Industrial and Systems Engineering, 2016, Vol. 39, No. 4, pp. 137-146. https://doi.org/10.11627/jkise.2016.39.4.137
  8. Jang, J.P., Research on the Countermeasures against NK's Field Artillery Threats Focused on the ROK Field Artillery, [dissertation], [Gyeonggi, Korea] : Daejin University, 2017.
  9. Jin, S.H., Kim, J.Y., Kim, K.S., and Jeong, S.J., Scenario-Based Optimization of Patient Distribution and Medical Resource Allocation in Disaster Response, Journal of the Korean Institute of Industrial Engineers, 2014, Vol. 40, No. 2, pp. 151-162. https://doi.org/10.7232/JKIIE.2014.40.2.151
  10. Kall, P. and Mayer, J., Stochastic Linear programming Models, Theory, and Computation, Kluwer Academic Publishers, 2005, pp. 75-189.
  11. Lee, M.G., Anti-artillery radar positioning by using stochastic optimization method, General Conference on Korea Institute of Military Science and Technology, 2018, Jeju, Korea, pp. 2083-2084.
  12. Lee, M.G., Optimal Allocation Model of Anti-Artillery Radar by Using ArcGIS and its Specifications, Journal of Society of Korea Industrial and Systems Engineering, 2018, Vol. 41, No. 2, pp. 74-83. https://doi.org/10.11627/jkise.2018.41.2.074
  13. Li, Z. and Floudas, C.A., Optimal scenario reduction framework based on distance of uncertainty distribution and output performance : I. Single reduction via mixed integer linear optimization, Computers and Chemical Engineering, 2014, Vol. 70, pp. 50-66. https://doi.org/10.1016/j.compchemeng.2014.03.019
  14. Li, Z. and Floudas, C.A., Optimal scenario reduction framework based on distance of uncertainty distribution and output performance : II. Sequential reduction, Computers and Chemical Engineering, 2016, Vol. 84, pp. 599-610. https://doi.org/10.1016/j.compchemeng.2015.05.010
  15. Park, T.Y. and Lim, J.S., Method on Radar deployment for Ballistic Missile Detection Probability Improvement, Journal of the Korea Institute of Information and Communication Engineering, 2016, Vol. 20, No. 3, pp. 669-676. https://doi.org/10.6109/jkiice.2016.20.3.669
  16. Park, S.H. and Lee, M.G., Optimal Allocation Heuristic Method of Military Engineering Equipments during Artillery Position Construction Operation, Journal of Society of Korea Industrial and Systems Engineering, 2017, Vol. 40, No. 1, pp. 11-21. https://doi.org/10.11627/jkise.2017.40.1.011
  17. Son, S.H. and Cho, N.S., Optimization Models for Deploying Air Defense Mobile Radars, Journal of the Korean Institute of Industrial Engineers, 2019, Vol. 45, No. 3, pp. 225-239. https://doi.org/10.7232/JKIIE.2019.45.3.225
  18. Wang, L., A two-stage stochastic programming framework for evacuation planning in disaster responses, Computers & Industrial Engineering, 2020, Vol. 145, pp. 106458. https://doi.org/10.1016/j.cie.2020.106458
  19. Wagner, D.H., Charles Mylander, W., and Sanders, T.J., Naval operations analysis, third ed., Naval institute press, 1999, pp. 108-115.