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Meta-heuristic algorithms for resource Management in Crisis Based on OWA approach

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Abstract

In crisis management, Threat Evaluation (TE) and Resource Allocation (RA) are two key components. To build an automated system in this area after modelling Threat Evaluation and Resource Allocation processes, solving these models and finding the optimal solution are further important issues. In this paper, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithms (SPEA-II) are employed to solve a multi-objective multi-stage Resource Allocation problem. These Algorithms have been compared using normalized values of the objectives by generational distance, spread, hyper-volume, cardinality and actual computational times. It is found that the non-dominated solutions obtained by SPEA-II are better than NSGA-II both in terms of convergence and diversity but at the expense of computational time. Here, the fuzzy inference systems and the decision tree have been used to conduct threat evaluation process. Finally, Ordered Weighted Averaging (OWA) with maximum Bayesian entropy method for determining the operator weights has been used to pick the final choice among optimal options. We plan to use the proposed method in this paper for crisis management in Iranian Red Crescent organization during fire fighting. Two real studies have been done and results have been presented.

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References

  1. Acuna JA, Castro JLZ, Charkhgard H (2019) Ambulance allocation optimization model for the overcrowding problem in US emergency departments: a case study in Florida. Socio Econ Plan Sci 71:100747. https://doi.org/10.1016/j.seps.2019.100747

    Article  Google Scholar 

  2. Ahner DK, Parson CR (2015) Optimal multi-stage allocation of weapons to targets using adaptive dynamic programming. Optim Lett 9(8):1689–1701

    Article  MathSciNet  MATH  Google Scholar 

  3. Ahuja RK, Kumar A, Jha KC, Orlin JB (2007) Exact and heuristic methods for the weapon target assignment problem. Oper Res 55(6):1136–1146

    Article  MathSciNet  MATH  Google Scholar 

  4. Bertsekas DP, Homer ML, Logan DA, Patek SD, Sandell NR (2000) Missile defense and interceptor allocation by NeuroDynamic programming. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans 30(1):42–51

    Article  Google Scholar 

  5. Bogdanowicz ZR, Tolano A, Patel K, Coleman NP (2013) Optimization of weapon–target pairings based on kill probabilities. IEEE transactions on cybernetics 43(6):1835–1844

    Article  Google Scholar 

  6. Bogdanowicz ZR (2012) Advanced input generating algorithm for effect-based weapon–target pairing optimization. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans. 42(1):276–280

    Article  MathSciNet  Google Scholar 

  7. Boonmee C, Arimura M, Asada T (2017) Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction 24:485–498

    Article  Google Scholar 

  8. Burr SA, Falk JE, Karr AF (1985) Integer prim-read solutions to a class of target defense problems. Oper Res 33(4):726–745

    Article  MATH  Google Scholar 

  9. Cao Q, He Z (2001) A genetic algorithm of solving WTA problem. Control Theory and Applications 18(1):76–79

    MATH  Google Scholar 

  10. Çetin E, Esen ST (2006) A weapon–target assignment approach to media allocation. Appl Math Comput 175(215):1266–1275

    MATH  Google Scholar 

  11. Chachi J, Taheri SM, Arghami NR (2014) A hybrid fuzzy regression model and its applicationin hydrology engineering. Appl Soft Comput 25:149–158

    Article  Google Scholar 

  12. Chaji AR, Fukuyama H, Shiraz RK (2018) Selecting a model for generating OWA operator weights in MAGDM problems by maximum entropy membership function. Comput Ind Eng 124:370–378

    Article  Google Scholar 

  13. Chaji AR (2017) Analytic approach on maximum Bayesian entropy ordered weighted averaging operators. Comput Ind Eng 105:260–264

    Article  Google Scholar 

  14. Coello CCA, Lamont GB, David A. Van V. 2007. Evolutionary algorithms for solving multi-objective problems. Springer, New York, second edition

  15. Curry DM, Dagli CH (2014) Computational complexity measures for many-objective optimization problems. Procedia Computer Science 36:185–191

    Article  Google Scholar 

  16. Dahan H, Cohen S, Rokach L, Maimon O (2014) Proactive data mining with decision trees. Springer-Verlag, New York

    Book  Google Scholar 

  17. Davis MT, Robbins MJ, Lunday BJ (2017) Approximate dynamic programming for missile defense interceptor fire control. Eur J Oper Res 259(3):873–886

    Article  MathSciNet  MATH  Google Scholar 

  18. Day RH (1966) Allocating weapons to target complexes by means of nonlinear programming. Oper Res 14(6):992–1013

    Article  Google Scholar 

  19. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  20. Ejaz W, Sharma SK, Saadat S, Naeem M, Chughtai NA (2020) A comprehensive survey on resource allocation for CRAN in 5G and beyond networks. J Netw Comput Appl 160:102638. https://doi.org/10.1016/j.jnca.2020.102638

    Article  Google Scholar 

  21. Farias V, Roy BV (2006) Approximation algorithms for dynamic resource allocation. Oper Res Lett 34(2):180–190

    Article  MathSciNet  Google Scholar 

  22. Fujita H, Gaeta A, Loia V, Orciuoli F (2019) Hypotheses analysis and assessment in counter-terrorism activities: a method based on OWA and fuzzy probabilistic rough sets. IEEE Trans Fuzzy Syst 28:831–845. https://doi.org/10.1109/TFUZZ.2019.2955047

    Article  Google Scholar 

  23. Gelenbe E, Timotheou S, Nicholson D (2010) Fast distributed near-optimum assignment of assets to tasks. Comput J 53(9):1360–1369

    Article  Google Scholar 

  24. Gong D, Han Y, Sun J (2018) A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems. Knowl-Based Syst 148:115–130

    Article  Google Scholar 

  25. Gülpınar N, Çanakoğlu E, Branke J (2018) Heuristics for the stochastic dynamic task-resource allocation problem with retry opportunities. Eur J Oper Res 266(1):291–303

    Article  MathSciNet  MATH  Google Scholar 

  26. Han Y, Gong D, Jin Y, Pan Q (2016) Evolutionary multi-objective blocking lot-streaming flow shop scheduling with interval processing time. Appl Soft Comput 42:229–245

    Article  Google Scholar 

  27. Han Y, Gong D, Sun X (2015) A discrete artificial bee colony algorithm incorporating differential evolution for the flow-shop scheduling problem with blocking. Eng Optim 47(7):927–946

    Article  MathSciNet  Google Scholar 

  28. Han Y, Gong D, Sun XY, Pan Q (2014) An improved NSGA-II algorithm for multi-objective lot-streaming flow shop scheduling problem. Int J Prod Res 52(8):2211–2231

    Article  Google Scholar 

  29. Hocaoğlu MF (2019) Weapon target assignment optimization for land based multi-air defense systems: a goal programming approach. Comput Ind Eng 128:681–689

    Article  Google Scholar 

  30. Hosein PA, Athans M. 1990. Preferential defense strategies. Part ii: the dynamic case. Cambridge (US): MIT Laboratory for information and decision systems. Report no.: LIDS-P 2003.Technical report

  31. Hosein PA. 1989. A class of dynamic nonlinear resource allocation problems. Tech. Rep. LIDS-TH-1922, Massachusetts Inst of tech Cambridge lab for information and decision systems

  32. Huaiping C, Jingxu L, Yingwu C, Hao W (2006) Survey of the research on dynamic weapon-target assignment problem. J Syst Eng Electron 17(3):559–565

    Article  MATH  Google Scholar 

  33. Johansson F, Falkman G (2011) Real-time allocation of firing units to hostile targets. Journal of Advances in Information Fusion 6(2):187–199

    Google Scholar 

  34. Johansson F (2010) Evaluating the performance of TEWA systems [dissertation]. University of Skövde, Skövde

    Google Scholar 

  35. Julstrom BA. 2009. String- and permutation-coded genetic algorithms for the static weapon-target assignment problem. Paper presented at: GECCO-2009. Proceedings of the genetic and evolutionary computation conference; July 08–12; Montreal, Québec, Canada. New York (US): ACM. p. 2553–2558

  36. Kalaiselvi S, Selvi CSK (2020) Hybrid cloud resource provisioning (HCRP) algorithm for optimal resource allocation using MKFCM and bat algorithm. Wirel Pers Commun 111:1171–1185

    Article  Google Scholar 

  37. Kalyanam K, Rathinam S, Casbeer D, Pachter M (2016) Optimal threshold policy for sequential weapon target assignment. IFAC-PapersOnLine. 49(17):7–10

    Article  MathSciNet  Google Scholar 

  38. Karasakal O (2008) Air defense missile-target allocation models for a naval task group. Comput Oper Res 35(6):1759–1770

    Article  MATH  Google Scholar 

  39. Kline A. 2017. Real-time heuristic algorithms for the static weapon-target assignment problem. Master’s thesis, Air Force Institute of Technology

  40. Kline AG, Ahner DK, Hill R (2019) The weapon-target assignment problem. Comput Oper Res 105:226–236

    Article  MathSciNet  MATH  Google Scholar 

  41. Klinkowski M, Lechowicz P, Walkowiak K (2018) Survey of resource allocation schemes and algorithms in spectrally-spatially flexible optical networking. Opt Switch Netw 27:58–78

    Article  Google Scholar 

  42. Kolitz, S. E., 1988. Analysis of a maximum marginal return assignment algorithm. In: 27th IEEE conference on decision and control, 1988. IEEE, pp. 2431–2436

  43. Kong D, Chang T, Wang Q, Sun H, Dai W. 2018.A threat assessment method of group targets based on interval-valued intuitionistic fuzzy multi-attribute group decision-making. Applied soft computing. 67:350–369

  44. Kwon O, Kang D, Lee K, Park S (1999) Lagrangian relaxation approach to the targeting problem. Nav Res Logist 46(6):640–653

    Article  MathSciNet  MATH  Google Scholar 

  45. Laszczyk M, Myszkowski PB (2019) Survey of quality measures for multi-objective optimization: construction of complementary set of multi-objective quality measures. Swarm and Evolutionary Computation 48:109–133

    Article  Google Scholar 

  46. Lee H, Choi BJ, Kim CO, Kim JS, Kim JE (2017) Threat evaluation of enemy air fighters via neural network-based Markov chain modeling. Knowl-Based Syst 116:49–57

    Article  Google Scholar 

  47. Lee MZ (2010) Constrained weapon–target assignment: enhanced very large scale neighborhood search algorithm. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 40(1):198–204

    Article  Google Scholar 

  48. Lee ZJ, Lee CY, Su SF (2002a) An immunity-based ant colony optimization algorithm for solving weapon–target assignment problem. Appl Soft Comput 2(1):39–47

    Article  Google Scholar 

  49. Lee ZJ, Lee CY (2005) A hybrid search algorithm with heuristics for resource allocation problem. Inf Sci 173(1):155–167

    Article  Google Scholar 

  50. Lee ZJ, Su SF, Lee CY (2002b) A genetic algorithm with domain knowledge for weapon-target assignment problems. J Chin Inst Eng 25(3):287–295

    Article  Google Scholar 

  51. Lee ZJ, Su SF, Lee CY (2003) Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 33(1):113–121

    Article  Google Scholar 

  52. Liu B, Zhu Q, Zhu H (2020) Trajectory optimization and resource allocation for UAV-assisted relaying communications. Wirel Netw 26:739–749

    Article  Google Scholar 

  53. Ma K, Liu X, Li G, Hu S, Guan X (2019) Resource allocation for smart grid communication based on a multi-swarm artificial bee colony algorithm with cooperative learning. Eng Appl Artif Intell 81:29–36

    Article  Google Scholar 

  54. Madni AM, Andrecut M (2009) Efficient heuristic approaches to the weapon-target assignment problem. J Aerosp Comput Inf Commun 6:405–414

    Article  Google Scholar 

  55. Manne AS (1958) A Target-Assignment Problem. Oper Res 6(3):346–351

    Article  MathSciNet  MATH  Google Scholar 

  56. Murphey RA. 2000. An approximate algorithm for a weapon target assignment stochastic program. In: Approximation and Complexity in Numerical Optimization. Springer, pp. 406–421

  57. Naseem A, Shah STH, Khan SA, Malik AW (2017) Decision support system for optimum decision making process in threat evaluation and weapon assignment: current status, challenges and future directions. Annu Rev Control 43:169–187

    Article  Google Scholar 

  58. Newman AM, Rosenthal RE, Salmeron J, Brown GG, Price W, Rowe A, Fennemore CF, Taft RL (2011) Optimizing assignment of tomahawk cruise missile missions to firing units. Nav Res Logist 58(3):281–295

    Article  MathSciNet  MATH  Google Scholar 

  59. Pendharkar C (2015) An ant colony optimization heuristic for constrained task allocation problem. Journal of Computational Science 7:37–47

    Article  MathSciNet  Google Scholar 

  60. Ross TJ (2005) Fuzzy logic with engineering application, Second edn. Wiely, Singapore

  61. Rudek R, Heppner L (2020) Efficient algorithms for discrete resource allocation problems under degressively proportional constraints. Expert Syst Appl 149:113293. https://doi.org/10.1016/j.eswa.2020.113293

    Article  Google Scholar 

  62. Runqvist A (2004) Threat evaluation: An application for air surveillance systems. Master’s thesis UPTEC IT04 003. Uppsala University, Sweden

  63. Russo LMS, Francisco AP (2014) Extending quick hypervolume. J Heuristics 22(3):245–271

    Article  Google Scholar 

  64. Schwarzrock J, Zacarias I, Bazzan ALC, Fernandes RQA, Freitas EP (2018) Solving task allocation problem in multi unmanned aerial vehicles systems using swarm intelligence. Eng Appl Artif Intell 72:10–20

    Article  Google Scholar 

  65. Shooli RG, Javidi MM (2020) Using gravitational search algorithm enhanced by fuzzy for resource allocation in cloud computing environments. SN Applied Sciences 2. https://doi.org/10.1007/s42452-020-2014-y

  66. Soland RM (1987) Optimal terminal defense tactics when several sequential engagements are possible. Oper Res 35(4):537–542

    Article  Google Scholar 

  67. Taner Gulez. Weapon-Target Allocation and Scheduling Air Defense with Time Varying Hit Probabilities. Master’s thesis, The Middle East technical university, Turkey, 2007

  68. Tokgoz A, Bulkan S (2013) Weapon target assignment with combinatorial optimization techniques. International Journal of Advanced Research in Artificial Intelligence 2(7):39–50

    Article  Google Scholar 

  69. Turan A. Algorithms for the weapon-target allocation problem. Master’s thesis, The Middle East technical university, Turkey, 2012

  70. Veldhuizen VD. 1999. Multiobjective evolutionary algorithms: classifications, analyzes, and new innovations. Dayton, OH: air force Inst. Technol. Report no.: AFIT/DS/ENG/99-01. Technical report

  71. Wacholder E (1989) A neural network-based optimization algorithm for the static weapon-target assignment problem. ORSA J Comput 1(4):232–246

    Article  MATH  Google Scholar 

  72. Wang J, Hu X, Demeulemeester E, Zhao Y (2019) A bi-objective robust resource allocation model for the RCPSP considering resource transfer costs. Int J Prod Res:1–21. https://doi.org/10.1080/00207543.2019.1695168

  73. Xin B, Chen J, Peng Z, Dou L, Zhang J (2011) An efficient rule-based constructive heuristic to solve dynamic weapon-target assignment problem. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans. 41(3):598–606

    Article  Google Scholar 

  74. Xin B, Chen J, Zhang J, Dou L, Peng Z (2010) Efficient decision makings for dynamic weapon-target assignment by virtual permutation and tabu search heuristics. IEEE Trans Syst Man Cybern Part C Appl Rev 40(6):649–662

    Article  Google Scholar 

  75. Xu X, Hao J, Zheng Y (2020) Multi-objective artificial bee colony algorithm for multi-stage resource leveling problem in sharing logistics network. Comput Ind Eng 142:106338. https://doi.org/10.1016/j.cie.2020.106338

    Article  Google Scholar 

  76. Yari G, Chaji AR (2012a) Determination of ordered weighted averaging operator weights based on the M-entropy measures. Int J Intell Syst 27:1020–1033

    Article  Google Scholar 

  77. Yari G, Chaji AR (2012b) Maximum Bayesian entropy method for determining ordered weighted averaging operator weights. Comput Ind Eng 63(1):338–342

    Article  MATH  Google Scholar 

  78. Yin PY, Wang JY (2006) A particle swarm optimization approach to the nonlinear resource allocation problem. Appl Math Comput 183(1):232–242

    MathSciNet  MATH  Google Scholar 

  79. Yuming LU, Weiqiang M, Ming LI (2013) The air defense missile optimum target assignment based on the improved genetic algorithm. J Theor Appl Inf Technol 48(2):809–816

    Google Scholar 

  80. Zhang J, Zhuang J (2019) Modeling a multi-target attacker-defender game with multiple attack types. Reliability Engineering & System Safety 185:465–475

    Article  Google Scholar 

  81. Zhu Z, Peng J, Liu K, Zhang X (2020) A game-based resource pricing and allocation mechanism for profit maximization in cloud computing. Soft Comput 24:4191–4203

    Article  Google Scholar 

  82. Zitzler E, Laumanns M, Thiele L. 2001. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Paper presented at: EUROGEN 2001. Proceedings of the evolutionary methods for design, optimization and control with applications to industrial problems; Athens, Greece, p.95–100

  83. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 3(4):257–271

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Abdolreza Asadi Ghanbari

  2. Faculty of Entrepreneurship, University of Tehran, 14155-6619, Tehran, Iran

    Hossein Alaei

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  1. Abdolreza Asadi Ghanbari
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Ghanbari, A.A., Alaei, H. Meta-heuristic algorithms for resource Management in Crisis Based on OWA approach. Appl Intell 51, 646–657 (2021). https://doi.org/10.1007/s10489-020-01808-y

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