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Critical objective function values in linear sum assignment problems

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Abstract

The linear sum assignment problem has been well studied in combinatorial optimization. Because of the integrality property, it is a linear programming problem with a variety of efficient algorithms to solve it. In the given research, we present a reformulation of the linear sum assignment problem and a Lagrangian relaxation algorithm for its reformulation. An important characteristic of the new Lagrangian relaxation method is that the optimal Lagrangian multiplier yields a critical bottleneck value. Lagrangian relaxation has only one Lagrangian multiplier, which can only take on a limited number of values, making the search for the optimal multiplier easy. The interpretation of the optimal Lagrangian parameter is that its value is equal to the price that must be paid for all objects in the problem to be assigned.

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References

  • Akgul M (1993) A genuinely polynomial primal simplex algorithm for the assignment problem. Discrete Appl Math 45(2):93–115

    Article  MathSciNet  MATH  Google Scholar 

  • Balinski ML, Gomory RE (1964) A primal method for the assignment and transportation problems. Manage Sci 10(3):578–593

    Article  Google Scholar 

  • Balinski ML (1985) Signature methods for the assignment problem. Oper Res 33(3):527–536

    Article  MathSciNet  MATH  Google Scholar 

  • Beasley JE (2016) OR-library: assignment problem. Resource document. OR-library

  • Bertsekas DP (1988) The auction algorithm: a distributed relaxation method for the assignment problem. Ann Oper Res 14(1):105–123

    Article  MathSciNet  MATH  Google Scholar 

  • Bertsekas DP, Eckstein J (1988) Dual coordinate step methods for linear network flow problems. Math Program 42(1–3):203–243

    Article  MathSciNet  MATH  Google Scholar 

  • Bokhari SH (2012) Assignment problems in parallel and distributed computing, vol 32. Springer, New York

    Google Scholar 

  • Burkard RE, Dell’Amico M, Martello S (2009) Assignment problems. Siam, Bangkok

    Book  MATH  Google Scholar 

  • Chen CP, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf Sci 275:314–347

    Article  Google Scholar 

  • Cunningham WH (1976) A network simplex method. Math Program 11(1):105–116

    Article  MathSciNet  MATH  Google Scholar 

  • Dantzig G (2016) Linear programming and extensions. Princeton University Press, Princeton

    MATH  Google Scholar 

  • Dinic EA, Kronrod MA (1969) An algorithm for the solution of the assignment problem. Soviet Math Dokl 10(6):1324–1326

    MATH  Google Scholar 

  • Easterfield TE (1946) A combinatorial algorithm. J Lond Math Soc 1(3):219–226

    Article  MathSciNet  MATH  Google Scholar 

  • Edmonds J, Karp RM (1972) Theoretical improvements in algorithmic efficiency for network flow problems. J ACM 19(2):248–264

    Article  MATH  Google Scholar 

  • Fox GC, Williams RD, Messina GC (2014) Parallel computing works!. Morgan Kaufmann, Burlington

    Google Scholar 

  • Garbow HN (1985) Scaling algorithms for network problems. J Comput Syst Sci 31(2):148–168

    Article  MathSciNet  Google Scholar 

  • Hung MS, Rom WO (1980) Solving the assignment problem by relaxation. Oper Res 28(4):969–982

    Article  MathSciNet  MATH  Google Scholar 

  • Kao YH, Krishnamachari B, Ra MR, Bai F (2017) Hermes: latency optimal task assignment for resource-constrained mobile computing. IEEE Trans Mobile Comput 16(11):3056–3069. https://doi.org/10.1109/TMC.2017.2679712

  • Koopmans TC, Beckmann M (1957) Assignment problems and the location of economic activities. Econom J Econom Soci 25(1):53–76. https://doi.org/10.2307/1907742

  • Kuhn HW (1955) The Hungarian method for the assignment problem. Naval Res Logist Q 2(12):83–97

    Article  MathSciNet  MATH  Google Scholar 

  • Kuhn HW (1956) Variants of the Hungarian method for assignment problems. Naval Res Logist Q 3(4):253–258

    Article  MathSciNet  MATH  Google Scholar 

  • Liu YY, Wang S (2015) A scalable parallel genetic algorithm for the generalized assignment problem. Parallel Comput 46:98–119

    Article  MathSciNet  Google Scholar 

  • Luo L, Chakraborty N, Sycara K (2012) A distributed algorithm for constrained multi-robot task assignment for grouped tasks. Research Showcase@CMU. Carnegie Mellon University, Pittsburgh

    Google Scholar 

  • Munkres J (1957) Algorithms for the assignment and transportation problems. J Soc Ind Appl Math 5(1):32–38

    Article  MathSciNet  MATH  Google Scholar 

  • Papagianni C, Leivadeas A, Papavassiliou S, Maglaris V, Cervello-Pastor C, Monje A (2013) On the optimal allocation of virtual resources in cloud computing networks. IEEE Trans Comput 62(6):1060–1071

    Article  MathSciNet  MATH  Google Scholar 

  • Ritzinger U, Puchinger J, Hartl RF (2016) A survey on dynamic and stochastic vehicle routing problems. Int J Prod Res 54(1):215–231

    Article  MATH  Google Scholar 

  • Roverso R, Naiem A, El-Beltagi M, El-Ansary S, Haridi S (2010) A GPU-enabled solver for time-constrained linear sum assignment problems. In: The 7th international conference on informatics and systems. IEEE, pp 1–6

  • Seow KT, Dang NH, Lee DH (2007) Towards an automated multiagent taxi-dispatch system. In: 2007 IEEE international conference on automation science and engineering. IEEE, pp 1045–1050

  • Thorndike RL (1950) The problem of classification of personnel. Psychometrika 15(3):215–235

    Article  Google Scholar 

  • Tomizawa N (1971) On some techniques useful for solution of transportation network problems. Networks 1(2):173–194

    Article  MathSciNet  MATH  Google Scholar 

  • Zavlanos MM, Spesivtsev L, Pappas GJ (2008) A distributed auction algorithm for the assignment problem. In: The 47th IEEE conference on decision and control. IEEE, pp 1212–1217

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Correspondence to Ivan Belik.

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Belik, I., Jornsten, K. Critical objective function values in linear sum assignment problems. J Comb Optim 35, 842–852 (2018). https://doi.org/10.1007/s10878-017-0240-z

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