Soft Computing

, Volume 23, Issue 22, pp 11557–11572 | Cite as

Uncertain multi-objective Chinese postman problem

  • Saibal Majumder
  • Samarjit KarEmail author
  • Tandra Pal
Methodologies and Application


Chinese postman problem is one of the significant combinatorial optimization problems with a wide range of real-world applications. Modelling such real-world applications quite often needs to consider some uncertain factors for which the belief degrees of the experts are essential. Liu (Uncertainty Theory, 2nd edn. Springer, Berlin, 2007) proposed uncertainty theory to model such human beliefs. This paper presents a multi-objective Chinese postman problem under the framework of uncertainty theory. The objectives of the problem are to maximize the total profit earned and to minimize the total travel time of the tour of a postman. Here, we have proposed an expected value model (EVM) for the uncertain multi-objective Chinese postman problem (UMCPP). The deterministic transformation of the corresponding EVM is done by computing the expected value of the uncertain variable using 999-method for which we have proposed an algorithm, 999-expected value model-uncertain multi-objective Chinese postman problem. Subsequently, the model is solved by two classical multi-objective solution techniques, namely global criterion method and fuzzy programming method. Two multi-objective genetic algorithms (MOGAs): nondominated sorting genetic algorithm II and multi-objective cross-generational elitist selection, heterogeneous recombination and cataclysmic mutation are also used to solve the model. A numerical example is presented to illustrate the proposed model. Finally, the performance of MOGAs is compared on six randomly generated instances of UMCPP.


Uncertain multi-objective Chinese postman problem Expected value model 999-Method NSGA-II MOCHC 



The authors are very much thankful to the Editor and the anonymous referees for their constructive and valuable suggestions to enhance the quality of the manuscript. Moreover, Saibal Majumder, an INSPIRE fellow (No.: DST/INSPIRE Fellowship/2015/IF150410) is indebted to the Department of Science & Technology (DST), Ministry of Science and Technology, Government of India, for providing him financial assistance for the work.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent is obtained from all individual participants included in the study.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceNational Institute of Technology DurgapurDurgapurIndia
  2. 2.Department of MathematicsNational Institute of Technology DurgapurDurgapurIndia

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