Skip to main content

An Evolutionary Simulating Annealing Algorithm for Google Machine Reassignment Problem

Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO,volume 8)


Google Machine Reassignment Problem (GMRP) is a real world problem proposed at ROADEF/EURO challenge 2012 competition which must be solved within 5 min. GMRP consists in reassigning a set of services into a set of machines for which the aim is to improve the machine usage while satisfying numerous constraints. This paper proposes an evolutionary simulating annealing (ESA) algorithm for solving this problem. Simulating annealing (SA) is a single solution based heuristic, which has been successfully used in various optimisation problems. The proposed ESA uses a population of solutions instead of a single solution. Each solution has its own SA algorithm and all SAs work in parallel manner. Each SA starts with different initial solution which can lead to a different search path with distinct local optima. In addition, mutation operators are applied once the solution cannot be improved for a certain number of iterations. This will not only help the search avoid being trapped in a local optima, but also reduce computation time. Because new solutions are not generated from scratch but based on existing ones. This study shows that the proposed ESA method can outperform state of the art algorithms on GMRP.


  • Machine Reassignment Problem
  • Simulating annealing
  • Cloud computing
  • Evolutionary algorithm

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-49049-6_31
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   229.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-49049-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   299.99
Price excludes VAT (USA)
Hardcover Book
USD   299.99
Price excludes VAT (USA)
Fig. 1


  1. Michael Armbrust, Armando Fox, Rean Griffith, Anthony D Joseph, Randy Katz, Andy Konwinski, Gunho Lee, David Patterson, Ariel Rabkin, Ion Stoica, et al. A view of cloud computing. Communications of the ACM, 53(4):50–58, 2010.

    CrossRef  Google Scholar 

  2. Rodrigo N Calheiros, Rajiv Ranjan, Anton Beloglazov, César AF De Rose, and Rajkumar Buyya. Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1):23–50, 2011.

    Google Scholar 

  3. Roadef/euro challenge 2012: Machine reassignment.

  4. Marcus Rolf Peter Ritt. An Algorithmic Study of the Machine Reassignment Problem. PhD thesis, UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL, 2012.

    Google Scholar 

  5. Haris Gavranović, Mirsad Buljubašić, and Emir Demirović. Variable neighborhood search for google machine reassignment problem. Electronic Notes in Discrete Mathematics, 39:209–216, 2012.

    CrossRef  MATH  Google Scholar 

  6. Deepak Mehta, Barry O’Sullivan, and Helmut Simonis. Comparing solution methods for the machine reassignment problem. In Principles and practice of constraint programming, pages 782–797. Springer, 2012.

    Google Scholar 

  7. Felix Brandt, Jochen Speck, and Markus Völker. Constraint-based large neighborhood search for machine reassignment. Annals of Operations Research, pages 1–29, 2012.

    Google Scholar 

  8. Renaud Masson, Thibaut Vidal, Julien Michallet, Puca Huachi Vaz Penna, Vinicius Petrucci, Anand Subramanian, and Hugues Dubedout. An iterated local search heuristic for multi-capacity bin packing and machine reassignment problems. Expert Systems with Applications, 40(13):5266–5275, 2013.

    CrossRef  Google Scholar 

  9. Ramon Lopes, Vinicius WC Morais, Thiago F Noronha, and Vitor AA Souza. Heuristics and matheuristics for a real-life machine reassignment problem. International Transactions in Operational Research, 22(1):77–95, 2015.

    MathSciNet  CrossRef  Google Scholar 

  10. Scott Kirkpatrick, C Daniel Gelatt, Mario P Vecchi, et al. Optimization by simulated annealing. Science, 220(4598):671–680, 1983.

    Google Scholar 

  11. M Emin Aydin and Terence C Fogarty. A distributed evolutionary simulated annealing algorithm for combinatorial optimisation problems. Journal of Heuristics, 10(3):269–292, 2004.

    CrossRef  Google Scholar 

  12. Salvador García, Alberto Fernández, Julián Luengo, and Francisco Herrera. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences, 180(10):2044–2064, 2010.

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Ayad Turky .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Turky, A., Sabar, N.R., Song, A. (2017). An Evolutionary Simulating Annealing Algorithm for Google Machine Reassignment Problem. In: Leu, G., Singh, H., Elsayed, S. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 8. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49048-9

  • Online ISBN: 978-3-319-49049-6

  • eBook Packages: EngineeringEngineering (R0)