Skip to main content
SpringerLink
Log in

Multimethodology in Metaheuristics

  • General Paper
  • Published:
Journal of the Operational Research Society

Abstract

As a combination of different methodologies or parts of methodologies, Multimethodology is becoming more frequent in OR practice. This paper contributes with a new proposal and a new field of application: the employment of Multimethodology in problem solving with Metaheuristics (Mh). A convenient selection of soft and hard methods will be considered, from Soft OR, Creativity and Metaheuristics, such as Strategic Choice Approach, SWOT Analysis and Divergent and Convergent thinking. Formulating the ‘right’ optimisation problem, choosing a method based on Mh and accomplishing an effective implementation is an imprecise decision-making process, which may require skills and ideas that are beyond the ordinary boundaries of Mh practice. The relevance and success of Mh have been well-known for decades, but some open questions concerning choice and implementation strategies, for instance, still remain. If these questions are not adequately answered, they may lose credibility in the long term. The quality of solutions and computational times are not the only criteria used to analyse Mh, nor are they the most important. Very often, the effectiveness of an approach has to be evaluated from the perspective of modelling and practical problem solving. This paper investigates the advantages of Multimethodology and, furthermore, it sketches a framework for a coherent and comprehensive comparison of Mh and recommends a dynamic guiding tool for their implementation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 10
Figure 9
Figure 11

Similar content being viewed by others

References

  • Ackoff R (1978). The Art of Problem Solving. Wiley: New York.

    Google Scholar 

  • Ackoff R (1987). OR: A post mortem. Operations Research 35 (3): 471–474.

    Article  Google Scholar 

  • Ackoff R and Vergara E (1981). Creativity in problem solving and planning: A review. European Journal of Operational Research 7 (1): 1–13.

    Article  Google Scholar 

  • Basadur M, Ellspermann S and Evans G (1994). A new methodology for formulating ill-structured problems. Omega 22 (6): 627–645.

    Article  Google Scholar 

  • Bell G, Warwick J and Kennedy M (2009). From theory to practice and back: Some experiences with an emerging multimethodology. OR Insight 22 (2): 65–87.

    Article  Google Scholar 

  • Blum C (Guest Editor) (2010). Hybrid metaheuristics. Computers & Operations Research 37 (3): 429–610

    Article  Google Scholar 

  • Blum C, Aguilera B, Roli M and Sampels M (eds). (2008). Hybrid Metaheuristics—An Emerging Approach to Optimization. Studies in Computational Intelligence. Vol. 114. Springer: New York.

    Google Scholar 

  • Boschetti M, Maniezzo V, Roffilli M and Röhler A (2009). Matheuristics: Optimization, simulation and control. In: Proceedings of HM 2009. LNCS 5818, Springer-Verlag: New York, pp 171–177.

  • Burke E, Hart E, Kendall G, Newall J, Ross P and Schulenburg S (2003). Hyper-heuristics: An emerging direction in modern search technology. In: Glover F and Kochenberger G (eds). Handbook of Metaheuristics. Kluwer: Dordrecht, MA, pp 457–474.

    Chapter  Google Scholar 

  • Corberan A and Prins C (2010). Recent results on arc routing problems: An annotated bibliography. Networks 56 (1): 50–69.

    Google Scholar 

  • Corne D, et al (eds). (1999). New Ideas in Optimization. McGraw-Hill: New York.

    Google Scholar 

  • De Bono E (1992). Serious creativity: Using the Power of Lateral Thinking to Create New Ideas. HarperCollins: New York.

    Google Scholar 

  • Dréo J, Pétrowski A, Siarry P and Taillard E (2006). Metaheuristics for Hard Optimization. Springer: New York.

    Google Scholar 

  • Driscoll D, Appiah-Yeboah A, Salib P and Rupert D (2007). Merging qualitative and quantitative data in mixed methods research: How to and why not. Ecological and Environmental Anthropology 3 (1): 19–28.

    Google Scholar 

  • Eden C, Ackermann F, Bryson J, Richardson GD, Andersen D and Finn C (2009). Integrating modes of policy analysis and strategic management practice: Requisite elements and dilemmas. Journal of the Operational Research Society 60(1): 2–13.

    Article  Google Scholar 

  • Ferreira JS (2008). Creativity in Optimization—Some Ideas. IV SELASI—European Latin American Workshop on Engineering Systems, 1–5 December, Havana, Cuba.

  • Frederickson G (1979). Approximation algorithms for some postman problems. Journal of the Association for Computing Machinery 26 (3): 538–554.

    Article  Google Scholar 

  • Friend J (2001). The strategic choice approach. In: Rosenhead J and Mingers J (eds). Rational Analysis for a Problematic World Revisited. John Wiley: New York, pp 115–150.

    Google Scholar 

  • Friend J and Hickling A (2005). Planning Under Pressure: The Strategic Choice Approach. Elsevier: Amsterdam.

    Google Scholar 

  • Ghiani G, Laganà D and Musmanno R (2006). A constructive heuristic for the undirected rural postman problem. Computers & Operations Research 33 (12): 3450–3457.

    Article  Google Scholar 

  • Glass R (2006). Software Creativity 2.0. developer.* Books: Atlanta, GA.

    Google Scholar 

  • Glover F and Kochenberger G (eds). (2003). Handbook of Metaheuristics. Kluwer: Amsterdam.

    Book  Google Scholar 

  • Glover F and Laguna M (1977). Tabu Search. Kluwer Academic Press: Dordrecht, MA.

    Google Scholar 

  • Gonzalez T (eds). (2007). Handbook of Approximation Algorithms and Metaheuristics. Chapman: London.

    Book  Google Scholar 

  • Gordon W (1961). Synetics: The Development of Creative Capacity. Harper & Row: New York.

    Google Scholar 

  • Guilford J (1967). The Nature of Human Intelligence. McGraw-Hill: New York.

    Google Scholar 

  • Herrmann N (1996). The Whole Brain Business Book. McGraw-Hill: New York.

    Google Scholar 

  • Hertz A, Laporte G and Hugo P (1999). Improvement procedures for the undirected rural postman problem. INFORMS Journal on Computing 11 (1): 53–62.

    Article  Google Scholar 

  • Hooker J (1995). Testing heuristics: We have it all wrong. Journal of Heuristics 1 (1): 33–42.

    Article  Google Scholar 

  • Hoos H and Stultzle T (2005). Stochastic Local Search: Foundations and Applications. Elsevier: Amsterdam.

    Google Scholar 

  • Howick S and Ackermann F (2011). Mixing OR methods in practice: Past, present and future directions. European Journal of Operational Research 215 (3): 503–511.

    Article  Google Scholar 

  • Johnson R and Onwuegbuzie A (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher 33 (7): 14–26.

    Article  Google Scholar 

  • Joldersma C and Roelofs E (2004). The impact of soft OR-methods on problem structuring. European Journal of Operational Research 152 (3): 696–708.

    Article  Google Scholar 

  • Keys P (2006). On becoming expert in the use of problem structuring methods. Journal of the Operational Research Society 57 (7): 822–829.

    Article  Google Scholar 

  • Kotiadis K and Mingers J (2006). Combining PSMs with hard OR methods: The philosophical and practical challenges. Journal of the Operational Research Society 57 (7): 856–867.

    Article  Google Scholar 

  • Laganà D, Laporte G, Mari F, Musmanno R and Pisacane O (2007). An ant colony optimization metaheuristic for the undirected rural postman problem. Les Cahiers du GERAD, G-2007-106.

  • Lozano M and García-Martínez C (2010). Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: Overview and progress report. Computers & Operations Research 37 (3): 481–497.

    Article  Google Scholar 

  • Mabin V, Davies J and Kim S (2009). Rethinking tradeoffs and OR/MS methodology. Journal of the Operational Research Society 60 (10): 1384–1395.

    Article  Google Scholar 

  • Maniezzo V, Stützle T and Voß S (eds). (2010). Matheuristics - Hybridizing Metaheuristics and Mathematical Programming. Series: Annals of Information Systems, Vol. 10. Springer: New York.

    Google Scholar 

  • Marakas G and Elam J (1997). Creativity enhancement in problem solving: Through software or process? Management Science 43 (8): 1136–1146.

    Article  Google Scholar 

  • Mark M, Pedroso J and Ferreira JS (2010a). New methods to solve the graph partitioning problem. EURO XXIV, July 11–14, 2010, Lisboa, Portugal.

  • Mark M, Pedroso J and Ferreira JS (2010b). The graph partitioning problem—Comparing solution methods. FEUP/INESC TEC.

  • Mingers J (2000). Variety is the spice of life—Combining soft and hard OR/MS methods. International Transactions in Operations Research 7 (6): 673–691.

    Article  Google Scholar 

  • Mingers J (2001). Multimethodology—Mixing and matching methods. In: Rosenhead J and Mingers J (eds). Rational Analysis for a Problematic World Revisited. John Wiley and Sons: Chichester, pp 289–309.

    Google Scholar 

  • Mingers J and Brocklesby J (1997). Multimethodology: Towards a framework for mixing methodologies. OMEGA 25 (5): 489–509.

    Article  Google Scholar 

  • Mingers J and Gill A (eds). (1997). Multimethodology: Theory and Practice of Combining Management Science Methodologies. Wiley: Chichester.

    Google Scholar 

  • Mingers J, Liu W and Meng W (2009). Using SSM to structure the identification of inputs and outputs in DEA. Journal of the Operational Research Society 60 (2): 168–179.

    Article  Google Scholar 

  • Munro I and Mingers J (2002). The use of multimethodology in practice—Results of a survey of practitioners. Journal of the Operational Research Society 53 (4): 369–378.

    Article  Google Scholar 

  • Ormerod R (2001). Mixing methods in practice. In: Rosenhead J and Mingers J (eds). Rational Analysis for a Problematic World Revisited: Problem Structuring Methods for Complexity, Uncertainty and Conflict. Wiley: Chichester, pp 289–310.

    Google Scholar 

  • Osborn A (1953). Applied Imagination. Charles Scribner's & Sons: New York.

    Google Scholar 

  • Panagiotou G (2003). Bringing SWOT into focus. Business Strategy Review 14 (2): 8–10.

    Article  Google Scholar 

  • Papamichail K, Alves G, French S, Yang J and Snowdon R (2007). Facilitation practices in decision workshops. Journal of the Operational Research Society 58 (5): 614–632.

    Article  Google Scholar 

  • Parnes S (1997). Optimize: The Magic of Your Mind. Bearly Limited: Buffalo, NY.

    Google Scholar 

  • Paucar-Caceres A (2009). Mapping the changes in management science: A review of ‘soft’ OR/MS articles published in Omega (1973–2008). Omega 38 (1–2): 46–56.

    Google Scholar 

  • Pidd M (1996). Tools for Thinking. Wiley: New York.

    Google Scholar 

  • Pollack J (2009). Multimethodology in series and parallel: Strategic planning using hard and soft OR. Journal of the Operational Research Society 60 (2): 156–167.

    Article  Google Scholar 

  • Reeves C (eds). (1995). Modern Heuristic Techniques for Combinatorial Problems. McGraw-Hill: New York.

    Google Scholar 

  • Rodrigues A and Ferreira JS (2010). Rural postman and related arc routing problems. EURO XXIV, July 11–14, 2010, Lisboa, Portugal.

  • Rodrigues A and Ferreira JS (2012). Cutting path as a rural postman problem: Solutions by memetic algorithms. International Journal of Combinatorial Optimization Problems and Informatics 3 (1): 22–37.

    Google Scholar 

  • Rosenhead J and Mingers J (eds). (2001). Rational Analysis for a problematic World Revisited. John Wiley: New York.

    Google Scholar 

  • Rubinstein M (1986). Tools for Thinking and Problem Solving. Prentice-Hall: Englewood Cliffs, NJ.

    Google Scholar 

  • Runco M (2007). Creativity. Elsevier: Amsterdam.

    Google Scholar 

  • Schulz P (2006). Creative design in optimization—Metaheuristics design to multi-modal continuous functions. Technical University of Denmark, IMM-M.Sc, 2006–2029.

  • Smith-Miles K and Lopes L (2012). Measuring instance difficulty for combinatorial optimization problems. Computers & Operations Research 39 (5): 875–889.

    Article  Google Scholar 

  • Soares G, Rodrigues A and Ferreira JS (2008). Facilitating to deal with combinatorial optimisation problems: Report of a workshop (in Portuguese). INESC TEC.

  • Sodhi M and Tang C (2008). The OR/MS ecosystem: Strengths, weaknesses, opportunities, and threats. Operations Research 56 (2): 267–277.

    Article  Google Scholar 

  • Talbi E (2009). Metaheuristics: From Design to Implementation. Wiley: New York.

    Book  Google Scholar 

  • Teddlie C and Tashakkori A (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage Publications: London.

    Google Scholar 

  • Vidal V (Guest Editor). (2004). European Journal of Operational Research 152 (3): 529–806.

  • Vidal V (2004). Applications of Soft O.R. Methods. Creativity for operational researchers. Investigação Operacional 25 (1): 1–24.

    Google Scholar 

  • Vidal V (2006). Creative and Participative Problem Solving—The Art and the Science, http://www2.imm.dtu.dk/~vvv/CPPS/index.htm.

  • Weihrich H (1982). The TOWS matrix—A tool for situational analysis. Long Range Planning 15 (2): 54–66.

    Article  Google Scholar 

  • White L (2009). Understanding problem structuring methods interventions. European Journal of Operational Research 199 (3): 823–833.

    Article  Google Scholar 

  • Winterfeldt D and Fasolo B (2009). Structuring decision problems: A case study and reflections for practitioners. European Journal of Operational Research 199 (3): 857–866.

    Article  Google Scholar 

  • Zäpfel G, Braume R and Bögl M (2010). Metaheuristic Search Concepts. Springer: New York.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculdade de Engenharia da Universidade do Porto, Porto, Portugal

    J Soeiro Ferreira

  2. INESC TEC—Instituto de Engenharia de Sistemas e Computadores, Porto, Portugal

    J Soeiro Ferreira

Authors
  1. J Soeiro Ferreira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J Soeiro Ferreira.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ferreira, J. Multimethodology in Metaheuristics. J Oper Res Soc 64, 873–883 (2013). https://doi.org/10.1057/jors.2012.88

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jors.2012.88

Keywords

Search

Navigation