Journal of the Operational Research Society

, Volume 67, Issue 4, pp 537–550 | Cite as

A hybrid collaborative algorithm to solve an integrated wood transportation and paper pulp production problem

  • Jose Eduardo PecoraJunior
  • Angel Ruiz
  • Patrick Soriano
General Paper


This paper proposes a hybrid algorithm to tackle a real-world problem arising in the context of pulp and paper production. This situation is modelled as a production problem where one has to decide which wood will be used by each available processing unit (wood cooker) in order to minimize the variance of wood densities within each cooker for each period of the planning horizon. The proposed hybrid algorithm is built around two distinct phases. The first phase uses two interacting heuristic methods to identify a promising reduced search space, which is then thoroughly explored in the second phase. This hybrid algorithm produces high-quality solutions in reasonable computation times, especially for the largest test instances. Extensive computational experiments demonstrated the robustness and efficiency of the method.


linear programming hybrid algorithms variance minimization heuristics paper pulp production 



This research was partially supported by grants [OPG 0293307 and OPG 0177174] from the Canadian Natural Sciences and Engineering Research Council (NSERC) and grant [2671/04-2] from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)—Brazil. Their support is gratefully acknowledged.


  1. Antonio Parejo J, Ruiz-Cortes A, Lozano S and Fernandez P (2012). Metaheuristic optimization frameworks: A survey and benchmarking. Soft Computing 16 (3, SI): 527–561.CrossRefGoogle Scholar
  2. Biermann CJ (1996). Handbook of Pulping and Papermaking. 2nd edn Academic Press: San Diego, CA.Google Scholar
  3. Blum C, Puchinger J, Raidl GR and Roli A (2011). Hybrid metaheuristics in combinatorial optimization: A survey. Applied Soft Computing 11 (6): 4135–4151.CrossRefGoogle Scholar
  4. Bredström D, Lundgren JT, Rönnqvist M, Carlsson D and Mason A (2004). Supply chain optimization in the pulp mill industry—IP models, column generation and novel constraint branches. European Journal of Operational Research 156 (1): 2–22.CrossRefGoogle Scholar
  5. Bredström D and Rönnqvist M (2008). Combined vehicle routing and scheduling with temporal precedence and synchronization constraints. European Journal of Operational Research 191 (1): 19–31.CrossRefGoogle Scholar
  6. Correa AI, Langevin A and Rousseau LM (2004). Dispatching and conflict-free routing of automated guided vehicles: A hybrid approach combining constraint programming and mixed integer programming. In: van Hentenryck P and Milano M (eds). Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. Lecture Notes in Computer Science, Vol. 3011. Springer-Verlag Berlin: Berlin, pp 370–379.CrossRefGoogle Scholar
  7. D’Amours S, Rönnqvist M and Weintraub A (2008). Using operational research for supply chain planning in the forest products industry. INFOR 46 (4, Part 2, SI): 265–281.Google Scholar
  8. De Lima MP et al (2011). Methodology for planning log stacking using geotechnology and operations research. CERNE 17 (3): 309–319.CrossRefGoogle Scholar
  9. El Hachemi N, Gendreau M and Rousseau L-M (2011). A hybrid constraint programming approach to the log-truck scheduling problem. Annals of Operations Research, 184(1):163–178. 5th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, Paris, France, 20–23 May 2008.Google Scholar
  10. Fisher WD (1958). On grouping for maximum homogeneity. Journal of American Statistical Association 53 (284): 789–798.CrossRefGoogle Scholar
  11. Foelkel CEB, Mora E and Menochelli S (1992). Densidade básica: sua verdadeira utilidade como índice de qualidade da madeira de eucalipto para produção de celulose. O Papel 53 (5): 35–40.Google Scholar
  12. Gallardo J, Cotta C and Fernandez A (2007). On the hybridization of memetic algorithms with branch-and-bound techniques. IEEE Transactions On Systems Man and Cybernetics Part B—Cybernetics 37 (1): 77–83.CrossRefGoogle Scholar
  13. Hooker JN (2006). An integrated method for planning and scheduling to minimize tardiness. Constraints 11 (2–3): 139–157.CrossRefGoogle Scholar
  14. Jourdan L, Basseur M and Talbi EG (2009). Hybridizing exact methods and metaheuristics: A taxonomy. European Journal of Operational Research 199 (3): 620–629.CrossRefGoogle Scholar
  15. Karlsson J, Ronnqvist M and Bergstrom J (2003). Short-term harvest planning including scheduling of harvest crews. International Transactions in Operational Research 10 (5): 413–431.CrossRefGoogle Scholar
  16. Kennedy JF, Philips G and Williams P (eds) (1989). Wood Processing and Utilization. Ellis Horwood Limited: Chichester, West Sussex, UK.Google Scholar
  17. Martello S and Toth P (1990). Knapsack Problems: Algorithms and Computer Implementations. John Wiley & Sons: New York.Google Scholar
  18. Moura AV and Scaraficci RA (2008). Hybrid heuristic strategies for planning and scheduling forest harvest and transportation activities. In Proceedings of 11th IEEE International Conference on Computational Science and Engineering, São Paulo, Brazil, 16–18 July 2008, pp 447–454.Google Scholar
  19. Pécora JE, Ruiz A and Soriano P (2007). Minimization of the wood density variation in pulp and paper production. INFOR 45 (4): 187–196.Google Scholar
  20. Pendharkar PC (2005). Hybrid approaches for classification under information acquisition cost constraint. Decision Support Systems 41 (1): 228–241.CrossRefGoogle Scholar
  21. Peng J, Shang G and Liu H (2006). A hybrid intelligent algorithm for vehicle routing models with fuzzy travel times. In: Computational Intelligence: International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August 16–19, 2006, Lecture Notes in Computer Science, Springer, Berlin Heidelberg.Google Scholar
  22. Puchinger J and Raidl GR (2005). Combining metaheuristics and exact algorithms in combinatorial optimization: A survey and classification. In Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach, Pt 2, Proceedings, volume 3562 of Lecture Notes in Computer Science, Springer-Verlag, Berlin, pp 41–53.Google Scholar
  23. Rönnqvist M (2003). Optimization in forestry. Mathematical Programming 97 (1–2): 267–284.Google Scholar
  24. Sahoo B and Maity D (2007). Damage assessment of structures using hybrid neuro-genetic algorithm. Applied Soft Computing 7 (1): 89–104.CrossRefGoogle Scholar
  25. Santa-Eulalia LA, Ait-Kadi D, D’Amours S, Frayret JM and Lemieux S (2011). Agent-based experimental investigations of the robustness of tactical planning and control policies in a softwood lumber supply chain. Production Planning & Control 22 (8, SI): 782–799.CrossRefGoogle Scholar
  26. Talbi EG (2002). A taxonomy of hybrid metaheuristics. Journal of Heuristics 8 (5): 541–564.CrossRefGoogle Scholar
  27. Williams MF (1994). Matching wood fiber characteristics to pulp and paper processes and products. Tappi Journal 77 (3): 227–233.Google Scholar
  28. Yan S and Zhou K (2006). Three-tier multi-agent approach for solving traveling salesman problem. PRICAI 2006: Trends in Artificial Intelligence, Proceedings. Lecture Notes in Computer Science, Vol. 4099, Springer, Berlin Heidelberg, pp 813–817.Google Scholar

Copyright information

© Operational Research Society Ltd. 2015

Authors and Affiliations

  • Jose Eduardo PecoraJunior
    • 1
    • 4
  • Angel Ruiz
    • 2
    • 4
  • Patrick Soriano
    • 3
    • 4
  1. 1.Universidade Federal do ParanáBrazil
  2. 2.Université LavalCanada
  3. 3.HÉC MontrealCanada
  4. 4.Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT)

Personalised recommendations