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Separation, dimension, and facet algorithms for node flow polyhedra

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Abstract

Production planning problems such as an Available to Promise (ATP) model of Chen et al. (2002) can involve material compatibility constraints that specify when components from various suppliers can be feasibly assembled into a final product. In a companion paper to Chen et al. (2002), Ball et al. (2003) showed that in many cases such constraints can be modeled as the set of feasible source-sink flows through an acyclic network. The flow through a node is the sum of the flows on all paths containing it. The number of paths is often exponential in the number of nodes, and so it is more computationally tractable to consider the set of node flows in place of that of path flows. Here nodes represent components and paths represent product configurations. In the context of NP hard Mixed Integer Programming models such as the ATP model, when the description of the set of node flows is too complicated to be explicitly written out, the material compatibility constraints can be handled in a cutting plane framework by using a separation algorithm. Ball et al. characterized the polyhedron of node flows for some special cases. We extend this work in various practical and theoretical directions: we allow arbitrary directed networks, we allow both upper and lower bounds on flows, we characterize which valid inequalities are facets, we give fast algorithms for separation, violation, and dimension, and we put all the pieces together into an algorithm for facet-separation. All algorithms are very efficient, as they are based on max flow and min-cost flow subroutines.

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References

  1. Ahuja R.K., Goldberg A.V., Orlin J.B., Tarjan R.E.: Finding minimum-cost flows by double scaling. Math. Prog. 53, 243–266 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  2. Ahuja R.K., Magnanti T.L., Orlin J.B.: Network Flows: Theory, Algorithms and Applications. Prentice Hall, New York (1993)

    Google Scholar 

  3. Ahuja R.K., Orlin J.B., Tarjan R.E.: Improved time bounds for the maximum flow problem. SIAM J. Comput. 18, 939–954 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  4. Applegate D.L., Bixby R.E., Chvátal V., Cook W.J.: The Traveling Salesman Problem: A Computational Study. Princeton University Press, Princeton (2007)

    Google Scholar 

  5. Balakrishnan A., Geunes J.: Requirements Planning with Substitutions: Exploring Bill-of-Materials Flexibility in Production Planning. MSOM 2, 166–185 (2000)

    Google Scholar 

  6. Ball M.O., Chen C.-Y., Zhao Z.-Y.: Material compatibility constraints for make-to-order production planning. OR Lett. 31, 420–428 (2003)

    MATH  MathSciNet  Google Scholar 

  7. Bland R.G., Jensen D.L.: On the computational behavior of a polynomial-time network flow algorithm. Math. Prog. 54, 1–39 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  8. Chen C.-Y., Zhao Z., Ball M.O.: A model for batch advanced available-to-promise. Prod. Oper. Manage. 11, 424–440 (2002)

    Google Scholar 

  9. Cheriyan J., Hagerup T.: A randomized maximum-flow algorithm. SIAM J. Comput. 24, 203–226 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  10. Cormen T.H., Leiserson C.E., Rivest R.L.: Introduction to Algorithms. McGraw-Hill, New York (1990)

    MATH  Google Scholar 

  11. Edmonds J., Karp R.M.: Theoretical improvements in algorithmic efficiency for network flow problems. J. ACM 19, 248–264 (1972)

    Article  MATH  Google Scholar 

  12. Fordyce K.: Matching assets with demand engines for PROFIT and supply chain management. MicroNews 4, 28–32 (1998)

    Google Scholar 

  13. Garey M.R., Johnson D.S.: Computers and Intractability, A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, New York (1979)

    MATH  Google Scholar 

  14. Goldberg A.V.: Scaling algorithms for the shortest paths problem. SIAM J. Comput. 24, 494–504 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  15. Goldberg A.V., Rao S.: Beyond the flow decomposition barrier. J. ACM 45, 753–797 (1998)

    Article  MathSciNet  Google Scholar 

  16. Goldberg A.V., Tarjan R.E.: A new approach to the maximum flow problem. J. ACM 35, 921–940 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  17. Goldberg A.V., Tarjan R.E.: Finding minimum-cost circulations by successive approximation. Math. Oper. Res. 15, 430–466 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  18. Grötschel M., Lovász L., Schrijver A.: Geometric Algorithms and Combinatorial Optimization. Springer, Berlin (1988)

    MATH  Google Scholar 

  19. Grötschel M., Padberg M.W.: On the symmetric traveling salesman problem II: Lifting theorems and facets. Math. Prog. 16, 281–302 (1979)

    Article  MATH  Google Scholar 

  20. Hoffman A.J.(1960) Some recent applications of the theory of linear inequalities to extremal combinatorial analysis. In: Bellman, R., Hall, M., Jr. (eds.) Proceedings of Symposia in Applied Mathematics, vol. X, Combinatorial Analysis. American Mathematical Society, Providence RI, pp 113–127

  21. Hoffman A.J.: A generalization of max flow- min cut. Math. Prog. 6, 352–359 (1974)

    Article  MATH  Google Scholar 

  22. King V., Rao S., Tarjan R.: A faster deterministic maximum flow algorithm. J. Algorithms 17, 447–474 (1994)

    Article  MathSciNet  Google Scholar 

  23. Lawler, E.L.: Generalizations of the Polymatroidal Network Flow Model. Preprint BW 158/82 of Stichting Mathematisch Centrum, Amsterdam (1982)

  24. Lawler E.L., Martel C.U.: Computing maximal polymatroidal network flows. Math. Oper. Res. 7, 334–347 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  25. Martens, M., McCormick, S.T.: A polynomial algorithm for weighted abstract flow. In: Proceedings of the 13th Conference on Integer Programming and Combinatorial Optimization, pp. 97–111 (2008)

  26. McCormick, S.T.: A Polynomial Algorithm for Abstract Maximum Flow. UBC Faculty of Commerce Working Paper 95-MSC-001. In: An extended abstract appears in Proceedings of the Seventh SODA, pp. 490–497 (1995)

  27. McCormick S.T.: How to compute least infeasible flows. Math. Prog. B 78, 179–194 (1997)

    MathSciNet  Google Scholar 

  28. McCormick, S.T., Queyranne, M.N.: Le Cône des Flots aux Noeuds dans un Réseau Acyclique. In: Proceedings of Journées sur les Polyèdres en Optimisation Combinatoire at Clermont-Ferrand (in French) (2003)

  29. Nemhauser G.A., Wolsey L.A.: Integer and Combinatorial Optimization. Wiley, New York (1999)

    MATH  Google Scholar 

  30. Orlin J.B.: A faster strongly polynomial minimum cost flow algorithm. Oper. Res. 41, 338–350 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  31. Phillips, S., Westbrook, J.: Online load balancing and network flow. In: Proceedings of the 25th Annual ACM Symposium on Theory of Computing, pp. 402–411 (1993)

  32. Picard J.-C., Queyranne M.N.: On the structure of all minimum cuts in a network and applications. Math. Prog. Study 13, 8–16 (1980)

    MATH  MathSciNet  Google Scholar 

  33. Queyranne, M.N.: On the node flow cone of an acyclic directed graph. In: Seventh Aussois Conference of Combinatorial Optimization (2003)

  34. Röck, H.: Scaling techniques for minimum cost network flows In: Pape, U. (ed.) Discrete Structures and Algorithms, pp. 181–191. Hanser (1980)

  35. Schrijver A.: Combinatorial Optimization: Polyhedra and Efficiency. Springer, Berlin (2003)

    MATH  Google Scholar 

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Correspondence to S. Thomas McCormick.

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Dedicated to Alan J. Hoffman.

Research of all authors was supported in part by research grants from the Natural Sciences and Engineering Research Council of Canada (NSERC).

S. Thomas McCormick, Maurice Queyranne: This research was initiated during a visit of M. Queyranne to the IMA Special Year on Optimization, whose support is gratefully appreciated; some research was done while at Laboratoire Leibniz, Institut IMAG, 38031 Grenoble cedex.

Maren Martens: This research was initiated during a postdoc at the Sauder School of Business, UBC, whose support is gratefully appreciated.

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Martens, M., McCormick, S.T. & Queyranne, M. Separation, dimension, and facet algorithms for node flow polyhedra. Math. Program. 124, 317–348 (2010). https://doi.org/10.1007/s10107-010-0378-2

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