Mathematical Programming

, Volume 161, Issue 1–2, pp 193–235 | Cite as

The Ancestral Benders’ cutting plane algorithm with multi-term disjunctions for mixed-integer recourse decisions in stochastic programming

  • Yunwei Qi
  • Suvrajeet SenEmail author
Full Length Paper Series A


This paper focuses on solving two-stage stochastic mixed integer programs (SMIPs) with general mixed integer decision variables in both stages. We develop a decomposition algorithm in which the first-stage approximation is solved by a branch-and-bound algorithm with its nodes inheriting Benders’ cuts that are valid for their ancestor nodes. In addition, we develop two closely related convexification schemes which use multi-term disjunctive cuts to obtain approximations of the second-stage mixed-integer programs. We prove that the proposed methods are finitely convergent. One of the main advantages of our decomposition scheme is that we use a Benders-based branch-and-cut approach in which linear programming approximations are strengthened sequentially. Moreover as in many decomposition schemes, these subproblems can be solved in parallel. We also illustrate these algorithms using several variants of an SMIP example from the literature, as well as a new set of test problems, which we refer to as Stochastic Server Location and Sizing. Finally, we present our computational experience with previously known examples as well as the new collection of SMIP instances. Our experiments reveal that our algorithm is able to produce provably optimal solutions (within an hour of CPU time) even in instances for which a highly reliable commercial MIP solver is unable to provide an optimal solution within an hour of CPU time.


Two-stage stochastic mixed-integer programs Cutting plane tree algorithm Multi-term disjunctive cut Benders’ decomposition 

Mathematics Subject Classification

90C15 90C10 90C06 



The authors are grateful to the referees for their detailed reading of the paper. Their suggestions improved the presentation, and made the paper more compelling.


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Copyright information

© Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2016

Authors and Affiliations

  1. 1.Integrated Systems EngineeringThe Ohio State UniversityColumbusUSA
  2. 2.Epstein Department of Industrial and Systems EngineeringUniversity of Southern CaliforniaLos AngelesUSA

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