5556 2015 eng 181 192 Experimental Algorithms, Lecture Notes in Computer Science 9125 conferenceobject Springer 0 -- -- -- Recently, there have been many successful applications of optimization algorithms that solve a sequence of quite similar mixed-integer programs (MIPs) as subproblems. Traditionally, each problem in the sequence is solved from scratch. In this paper we consider reoptimization techniques that try to benefit from information obtained by solving previous problems of the sequence. We focus on the case that subsequent MIPs differ only in the objective function or that the feasible region is reduced. We propose extensions of the very complex branch-and-bound algorithms employed by general MIP solvers based on the idea to ``warmstart'' using the final search frontier of the preceding solver run. We extend the academic MIP solver SCIP by these techniques to obtain a reoptimizing branch-and-bound solver and report computational results which show the effectiveness of the approach. 978-3-319-20086-6 10.1007/978-3-319-20086-6_14 Experimental Algorithms, Lecture Notes in Computer Science yes urn:nbn:de:0297-zib-54375 Gerald Gamrath Jakob Witzig Benjamin Hiller Jakob Witzig Mathematical Optimization Mathematical Optimization Methods Gamrath, Gerald ASTfSCM MIP-ZIBOPT MODAL-SynLab Siemens TRR154-A04 MODAL-Gesamt Applied Optimization