TY - GEN A1 - Shinano, Yuji A1 - Achterberg, Tobias A1 - Berthold, Timo A1 - Heinz, Stefan A1 - Koch, Thorsten T1 - ParaSCIP - a parallel extension of SCIP N2 - Mixed integer programming (MIP) has become one of the most important techniques in Operations Research and Discrete Optimization. SCIP (Solving Constraint Integer Programs) is currently one of the fastest non-commercial MIP solvers. It is based on the branch-and-bound procedure in which the problem is recursively split into smaller subproblems, thereby creating a so-called branching tree. We present ParaSCIP, an extension of SCIP, which realizes a parallelization on a distributed memory computing environment. ParaSCIP uses SCIP solvers as independently running processes to solve subproblems (nodes of the branching tree) locally. This makes the parallelization development independent of the SCIP development. Thus, ParaSCIP directly profits from any algorithmic progress in future versions of SCIP. Using a first implementation of ParaSCIP, we were able to solve two previously unsolved instances from MIPLIB2003, a standard test set library for MIP solvers. For these computations, we used up to 2048 cores of the HLRN~II supercomputer. T3 - ZIB-Report - 10-27 KW - massive parallization KW - mixed integer programming KW - ParaSCIP KW - branch-and-cut KW - branch-and-bound Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-11921 UR - http://www.springerlink.com/content/t2160206253v7661/ ER - TY - GEN A1 - Koch, Thorsten A1 - Ralphs, Ted A1 - Shinano, Yuji T1 - What could a million CPUs do to solve Integer Programs? N2 - Given the steady increase in cores per CPU, it is only a matter of time until supercomputers will have a million or more cores. In this article, we investigate the opportunities and challenges that will arise when trying to utilize this vast computing power to solve a single integer linear optimization problem. We also raise the question of whether best practices in sequential solution of ILPs will be effective in massively parallel environments. T3 - ZIB-Report - 11-40 Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-14222 SN - 1438-0064 ER - TY - CHAP A1 - Shinano, Yuji A1 - Berthold, Timo A1 - Heinz, Stefan T1 - A First Implementation of ParaXpress: Combining Internal and External Parallelization to Solve MIPs on Supercomputers T2 - Mathematical Software - ICMS 2016, 5th International Conference Berlin, Germany, July 11-14, 2016 Proceedings Y1 - 2016 U6 - https://doi.org/10.1007/978-3-319-42432-3_38 SP - 308 EP - 316 ER - TY - CHAP A1 - Berthold, Timo A1 - Gleixner, Ambros A1 - Heinz, Stefan A1 - Koch, Thorsten A1 - Shinano, Yuji T1 - SCIP Optimization Suite を利用した 混合整数(線形/非線形) 計画問題の解法 T2 - Proceedings of the 24th RAMP symposium. The Operations Society of Japan, RAMP: Research Association of Mathematical Programming N2 - この論文ではソフトウェア・パッケージSCIP Optimization Suite を紹介し,その3つの構成要素:モデリン グ言語Zimpl, 線形計画(LP: linear programming) ソルバSoPlex, そして,制約整数計画(CIP: constraint integer programming) に対するソフトウェア・フレームワークSCIP, について述べる.本論文では,この3つの 構成要素を利用して,どのようにして挑戦的な混合整数線形計画問題(MIP: mixed integer linear optimization problems) や混合整数非線形計画問題(MINLP: mixed integer nonlinear optimization problems) をモデル化 し解くのかを説明する.SCIP は,現在,最も高速なMIP,MINLP ソルバの1つである.いくつかの例により, Zimpl, SCIP, SoPlex の利用方法を示すとともに,利用可能なインタフェースの概要を示す.最後に,将来の開 発計画の概要について述べる. N2 - This paper introduces the SCIP Optimization Suite and discusses the capabilities of its three components: the modeling language Zimpl, the linear programming solver SoPlex, and the constraint integer programming framework SCIP. We explain how in concert these can be used to model and solve challenging mixed integer linear and nonlinear optimization problems. SCIP is currently one of the fastest non-commercial MIP and MINLP solvers. We demonstrate the usage of Zimpl, SCIP, and SoPlex by selected examples, we give an overview over available interfaces, and outline plans for future development. Y1 - 2012 SP - 165 EP - 192 ER - TY - GEN A1 - Yokoyama, Ryohei A1 - Shinano, Yuji A1 - Taniguchi, Syusuke A1 - Ohkura, Masashi A1 - Wakui, Tetsuya T1 - Optimization of energy supply systems by MILP branch and bound method in consideration of hierarchical relationship between design and operation N2 - To attain the highest performance of energy supply systems, it is necessary to rationally determine types, capacities, and numbers of equipment in consideration of their operational strategies corresponding to seasonal and hourly variations in energy demands. In the combinatorial optimization method based on the mixed-integer linear programming (MILP), integer variables are used to express the selection, numbers, and on/off status of operation of equipment, and the number of these variables increases with those of equipment and periods for variations in energy demands, and affects the computation efficiency significantly. In this paper, a MILP method utilizing the hierarchical relationship between design and operation variables is proposed to solve the optimal design problem of energy supply systems efficiently: At the upper level, the optimal values of design variables are searched by the branch and bound method; At the lower level, the values of operation variables are optimized independently at each period by the branch and bound method under the values of design variables given tentatively during the search at the upper level; Lower bounds for the optimal value of the objective function are evaluated, and are utilized for the bounding operations at both the levels. This method is implemented into open and commercial MILP solvers. Illustrative and practical case studies on the optimal design of cogeneration systems are conducted, and the validity and effectiveness of the proposed method are clarified. T3 - ZIB-Report - 14-09 KW - Energy supply systems KW - Optimal design KW - Optimal operation KW - Mixed-integer linear programming KW - Branch and bound method KW - Hierarchical approach Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-49413 SN - 1438-0064 ER - TY - JOUR A1 - Shinano, Yuji A1 - Inui, Nobuo A1 - Fukagawa, Youzou A1 - Takakura, Noburu T1 - Optimizing Movement Sequences for Step-and-Scan Lithography Equipment JF - Journal of Advanced Mechanical Design, Systems, and Manufacturing N2 - The purpose of this work is to improve the throughput of step-and-scan lithography equipment to shorten the production time of a wafer. For this purpose, we propose a method for solving the MSOP (Movement Sequence Optimization Problem), which is the problem of computing the fastest schedule for visiting all shots on a wafer. It is well-known that the MSOP on step-and-repeat lithography equipment can be modeled as a traveling salesman problem. In contrast to step-and-repeat lithography equipment, a schedule for step-and-scan lithography equipment must also indicate the scanning direction of each shot, in addition to the sequence of the shots. For this reason, the traveling salesman problem formulation for step-and-repeat lithography equipment cannot be applied to solve the MSOP on step-and-scan lithography equipment directly. We overcame this difficulty by introducing auxiliary vertices to model the scanning directions in the traveling salesman problem formulation. By this method, we were able to compute exact optimal sequences considering the scanning directions of shots for several MSOP instances. Our numerical experiments demonstrated that our proposed method was capable of computing exact optimal solutions for real-world MSOP instances having up to 232 shots on a wafer. These optimal solutions gave a 0.25% to 4.66% improvement in productivity over solutions computed by previously known methods. Y1 - 2013 U6 - https://doi.org/10.1299/jamdsm.7.608 VL - 7 IS - 4 SP - 608 EP - 618 ER - TY - GEN A1 - Ralphs, Ted A1 - Shinano, Yuji A1 - Berthold, Timo A1 - Koch, Thorsten T1 - Parallel Solvers for Mixed Integer Linear Programming N2 - In this article, we introduce parallel mixed integer linear programming (MILP) solvers. MILP solving algorithms have been improved tremendously in the last two decades. Currently, commercial MILP solvers are known as a strong optimization tool. Parallel MILP solver development has started in 1990s. However, since the improvements of solving algorithms have much impact to solve MILP problems than application of parallel computing, there were not many visible successes. With the spread of multi-core CPUs, current state-of-the-art MILP solvers have parallel implementations and researches to apply parallelism in the solving algorithm also getting popular. We summarize current existing parallel MILP solver architectures. T3 - ZIB-Report - 16-74 KW - parallel computing KW - integer programming KW - branch-and-bound KW - supercomputing Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-62239 SN - 1438-0064 ER - TY - CHAP A1 - Shinano, Yuji A1 - Achterberg, Tobias A1 - Berthold, Timo A1 - Heinz, Stefan A1 - Koch, Thorsten ED - Bischof, Christian ED - Hegering, Heinz-Gerd ED - Nagel, Wolfgang ED - Wittum, Gabriel T1 - ParaSCIP: a parallel extension of SCIP T2 - Competence in High Performance Computing 2010 Y1 - 2012 U6 - https://doi.org/10.1007/978-3-642-24025-6_12 SP - 135 EP - 148 PB - Springer ER - TY - JOUR A1 - Koch, Thorsten A1 - Ralphs, Ted A1 - Shinano, Yuji T1 - Could we use a million cores to solve an integer program? JF - Mathematical Methods of Operations Research Y1 - 2012 U6 - https://doi.org/10.1007/s00186-012-0390-9 VL - 76 IS - 1 SP - 67 EP - 93 ER - TY - GEN A1 - Berthold, Timo A1 - Gleixner, Ambros A1 - Heinz, Stefan A1 - Koch, Thorsten A1 - Shinano, Yuji T1 - SCIP Optimization Suite を利用した 混合整数(線形/非線形) 計画問題の解法 T1 - Solving mixed integer linear and nonlinear problems using the SCIP Optimization Suite N2 - この論文ではソフトウェア・パッケージSCIP Optimization Suite を紹介し,その3つの構成要素:モデリン グ言語Zimpl, 線形計画(LP: linear programming) ソルバSoPlex, そして,制約整数計画(CIP: constraint integer programming) に対するソフトウェア・フレームワークSCIP, について述べる.本論文では,この3つの 構成要素を利用して,どのようにして挑戦的な混合整数線形計画問題(MIP: mixed integer linear optimization problems) や混合整数非線形計画問題(MINLP: mixed integer nonlinear optimization problems) をモデル化 し解くのかを説明する.SCIP は,現在,最も高速なMIP,MINLP ソルバの1つである.いくつかの例により, Zimpl, SCIP, SoPlex の利用方法を示すとともに,利用可能なインタフェースの概要を示す.最後に,将来の開 発計画の概要について述べる. N2 - This paper introduces the SCIP Optimization Suite and discusses the capabilities of its three components: the modeling language Zimpl, the linear programming solver SoPlex, and the constraint integer programming framework SCIP. We explain how in concert these can be used to model and solve challenging mixed integer linear and nonlinear optimization problems. SCIP is currently one of the fastest non-commercial MIP and MINLP solvers. We demonstrate the usage of Zimpl, SCIP, and SoPlex by selected examples, we give an overview over available interfaces, and outline plans for future development. T3 - ZIB-Report - 12-24 KW - SCIP, MIP, MINLP, CIP, LP, modeling, optimization Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-15598 SN - 1438-0064 ER -