Refine
Year of publication
- 2020 (11) (remove)
Document Type
- Article (4)
- In Proceedings (3)
- ZIB-Report (2)
- Book chapter (1)
- Software (1)
Keywords
A robust optimal design method of energy supply systems under uncertain energy demands has been proposed using a mixed- integer linear model for constituent equipment. A robust optimal design problem has been formulated as a three-level min-max- min optimization one by expressing uncertain energy demands by intervals, evaluating the robustness in a performance criterion based on the minimax regret criterion, and considering hierarchical relationships among design variables, uncertain energy demands, and operation variables. Since this problem must be solved by a special algorithm and is too difficult to solve even using a commercial solver, a hierarchical optimization approach has been applied to solve the problem but its application is limited only to small scale toy problems. In this paper, some strategies are introduced into the hierarchical optimization approach to enhance the computation efficiency for the purpose of applying the approach to large scale practical problems. In a case study, the proposed approach is applied to the robust optimal design of a cogeneration system with a complex configuration, and the validity and effectiveness of the method are ascertained.
A robust optimal design method of energy supply systems under uncertain energy demands has been proposed using a mixed-integer linear model for constituent equipment. However, this method takes a long computation time, and thus it can be applied only to small-scale problems. In this paper, a hierarchical optimization method is applied to two types of optimization problems for evaluating robustness to solve them efficiently. In a case study, the proposed method is applied to a cogeneration system with a complex configuration, and the validity and effectiveness of the method are ascertained.
To attain the highest performance of energy supply systems, it is necessary to determine design specifications optimally in consideration of operational strategies corresponding to seasonal and hourly variations in energy demands. Mixed-integer linear programming (MILP) methods have been applied widely to such optimal design problems. A hierarchical MILP method has been proposed to solve the problems very efficiently. In addition, by utilizing features of the hierarchical MILP method, a method of reducing model by clustering periods based on the optimal operational strategies of equipment has been proposed to search design solution candidates efficiently in the relaxed optimal design problem at the upper level. In this paper, these methods are applied to the multiobjective optimal design of a cogeneration system by considering the annual total cost and primary energy consumption as the objective functions to be minimized. Through a case study, it turns out that the model reduction by the operation-based time-period clustering is effective in terms of the computation efficiency when importance is given to the first objective function, while it is not when importance is given to the second objective function.
To attain the highest performance of energy supply systems, it is necessary to determine design specifications optimally in consideration of operational strategies corresponding to seasonal and hourly variations in energy demands. A hierarchical mixed-integer linear programming method has been proposed to solve such an optimal design problem efficiently. In this paper, a method of reducing model by clustering periods with the k-medoids method is applied to the relaxed optimal design problem at the upper level. Through a case study, it is clarified how the proposed method is effective to enhance the computation efficiency in a large scale optimal design problem.
UG is a generic framework to parallelize branch-and-bound based solvers (e.g., MIP, MINLP, ExactIP) in a distributed or shared memory computing environment. It exploits the powerful performance of state-of-the-art "base solvers", such as SCIP, CPLEX, etc. without the need for base solver parallelization.
UG framework, ParaSCIP(ug[SCIP,MPI]) and FiberSCIP (ug[SCIP,Pthreads]) are available as a beta version. For MIP solving, ParaSCIP and FiberSCIP are well debugged and should be stable. For MINLP solving, they are relatively stable, but not as thoroughly debugged. This release version should handle branch-and-cut approaches where subproblems are defined by variable bounds and also by constrains for ug[SCIP,*] ParaSCIP and FiberSCIP). Therefore, problem classes other than MIP or MINLP can be handled, but they have not been tested yet.
v0.9.1: Update orbitope cip files.
Massive Parallelization for Finding Shortest Lattice Vectors Based on Ubiquity Generator Framework
(2020)
Lattice-based cryptography has received attention as a next-generation encryption technique, because it is believed to be secure against attacks by classical and quantum computers. Its essential security depends on the hardness of solving the shortest vector problem (SVP). In the cryptography, to determine security levels, it is becoming significantly more important to estimate the hardness of the SVP by high-performance computing. In this study, we develop the world’s first distributed and asynchronous parallel SVP solver, the MAssively Parallel solver for SVP (MAP-SVP). It can parallelize algorithms for solving the SVP by applying the Ubiquity Generator framework, which is a generic framework for branch-and-bound algorithms. The MAP-SVP is suitable for massive-scale parallelization, owing to its small memory footprint, low communication overhead, and rapid checkpoint and restart mechanisms. We demonstrate its performance and scalability of the MAP-SVP by using up to 100,032 cores to solve instances of the Darmstadt SVP Challenge.
The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems effi- ciently. As one of the strategies to enhance the computation efficiency furthermore, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently in the relaxed optimal design problem at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. In applying the model reduc- tion, the methods of clustering periods by the order of time series, based on an operational strategy, and by the k-medoids method are applied. As a case study, the multiobjective optimal design of a gas turbine cogeneration system with a practical configuration is investigated by adopting the annual total cost and pri- mary energy consumption as the objective functions to be minimized simultaneously, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function. It also turns out that the model reduction only by the k- medoids method is effective very limitedly when importance is given to minimizing the second objective function.
In designing energy supply systems, designers should heighten the robustness in performance criteria against the uncertainty in energy demands. In this paper, a robust optimal design method using a hierarchi- cal mixed-integer linear programming (MILP) method is proposed to maximize the robustness of energy sup- ply systems under uncertain energy demands based on a mixed-integer linear model. A robust optimal design problem is formulated as a three-level min-max-min MILP one by expressing uncertain energy demands by intervals, evaluating the robustness in a performance criterion based on the minimax regret cri- terion, and considering relationships among integer design variables, uncertain energy demands, and inte- ger and continuous operation variables. This problem is solved by evaluating upper and lower bounds for the minimum of the maximum regret of the performance criterion repeatedly outside, and evaluating lower and upper bounds for the maximum regret repeatedly inside. Since these different types of optimization problems are difficult to solve even using commercial MILP solvers, they are solved by applying a hierarchi- cal MILP method developed for ordinary optimal design problems with its modifications. In a case study, the proposed approach is applied to the robust optimal design of a cogeneration system. Through the study, its validity and effectiveness are ascertained, and some features of the obtained robust designs are clarified.
Mixed-integer programming (MIP) problem is arguably among the hardest classes of optimization problems. This paper describes how we solved 21 previously unsolved MIP instances from the MIPLIB benchmark sets. To achieve these results we used an enhanced version of ParaSCIP, setting a new record for the largest scale MIP computation: up to 80,000 cores in parallel on the Titan supercomputer. In this paper, we describe the basic parallelization mechanism of ParaSCIP, improvements of the dynamic load balancing and novel techniques to exploit the power of parallelization for MIP solving. We give a detailed overview of computing times and statistics for solving open MIPLIB instances.