基于硬件可编程逻辑的SAT求解算法研究与进展

布尔可满足性SAT问题作为第一个被证明的NP完全问题,是计算机理论与应用的核心问题,有着重要的应用价值,因此近年来涌现了各种各样SAT求解器。但是,SAT求解器的运算效率始终是影响其应用的关键因素,所以利用硬件的高性能与并行性来加速SAT求解过程已成为验证领域的一个研究热点。归纳总结了在SAT求解过程中,利用硬件现场可编程门逻辑FPGA的并行性和灵活性加速求解过程的各种算法研究,着重总结分析了应用型SAT求解器的加速策略。通过对各种方法的深入分析,指出它们的优缺点,为未来的研究提供了思路。     

搜索引擎系统：UML/OCL模型驱动开发

介绍一个搜索引擎系统的开发。运用UML用例图做系统的需求分析,运用UML类图、状态图、包图进行系统设计。以OCL表达式丰富状态图。使用C#语言和ASP.NET编程,SQL Server存储信息,系统运行在微软.NET环境上。遵循面向对象方法,如逐步求精和模块化,建立UML/OCL模型、组织程序,将系统逐层分解成具有特定功能的模块。系统由收集器、索引器、检索器三大部分组成,分别负责数据收集、数据分析和索引构建、用户检索接口。论文报告系统分析、设计和实现的思想和过程。     

Solving a real-time allocation problem with constraint programming

In this paper, we present an original approach (CPRTA for “Constraint Programming for solving Real-Time Allocation”) based on constraint programming to solve a static allocation problem of hard real-time tasks. This problem consists in assigning periodic tasks to distributed processors in the context of fixed priority preemptive scheduling. CPRTA is built on dynamic constraint programming together with a learning method to find a feasible processor allocation under constraints. Two efficient new approaches are proposed and validated with experimental results. Moreover, CPRTA exhibits very interesting properties. It is complete (if a problem has no solution, the algorithm is able to prove it); it is non-parametric (it does not require specific tuning) thus allowing a large diversity of models to be easily considered. Finally, thanks to its capacity to explain failures, it offers attractive perspectives for guiding the architectural design process.     

基于混合双种群差分进化的电力系统经济负荷分配

针对电力系统经济负荷分配本质上的非线性约束优化问题,提出一种双种群混合差分进化算法.采用两个种群且以较小的计算量实现目标函数的寻优并保持解的可行性,同时引入单纯型法来提高算法的局部搜索能力.基于典型算例对该算法的进化行为进行测试,并通过仿真和比较验证了所提出算法的有效性.     

Level‐set topology optimization with many linear buckling constraints using an efficient and robust eigensolver

Linear buckling constraints are important in structural topology optimization for obtaining designs that can support the required loads without failure. During the optimization process, the critical buckling eigenmode can change; this poses a challenge to gradient‐based optimization and can require the computation of a large number of linear buckling eigenmodes. This is potentially both computationally difficult to achieve and prohibitively expensive. In this paper, we motivate the need for a large number of linear buckling modes and show how several features of the block Jacobi conjugate gradient (BJCG) eigenvalue method, including optimal shift estimates, the reuse of eigenvectors, adaptive eigenvector tolerances and multiple shifts, can be used to efficiently and robustly compute a large number of buckling eigenmodes. This paper also introduces linear buckling constraints for level‐set topology optimization. In our approach, the velocity function is defined as a weighted sum of the shape sensitivities for the objective and constraint functions. The weights are found by solving an optimization sub‐problem to reduce the mass while maintaining feasibility of the buckling constraints. The effectiveness of this approach in combination with the BJCG method is demonstrated using a 3D optimization problem. Copyright © 2016 John Wiley & Sons, Ltd.     

Automated diagnosis of feature model configurations

Software product-lines (SPLs) are software platforms that can be readily reconfigured for different project requirements. A key part of an SPL is a model that captures the rules for reconfiguring the software. SPLs commonly use feature models to capture SPL configuration rules. Each SPL configuration is represented as a selection of features from the feature model. Invalid SPL configurations can be created due to feature conflicts introduced via staged or parallel configuration or changes to the constraints in a feature model. When invalid configurations are created, a method is needed to automate the diagnosis of the errors and repair the feature selections.This paper provides two contributions to research on automated configuration of SPLs. First, it shows how configurations and feature models can be transformed into constraint satisfaction problems to automatically diagnose errors and repair invalid feature selections. Second, it presents empirical results from diagnosing configuration errors in feature models ranging in size from 100 to 5,000 features. The results of our experiments show that our CSP-based diagnostic technique can scale up to models with thousands of features.     

Project scheduling under resource constraints: Application of the cumulative global constraint in a decision support framework

This paper concerns project scheduling under resource constraints. Traditionally, the objective is to find a unique solution that minimizes the project makespan, while respecting the precedence constraints and the resource constraints. This work focuses on developing a model and a decision support framework for industrial application of the cumulative global constraint. For a given project scheduling, the proposed approach allows the generation of different optimal solutions relative to the alternate availability of outsourcing and resources. The objective is to provide a decision-maker an assistance to construct, choose, and define the appropriate scheduling program taking into account the possible capacity resources. The industrial problem under consideration is modeled as a constraint satisfaction problem (CSP). It is implemented under the constraint programming language CHIP V5. The provided solutions determine values for the various variables associated to the tasks realized on each resource, as well as the curves with the profile of the total consumption of resources on time.     

A constraint programming-based solution approach for medical resident scheduling problems

Persistent calls come from within the graduate medical education community and from external sources for regulating the resident duty hours in order to meet the obligations about the quality of resident education, the well-being of residents themselves, and the quality of patient care services. The report of the Accreditation Council for Graduate Medical Education (ACGME) proposes common program requirements for resident hours. In this paper, we first develop a mixed-integer programming model for scheduling residents’ duty hours considering the on-call night, day-off, rest period, and total work-hour ACGME regulations as well as the demand coverage requirements of the residency program. Subsequently, we propose a column generation model that consists of a master problem and an auxiliary problem. The master problem finds a configuration of individual schedules that minimizes the sum of deviations from the desired service levels for the day and night periods. The formulation of this problem is possible by representing the feasible schedules using column variables, whereas the auxiliary problem finds the whole set of feasible schedules using constraint programming. The proposed approach has been tested on a series of problems using real data obtained from a hospital. The results indicate that high-quality schedules can be obtained within a few seconds.     

Combined planning and scheduling in a divergent production system with co-production: A case study in the lumber industry

Many research initiatives carried out in production management consider process planning and operations scheduling as two separate and sequential functions. However, in certain contexts, the two functions must be better integrated. This is the case in divergent production systems with co-production (i.e. production of different products at the same time from a single product input) when alternative production processes are available. This paper studies such a context and focuses on the case of drying and finishing operations in a softwood lumber facility. The situation is addressed using a single model that simultaneously performs process planning and scheduling. We evaluate two alternative formulations. The first one is based on mixed integer programming (MIP) and the second on constraint programming (CP). We also propose a search procedure to improve the performance of the CP approach. Both approaches are compared with respect to their capacity to generate good solutions in short computation time.