随机规划ICC(β)模型的混合智能算法

讨论了当随机规划的随机变量服从离散分布时,合成机会约束模型(ICC(β))中约束集合的结构;利用适合全局搜索的遗传算法以及局部搜索能力很强的爬山算法,结合逐次增加约束方法,给出了计算该模型的混合智能算法;实例验证了该算法的有效性。该算法也可作为含连续随机变量的(ICC(β))的逼近算法。     

A New Algorithm To Solve Vehicle Routing Problems (VRPs)

Efficient Resource management has a direct influence on business performance and profitability. Vehicle Routing Problems (VRPs) considered in this paper are resource management problems where the aim is to use the limited number of resources to a large number of jobs so that the maximum number of jobs can be completed with minimum cost. A VRP consists of a workforce of maintenance engineers and a set of geographically distributed customers requiring certain services. The problem is complicated by incorporating certain technological and temporal constraints. The objective is to maximize the amount of work done measured in terms of total number of jobs completed and to minimize the total distance travelled by all the engineers. The solution to a VRP is a list of engineers and for each engineer a tour consisting of an ordered list of services to be completed by him under the given constraints. These Problems belong to the class of NP-Complete problems. The stochastic techniques such as Hill-Climbing (HC), Tabu Search (TS), Genetic Algorithms (GA) and Simulated Annealing (SA) are found to be suitable for solving these problems efficiently. It is found empirically that out of these SA gives good results for VRPs. But in some cases it also gives poor quality results. This happens due to not allocating intelligently the unallocated jobs in SA in subsequent iterations. A new algorithm is proposed to solve VRPs in this paper. This is achieved by allocating unallocated jobs intelligently in SA. The proposed algorithm is tested empirically on a number of randomly generated VRPs. Three types of VRPs considered are over resourced, under resourced and critically resourced VRPs. In almost all cases, the proposed algorithm completes a large number of jobs with minimum cost in comparison with SA.     

基于模糊优化的渭河流域水资源多维调配仿真

为解决部分地区水资源紧缺的问题,提高水资源利用率,提出基于模糊优化的渭河流域水资源多维调配方法。建立模糊优化模型,综合考虑优化时的模糊性与相对性,通过合理运算获得最优方案的隶属度,以此来提高模型的仿真度与可靠性。根据构建的模型结合渭河流域水资源的实际情况,获取目标函数和多方面约束条件,有效控制水资源的开采量。在此基础上运用理想点法建立新的综合效益目标函数,最终获得最优水资源的多维调配方案。仿真结果表明,所提方法能够有效实现对渭河水资源的多维调配,相比其他方法具有较高实用性、可靠性和高效性。     

CLP(ℜ) and some electrical engineering problems

The Constraint Logic Programming Scheme defines a class of languages designed for programming with constraints using a logic programming approach. These languages are soundly based on a unified framework of formal semantics. In particular, as an instance of this scheme with real arithmetic constraints, the CLP() language facilitates and encourages a concise and declarative style of programming for problems involving a mix of numeric and non-numeric computation.In this paper we illustrate the practical applicability of CLP() with examples of programs to solve electrical engineering problems. This field is particularly rich in problems that are complex and largely numeric, enabling us to demonstrate a number of the unique features of CLP(). A detailed look at some of the more important programming techniques highlights the ability of CLP() to support well-known, powerful techniques from constraint programming. Our thesis is that CLP() is an embodiment of these techniques in a language that is more general, elegant and versatile than the earlier languages, and yet is practical.An earlier version of this paper appeared in the proceedings of the 4th International Conference on Logic Programming, Melbourne, May 1987. Much of this work was carried out while the authors were at Monash University, Melbourne, Australia.     

遗传算法在多品种小批量模式MPS中的应用

结合准时制生产和约束理论,以“关键设备产能”和“产品提前期”两大约束为重心,确定以关键工作中心利用率最高和提前/拖期罚款最少作为MPS模型的两个目标,提出了一种多品种、小批量生产模式下企业MPS的双目标排单模型的建立思路和方法;应用遗传算法对模型进行优化计算,通过对企业实例数据的运行结果分析,验证了方案的有效性。     

Constrained motion planning of nonholonomic systems

This paper addresses the constrained motion planning problem for nonholonomic systems represented by driftless control systems with output. The problem consists in defining a control function driving the system output to a desirable point at a given time instant, whereas state and control variables remain over the control horizon within prescribed bounds. The state and control constraints are handled by extending the control system with a pair of state equations driven by the violation of constraints, and adding regularizing perturbations. For the regularized system a Jacobian motion planning algorithm is designed, called imbalanced. Solutions of example constrained motion planning problems for the rolling ball illustrate the theoretical concepts.     

2K-H型多级行星齿轮减速器优化设计系统

讨论了行星齿轮减速器设计中的穷举优化因素,以太阳轮和全部行星轮的体积之和最小为优化设计目标函数,以太阳轮的参数为设计变量,建立了优化数学模型,设计出行星轮系的优化设计系统,并以三级行星减速机为例,介绍了该系统的实际应用。     

Regulator problem for uncertain linear discrete-time systems with constrained control

A necessary and sufficient condition to test the robustness of a regulator of uncertain linear systems with constrained control is given. The candidate regulator for this test is that stabilizing nominal systems. An illustrative example is also given.     

Optimizing the Hot Isostatic Pressing Process

A genetic algorithm (GA) is used to optimize the hot isostatic pressing (HIPing) process for beryllium powder. The GA evaluates a HIPing model with different processing schedules in an effort to minimize temperature, pressure, processing time, ramp rates, grain growth, and distance to target relative density. It is shown that this is a constrained, multiobjective, noisy, optimization problem to which the GA is able to evolve a large number of viable solutions. However, for the GA to work in such a large multidimensional search space, it is suggested that the constraints be treated as objectives and then penalize the Pareto ranking for each constraint violated. This approach is necessary because a large-dimensional objective space naturally results in most members being Pareto rank 1.