a constraint solver and its application to path feasibility analysis

Many testing methods are based on program paths. A well-known problem with them is that some paths are infeasible. To decide the feasibility of paths, we may solve a set of constraints. In this paper, we describe constraint-based tools that can be used for this purpose. They accept constraints expressed in a natural form, which may involve variables of different types such as integers, Booleans, reals and fixed-size arrays. The constraint solver is an extension of a Boolean satisfiability checker and it makes use of a linear programming package. The solving algorithm is described, and examples are given to illustrate the use of the tools. For many paths in the testing literature, their feasibility can be decided in a reasonable amount of time.

Optimal design of a 7 T highly homogeneous superconducting magnet for a Penning trap

A Penning trap system called Lanzhou Penning Trap (LPT) is now being developed for precise mass measurements at the Institute of Modern Physics (IMP). One of the key components is a 7 T actively shielded superconducting magnet with a clear warm bore of 156 mm. The required field homogeneity is 3 x 10(-7) over two 1 cubic centimeter volumes lying 220 mm apart along the magnet axis. We introduce a two-step method which combines linear programming and a nonlinear optimization algorithm for designing the multi-section superconducting magnet. This method is fast and flexible for handling arbitrary shaped homogeneous volumes and coils. With the help of this method an optimal design for the LPT superconducting magnet has been obtained.

逆向物流流量不确定闭环供应链鲁棒运作策略设计

考虑一类同时具有再分销、再制造和再利用的闭环供应链在逆向物流流量不确定环境下的运作问题.采用具有已知概率的离散情景描述逆向物流流量的不确定性,利用基于情景分析的鲁棒线性优化方法建立该闭环供应链的多目标运作模型.设计了一个数值算例,其结果验证了运作策略的鲁棒性.在该算例基础上,分析了逆向物流流量的大小对闭环供应链系统运作性能的影响.

聚类引导搜索的移动机器人路径规划方法

本文提出一种聚类引导搜索（cluster guide searching，CGS）的路径规划方法。采用基于最大最小距离的K均值聚类方法对样本进行离线聚类学习，学习结果以相似环境相似决策的知识形式进行存储。路径规划过程中，机器人在线整理环境信息，获得输入空间样本，通过与知识库匹配，检索到最近的类别，然后在该类别内部采用速度优先策略和方向优先策略交替的方式搜索输出空间。若知识不完备导致检索失败，可重启线性规划算法（linear programming，LP）进行在线路径规划，并更新聚类知识库。仿真结果表明该方法是一种有效的路径规划学习方法。

加速度空间中基于线性规划的移动机器人路径规划方法

针对动态不确定环境下移动机器人的路径规划问题,提出了加速度空间中一种基于线性规划(Linear programming,LP)的方法.在机器人的加速度空间中利用相对信息,把机器人路径规划这一非线性问题,描述成满足一组线性约束同时使目标函数极小的线性规划问题,嵌入基于线性规划方法的规划器,得到一条满足性能要求的最优路径.仿真试验验证了算法的实用性及有效性,与势场引导进化计算的方法(Artificial potential guided evolution algorithm,APEA)相比更优化,更实时.

柔性制造系统的负荷分配及路径规划方法

柔性是柔性制造系统（ＦＭＳ）的一个基本优点，但这一基本优点却往往被人们所忽视，许多现在运行的ＦＭＳ不是缺乏柔性，就是没能充分利用可获得的柔性来提高生产效率柔性制造系统的负荷分配和路径规划问题正是这种柔性的一个主要方面．然而，路径规划决策却往往被忽视．其中一个主要原因就是人们仍不能从传统的生产管理概念中解放出来．本文在明确概念区分的基础上，提出了一种柔性制造系统的负荷分配和路径规划的线性规划模型，其主要特点是将负荷分配和路径规划问题有机地结合起来，并通过仿真实验验证并分析了此方法对ＦＭＳ性能上的影响。

Residual Vibration Reduction in Computer Controlled Machines

Control of machines that exhibit flexibility becomes important when designers attempt to push the state of the art with faster, lighter machines. Three steps are necessary for the control of a flexible planet. First, a good model of the plant must exist. Second, a good controller must be designed. Third, inputs to the controller must be constructed using knowledge of the system dynamic response. There is a great deal of literature pertaining to modeling and control but little dealing with the shaping of system inputs. Chapter 2 examines two input shaping techniques based on frequency domain analysis. The first involves the use of the first deriviate of a gaussian exponential as a driving function template. The second, acasual filtering, involves removal of energy from the driving functions at the resonant frequencies of the system. Chapter 3 presents a linear programming technique for generating vibration-reducing driving functions for systems. Chapter 4 extends the results of the previous chapter by developing a direct solution to the new class of driving functions. A detailed analysis of the new technique is presented from five different perspectives and several extensions are presented. Chapter 5 verifies the theories of the previous two chapters with hardware experiments. Because the new technique resembles common signal filtering, chapter 6 compares the new approach to eleven standard filters. The new technique will be shown to result in less residual vibrations, have better robustness to system parameter uncertainty, and require less computation than other currently used shaping techniques.

Safe Compositional Specification of Network Systems With Polymorphic, Constrained Types

In the framework of iBench research project, our previous work created a domain specific language TRAFFIC [6] that facilitates specification, programming, and maintenance of distributed applications over a network. It allows safety property to be formalized in terms of types and subtyping relations. Extending upon our previous work, we add Hindley-Milner style polymorphism [8] with constraints [9] to the type system of TRAFFIC. This allows a programmer to use for-all quantifier to describe types of network components, escalating power and expressiveness of types to a new level that was not possible before with propositional subtyping relations. Furthermore, we design our type system with a pluggable constraint system, so it can adapt to different application needs while maintaining soundness. In this paper, we show the soundness of the type system, which is not syntax-directed but is easier to do typing derivation. We show that there is an equivalent syntax-directed type system, which is what a type checker program would implement to verify the safety of a network flow. This is followed by discussion on several constraint systems: polymorphism with subtyping constraints, Linear Programming, and Constraint Handling Rules (CHR) [3]. Finally, we provide some examples to illustrate workings of these constraint systems.

Reasoning with imprecise trade-offs in decision making under certainty and uncertainty

In many real world situations, we make decisions in the presence of multiple, often conflicting and non-commensurate objectives. The process of optimizing systematically and simultaneously over a set of objective functions is known as multi-objective optimization. In multi-objective optimization, we have a (possibly exponentially large) set of decisions and each decision has a set of alternatives. Each alternative depends on the state of the world, and is evaluated with respect to a number of criteria. In this thesis, we consider the decision making problems in two scenarios. In the first scenario, the current state of the world, under which the decisions are to be made, is known in advance. In the second scenario, the current state of the world is unknown at the time of making decisions. For decision making under certainty, we consider the framework of multiobjective constraint optimization and focus on extending the algorithms to solve these models to the case where there are additional trade-offs. We focus especially on branch-and-bound algorithms that use a mini-buckets algorithm for generating the upper bound at each node of the search tree (in the context of maximizing values of objectives). Since the size of the guiding upper bound sets can become very large during the search, we introduce efficient methods for reducing these sets, yet still maintaining the upper bound property. We define a formalism for imprecise trade-offs, which allows the decision maker during the elicitation stage, to specify a preference for one multi-objective utility vector over another, and use such preferences to infer other preferences. The induced preference relation then is used to eliminate the dominated utility vectors during the computation. For testing the dominance between multi-objective utility vectors, we present three different approaches. The first is based on a linear programming approach, the second is by use of distance-based algorithm (which uses a measure of the distance between a point and a convex cone); the third approach makes use of a matrix multiplication, which results in much faster dominance checks with respect to the preference relation induced by the trade-offs. Furthermore, we show that our trade-offs approach, which is based on a preference inference technique, can also be given an alternative semantics based on the well known Multi-Attribute Utility Theory. Our comprehensive experimental results on common multi-objective constraint optimization benchmarks demonstrate that the proposed enhancements allow the algorithms to scale up to much larger problems than before. For decision making problems under uncertainty, we describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in Rp, and are typically only partially ordered. These can be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of the Pareto set based on ϵ-coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user trade-offs, which also greatly improves the efficiency.

Information relaxations and duality in stochastic dynamic programs

We describe a general technique for determining upper bounds on maximal values (or lower bounds on minimal costs) in stochastic dynamic programs. In this approach, we relax the nonanticipativity constraints that require decisions to depend only on the information available at the time a decision is made and impose a "penalty" that punishes violations of nonanticipativity. In applications, the hope is that this relaxed version of the problem will be simpler to solve than the original dynamic program. The upper bounds provided by this dual approach complement lower bounds on values that may be found by simulating with heuristic policies. We describe the theory underlying this dual approach and establish weak duality, strong duality, and complementary slackness results that are analogous to the duality results of linear programming. We also study properties of good penalties. Finally, we demonstrate the use of this dual approach in an adaptive inventory control problem with an unknown and changing demand distribution and in valuing options with stochastic volatilities and interest rates. These are complex problems of significant practical interest that are quite difficult to solve to optimality. In these examples, our dual approach requires relatively little additional computation and leads to tight bounds on the optimal values. © 2010 INFORMS.

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Representing temporal knowledge in legal discourse

This paper presents a formalism for representing temporal knowledge in legal discourse that allows an explicit expression of time and event occurrences. The fundamental time structure is characterized as a well‐ordered discrete set of primitive times, i.e. non‐decomposable intervals with positive duration or points with zero duration), from which decomposable intervals can be constructed. The formalism supports a full representation of both absolute and relative temporal knowledge, and a formal mechanism for checking the temporal consistency of a given set of legal statements is provided. The general consistency checking algorithm which addresses both absolute and relative temporal knowledge turns out to be a linear programming problem, while in the special case where only relative temporal relations are involved, it becomes a simple question of searching for cycles in the graphical representation of the corresponding legal text.

Assignment methods for incidence calculus

Incidence calculus is a mechanism for probabilistic reasoning in which sets of possible worlds, called incidences, are associated with axioms, and probabilities are then associated with these sets. Inference rules are used to deduce bounds on the incidence of formulae which are not axioms, and bounds for the probability of such a formula can then be obtained. In practice an assignment of probabilities directly to axioms may be given, and it is then necessary to find an assignment of incidence which will reproduce these probabilities. We show that this task of assigning incidences can be viewed as a tree searching problem, and two techniques for performing this research are discussed. One of these is a new proposal involving a depth first search, while the other incorporates a random element. A Prolog implementation of these methods has been developed. The two approaches are compared for efficiency and the significance of their results are discussed. Finally we discuss a new proposal for applying techniques from linear programming to incidence calculus.

Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review

The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

Investigation on the development of bidding strategies for a wind farm owner

In this paper, the development of bidding strategies is investigated for a wind farm owner. The optimization model is characterized by making the analysis of scenarios. The proposed approach allows evaluating alternative production strategies in order to submit bids to the electricity market with the goal of maximizing profits. The problem is formulated as a linear programming problem. An application to a case study is presented