Formalization of Workflows and Correctness Issues in the Presence of Concurrency

In this paper, main components of a workflow system that are relevant to the correctness in the presence of concurrency are formalized based on set theory and graph theory. The formalization which constitutes the theoretical basis of the correctness criterion provided can be summarized as follows:-Activities of a workflow are represented through a notation based on set theory to make it possible to formalize the conceptual grouping of activities.-Control-flow is represented as a special graph based on this set definition, and it includes serial composition, parallel composition, conditional branching, and nesting of individual activities and conceptual activities themselves.-Data-flow is represented as a directed acyclic graph in conformance with the control-flow graph.The formalization of correctness of concurrently executing workflow instances is based on this framework by defining two categories of constraints on the workflow environment with which the workflow instances and their activities interact. These categories are:-Basic constraints that specify the correct states of a workflow environment.-Inter-activity constraints that define the semantic dependencies among activities such as an activity requiring the validity of a constraint that is set or verified by a preceding activity.Basic constraints graph and inter-activity constraints graph which are in conformance with the control-flow and data-flow graphs are then defined to represent these constraints. These graphs are used in formalizing the intervals among activities where an inter-activity constraint should be maintained and the intervals where a basic constraint remains invalid.A correctness criterion is defined for an interleaved execution of workflow instances using the constraints graphs. A concurrency control mechanism, namely Constraint Based Concurrency Control technique is developed based on the correctness criterion. The performance analysis shows the superiority of the proposed technique. Other possible approaches to the problem are also presented.     

Throughput-Optimal Sequences for Cyclically Operated Plants

In this paper, we present a method to determine globally optimal schedules for cyclically operated plants where activities have to be scheduled on limited resources. In cyclic operation, a large number of entities is processed in an identical time scheme. For strictly cyclic operation, where the time offset between entities is also identical for all entities, the objective of maximizing throughput is equivalent to the minimization of the cycle time. The resulting scheduling problem is solved by deriving a mixed integer optimization problem from a discrete event model. The model includes timing constraints as well as open sequence decisions for the activities on the resources. In an extension, hierarchical nesting of cycles is considered, which often allows for schedules with improved throughput. The method is motivated by the application to high throughput screening plants, where a specific combination of requirements has to be obeyed (e.g. revisited resources, absence of buffers, or time window constraints).     

An elective surgery scheduling problem considering patient priority

This paper addresses a scheduling problem where patients with different priorities are scheduled for elective surgery in a surgical facility, which has a limited capacity. When the capacity is available, patients with a higher priority are selected from the waiting list and put on the schedule. At the beginning of each period, a decision of the number of patients to be scheduled is made based on the trade-offs between the cost for overtime work and the cost for surgery postponement. A stochastic dynamic programming model is formulated to address this problem. A structural analysis of the proposed model is conducted to understand the properties of an optimal schedule policy. Based on the structural analysis, bounds on feasible actions are incorporated into a value iteration algorithm, and a brief computation experiment shows the improvement in computational efficiency. Numerical examples show that the consideration of patient priority results in significant differences in surgery schedules from the schedule that ignores the patient priority.     

Probe Backtrack Search for Minimal Perturbation in Dynamic Scheduling

This paperdescribes an algorithm designed to minimally reconfigure schedulesin response to a changing environment. External factors havecaused an existing schedule to become invalid, perhaps due tothe withdrawal of resources, or because of changes to the setof scheduled activities. The total shift in the start and endtimes of already scheduled activities should be kept to a minimum.This optimization requirement may be captured using a linearoptimization function over linear constraints. However, the disjunctivenature of the resource constraints impairs traditional mathematicalprogramming approaches. The unimodular probing algorithm interleavesconstraint programming and linear programming. The linear programmingsolver handles only a controlled subset of the problem constraints,to guarantee that the values returned are discrete. Using probebacktracking, a complete, repair-based method for search, thesevalues are simply integrated into constraint programming. Unimodularprobing is compared with alternatives on a set of dynamic schedulingbenchmarks, demonstrating its effectiveness.In the final discussion, we conjecture that analogous probebacktracking strategies may obtain performance improvements overconventional backtrack algorithms for a broad range of constraintsatisfaction and optimization problems.     

An optimal algorithm for scheduling soft aperiodic tasks indynamic-priority preemptive systems

The paper addresses the problem of jointly scheduling tasks with both hard and soft real time constraints. We present a new analysis applicable to systems scheduled using a priority preemptive dispatcher, with priorities assigned dynamically according to the EDF policy. Further, we present a new efficient online algorithm (the acceptor algorithm) for servicing aperiodic work load. The acceptor transforms a soft aperiodic task into a hard one by assigning a deadline. Once transformed, aperiodic tasks are handled in exactly the same way as periodic tasks with hard deadlines. The proposed algorithm is shown to be optimal in terms of providing the shortest aperiodic response time among fixed and dynamic priority schedulers. It always guarantees the proper execution of periodic hard tasks. The approach is composed of two parts: an offline analysis and a run time scheduler. The offline algorithm runs in pseudopolynomial time O(mn), where n is the number of hard periodic tasks and m is the hyperperiod/min deadline     

Weighted Constraint Aggregation in Fuzzy Optimization

Many practical optimization problems are characterized by some flexibility in the problem constraints, where this flexibility can be exploited for additional trade-off between improving the objective function and satisfying the constraints. Fuzzy sets have proven to be a suitable representation for modeling this type of soft constraints. Conventionally, the fuzzy optimization problem in such a setting is defined as the simultaneous satisfaction of the constraints and the goals. No additional distinction is assumed to exist amongst the constraints and the goals. This paper proposes an extension of this model for satisfying the problem constraints and the goals, where preference for different constraints and goals can be specified by the decision-maker. The difference in the preference for the constraints is represented by a set of associated weight factors, which influence the nature of trade-off between improving the optimization objectives and satisfying various constraints. Simultaneous weighted satisfaction of various criteria is modeled by using the recently proposed weighted extensions of (Archimedean) fuzzy t-norms. The weighted satisfaction of the problem constraints and goals are demonstrated by using a simple fuzzy linear programming problem. The framework, however, is more general, and it can also be applied to fuzzy mathematical programming problems and multi-objective fuzzy optimization.     

A Framework for Composing Real-Time Schedulers

In this paper, we present a framework for integrating real-time components in the same system, where each component has its own scheduling algorithm. There are two main reasons for this research: to allow maximum flexibility in the design of systems with different real-time activities and to reuse already existing applications without changing their scheduling policy. After defining the concept of component in our context, we present our methodology that is based on a two-level hierarchical scheduling paradigm. At the global level, a scheduler selects which component must be executed at each instant; the selected component then chooses which task has to be scheduled depending on its own scheduling strategy.     

Exact quantification of the sub-optimality of uniprocessor fixed priority pre-emptive scheduling

This paper examines the relative effectiveness of fixed priority pre-emptive scheduling in a uniprocessor system, compared to an optimal algorithm such as Earliest Deadline First (EDF). The quantitative metric used in this comparison is the processor speedup factor, equivalent to the factor by which processor speed needs to increase to ensure that any taskset that is schedulable according to an optimal scheduling algorithm can be scheduled using fixed priority pre-emptive scheduling, assuming an optimal priority assignment policy.     

MINIMIZING SERVICE DELAY OF APERIODIC TASKS IN DYNAMIC‐PRIORITY NON‐PREEMPTIVE HARD REAL‐TIME SYSTEMS

A scheduling technique is presented to minimize service delay of aperiodic tasks in hard real‐time systems that employ dynamic‐priority scheduling and do not allow task preemption. In a real‐time scheduling process, the execution of periodic tasks can be deferred as long as this does not cause other tasks to violate their time constraints. However, aperiodic tasks that usually have urgent missions should complete execution as early as possible. In this paper, it is assumed that aperiodic tasks also have time constraints. Thus, the problem of deciding whether an aperiodic task with an unpredictable arrival time can be scheduled successfully or not is difficult to solve because delaying periodic tasks may cause them to fail to meet their time constraints. We present a dynamic scheduling technique to solve this problem which makes use of the symmetric property of a schedule. The maximum possible idle slot is always reserved at every scheduling point so that aperiodic tasks can be serviced immediately if the reserved idle slot is big enough to service them. The proposed technique also maximizes utilization of idle slots by reserving them for the longest possible time span.     

Whole-body hierarchical motion and force control for humanoid robots

Robots acting in human environments usually need to perform multiple motion and force tasks while respecting a set of constraints. When a physical contact with the environment is established, the newly activated force task or contact constraint may interfere with other tasks. The objective of this paper is to provide a control framework that can achieve real-time control of humanoid robots performing both strict and non strict prioritized motion and force tasks. It is a torque-based quasi-static control framework, which handles a dynamically changing task hierarchy with simultaneous priority transitions as well as activation or deactivation of tasks. A quadratic programming problem is solved to maintain desired task hierarchies, subject to constraints. A generalized projector is used to quantitatively regulate how much a task can influence or be influenced by other tasks through the modulation of a priority matrix. By the smooth variations of the priority matrix, sudden hierarchy rearrangements can be avoided to reduce the risk of instability. The effectiveness of this approach is demonstrated on both a simulated and a real humanoid robot.     

A manpower levelling/project planning program

This paper presents the design of a program for monthly development of detailed daily schedules of manpower assignments. Scheduling is normally performed by a heuristic project scheduling routine. However, facilities for user intervention and overrides are incorporated. While actual limits are fixed by the availability of offline storage, the scheduler is designed to handle up to 40,000 activities, 200 resources and any number of different project networks. Through an extensive use of program overlays and linked data records on random access storage, core requirements are less than 17K 24 bit words for a compiled program.A multiple pass scheduling algorithm is used. Activities are partitioned into three sets depending upon their externally specified priority. Activities in the first set are scheduled first; the availability of resources is adjusted to reflect this schedule, the second set of activities is scheduled, etc. When properly used, this feature compensates for the common tendency of one-pass heuristic algorithms to schedule low priority, high slack activities as soon as excess resources are available, even if this ties up resources that in a few days are required to schedule more critical activities.This program is written in COBOL and is currently implemented on a Honeywell (GE) 465 computer as a subsystem of a larger management control system.     

A new Lagrangian relaxation algorithm for hybrid flowshop scheduling to minimize total weighted completion time

We investigate the problem of scheduling n jobs in s-stage hybrid flowshops with parallel identical machines at each stage. The objective is to find a schedule that minimizes the sum of weighted completion times of the jobs. This problem has been proven to be NP-hard. In this paper, an integer programming formulation is constructed for the problem. A new Lagrangian relaxation algorithm is presented in which precedence constraints are relaxed to the objective function by introducing Lagrangian multipliers, unlike the commonly used method of relaxing capacity constraints. In this way the relaxed problem can be decomposed into machine type subproblems, each of which corresponds to a specific stage. A dynamic programming algorithm is designed for solving parallel identical machine subproblems where jobs may have negative weights. The multipliers are then iteratively updated along a subgradient direction. The new algorithm is computationally compared with the commonly used Lagrangian relaxation algorithms which, after capacity constraints are relaxed, decompose the relaxed problem into job level subproblems and solve the subproblems by using the regular and speed-up dynamic programming algorithms, respectively. Numerical results show that the new Lagrangian relaxation method produces better schedules in much shorter computation time, especially for large-scale problems.     

Possibility theory in constraint satisfaction problems: Handling priority, preference and uncertainty

In classical Constraint Satisfaction Problems (CSPs) knowledge is embedded in a set of hard constraints, each one restricting the possible values of a set of variables. However constraints in real world problems are seldom hard, and CSP's are often idealizations that do not account for the preference among feasible solutions. Moreover some constraints may have priority over others. Lastly, constraints may involve uncertain parameters. This paper advocates the use of fuzzy sets and possibility theory as a realistic approach for the representation of these three aspects. Fuzzy constraints encompass both preference relations among possible instantiations and priorities among constraints. In a Fuzzy Constraint Satisfaction Problem (FCSP), a constraint is satisfied to a degree (rather than satisfied or not satisfied) and the acceptability of a potential solution becomes a gradual notion. Even if the FCSP is partially inconsistent, best instantiations are provided owing to the relaxation of some constraints. Fuzzy constraints are thus flexible. CSP notions of consistency and k-consistency can be extended to this framework and the classical algorithms used in CSP resolution (e.g., tree search and filtering) can be adapted without losing much of their efficiency. Most classical theoretical results remain applicable to FCSPs. In the paper, various types of constraints are modelled in the same framework. The handling of uncertain parameters is carried out in the same setting because possibility theory can account for both preference and uncertainty. The presence of uncertain parameters leads to ill-defined CSPs, where the set of constraints which defines the problem is not precisely known.     

Dinkelbach NCUT: An Efficient Framework for Solving Normalized Cuts Problems with Priors and Convex Constraints

In this paper, we propose a novel framework, called Dinkelbach NCUT (DNCUT), which efficiently solves the normalized graph cut (NCUT) problem under general, convex constraints, as well as, under given priors on the nodes of the graph. Current NCUT methods use generalized eigen-decomposition, which poses computational issues especially for large graphs, and can only handle linear equality constraints. By using an augmented graph and the iterative Dinkelbach method for fractional programming (FP), we formulate the DNCUT framework to efficiently solve the NCUT problem under general convex constraints and given data priors. In this framework, the initial problem is converted into a sequence of simpler sub-problems (i.e. convex, quadratic programs (QP’s) subject to convex constraints). The complexity of finding a global solution for each sub-problem depends on the complexity of the constraints, the convexity of the cost function, and the chosen initialization. However, we derive an initialization, which guarantees that each sub-problem is a convex QP that can be solved by available convex programming techniques. We apply this framework to the special case of linear constraints, where the solution is obtained by solving a sequence of sparse linear systems using the conjugate gradient method. We validate DNCUT by performing binary segmentation on real images both with and without linear/nonlinear constraints, as well as, multi-class segmentation. When possible, we compare DNCUT to other NCUT methods, in terms of segmentation performance and computational efficiency. Even though the new formulation is applied to the problem of spectral graph-based, low-level image segmentation, it can be directly applied to other applications (e.g. clustering).     

A solution approach to find the critical path in a time-constrained activity network

Finding the longest path in an activity network, where time constraints are attached to activities, is generalized from the traditional critical path problem. Time constraints have attracted much research interest in recent years, because they can be used to represent a large set of real situations, arising not only in the field of the project management.In this paper, we propose a general approach for finding the critical path in a deterministic activity-on-the-arc network, considering three different types of time constraints. The first one is the time-window constraint, which imposes that an activity can start only in a predefined time interval, whereas no constraints are imposed on the activity completion time. The second one is the time-schedule constraint, which assumes that an activity can start its execution at one of the pre-specified instants of time. The third one is the time-switch constraint, which imposes a specified starting time on the project activities and forces them to be inactive during specified time periods.The algorithm introduced in this paper has been developed by redefining and combining together two procedures well-known in the scientific literature. The former, proposed by Chen, Rinks and Tang in 1997, can be used for finding the critical path in an activity network where time-schedule and time-window constraints are considered. The latter, proposed by Yang and Chen in 2000, can be applied in activity networks with only time-switch constraints.The method, developed in this paper, can be used to find the critical path in all the practical situations, in which the aforementioned time constraints are taken into account simultaneously. The proposed approach has been coded in Java and has been validated by considering two sets of instances: the former has been taken from the public domain project scheduling problem library, developed by Kolisch and Sprecher in 1997, whereas the latter consists of randomly generated activity networks.The computational results collected are very promising, showing that the solution process for the considered instances required at most few seconds, using a commercial Pentium class PC.     

效率异质型员工项目调度算法研究

研究了员工具有异质效率、最小化项目工期的项目调度问题,并建立了相应的整数线性规划模型。为解决此NP-hard问题,提出了基于优先规则的启发式算法,其在每次迭代中根据优先约束和优先规则选择优先任务员工对以分配任务,直至所有任务都完成调度。通过应用启发式算法生成初始调度,选用交换邻域结构和插入邻域结构产生邻域调度,并使用改进的前向递归算法求解目标函数值,构造出混合模拟退火算法。数值实验显示该算法能快速准确地进行寻优。     

Real-time visual tracking of complex structures

Presents a framework for three-dimensional model-based tracking. Graphical rendering technology is combined with constrained active contour tracking to create a robust wire-frame tracking system. It operates in real time at video frame rate (25 Hz) on standard hardware. It is based on an internal CAD model of the object to be tracked which is rendered using a binary space partition tree to perform hidden line removal. A Lie group formalism is used to cast the motion computation problem into simple geometric terms so that tracking becomes a simple optimization problem solved by means of iterative reweighted least squares. A visual servoing system constructed using this framework is presented together with results showing the accuracy of the tracker. The paper then describes how this tracking system has been extended to provide a general framework for tracking in complex configurations. The adjoint representation of the group is used to transform measurements into common coordinate frames. The constraints are then imposed by means of Lagrange multipliers. Results from a number of experiments performed using this framework are presented and discussed     

MULTI-AGENT INTELLIGENT PLANNING ARCHITECTURE FOR PEOPLE LOCATION AND ORIENTATION USING RFID

The new advances in information communications technology (ICT) allow expanding the electronic communication networks into an objective environment. These techniques can be used to help users who have to go to a building with several floors, corridors, and departments to get its right location and orientation. The problem gets more complicated when there are a high number of visitors with temporal constraints. Typically the lack of information and signs to guide the user complicates this task. A typical example is a hospital, where patients have scheduled doctor's appointments and in some cases movement problems (handicaps). A possibility for solving this problem is to equip the building with an intelligent system for user detection and orientation.

In this article we present a solution that uses a detection and a location system based on wireless technology and artificial intelligence techniques to plan and inform about the paths the user can follow.     

Local reasoning and knowledge compilation for efficient temporal abduction

Generating abductive explanations is the basis of several problem solving activities such as diagnosis, planning, and interpretation. Temporal abduction means generating explanations that do not only account for the presence of observations, but also for temporal information on them, based on temporal knowledge in the domain theory. We focus on the case where such a theory contains temporal constraints that are required to be consistent with temporal information on observations. Our aim is to propose efficient algorithms for computing temporal abductive explanations. Temporal constraints in the theory and in the observations can be used actively by an abductive reasoner in order to prune inconsistent candidate explanations at an early stage during their generation. However, checking temporal constraint satisfaction frequently generates some overhead. We analyze two incremental ways of making this process efficient. First we show how, using a specific class of temporal constraints (which is expressive enough for many applications), such an overhead can be reduced significantly, yet preserving a full pruning power. In general, the approach does not affect the asymptotic complexity of the problem, but it provides significant advantages in practical cases. We also show that, for some special classes of theories, the asymptotic complexity is also reduced. We then show how, compiled knowledge based on temporal information, can be used to further improve the computation, thus, extending to the temporal framework previous results in the case of atemporal abduction. The paper provides both analytic and experimental evaluations of the computational advantages provided by our approaches.     

一种有限优先级的静态优先级分配算法

静态优先级调度在实时系统中得到了广泛应用.然而,静态优先级调度受到系统支持的优先级个数的限制.当任务的个数大于优先级个数时,需要将多个任务映射到同一个优先级.针对优先级个数有限的情况,给出了在截止期限大于周期时任务可调度的充分必要条件,并提出了基于有限优先级的静态优先级分配算法(AGP).AGP算法对于基本任务集合是最优的静态优先级分配算法.其最优性表现在,所需的优先级个数最小,并且若采用AGP算法不可调度某个任务集,则采用其他静态优先级分配算法也不可调度该任务集.模拟结果表明,AGP算法的可调度性要远远大于常量法.AGP算法对于解决在嵌入式实时系统中任务的优先级分配问题具有重要意义.