基于时间Petri网的实时系统低能耗高层综合

以时间Petri网为模型,验证系统功能性和实时性,在此基础上,提出了一种由子任务的能耗变化率驱动的启发式能耗优化算法,并针对一类特殊形式的网模型——可组合时间Petri网,设计了相应的简化算法．实验说明,上述算法时间复杂度低,且优化效果接近最优值,能够为实时系统低能耗高层综合提供有力支持．     

Reachability analysis of real-time systems using time Petri nets

Time Petri nets (TPNs) are a popular Petri net model for specification and verification of real-time systems. A fundamental and most widely applied method for analyzing Petri nets is reachability analysis. The existing technique for reachability analysis of TPNs, however, is not suitable for timing property verification because one cannot derive end-to-end delay in task execution, an important issue for time-critical systems, from the reachability tree constructed using the technique. In this paper, we present a new reachability based analysis technique for TPNs for timing property analysis and verification that effectively addresses the problem. Our technique is based on a concept called clock-stamped state class (CS-class). With the reachability tree generated based on CS-classes, we can directly compute the end-to-end time delay in task execution. Moreover, a CS-class can be uniquely mapped to a traditional state class based on which the conventional reachability tree is constructed. Therefore, our CS-class-based analysis technique is more general than the existing technique. We show how to apply this technique to timing property verification of the TPN model of a command and control (C2) system.     

An investigation of the sensitivity of DEA to data errors

The predictive value of a common measure of efficiency and the robustness of the data envelopment analysis (DEA) technique is examined when statistical noise is present in the data. Inferences are drawn from a hypothetical example regarding the potential limitations of the efficiency measure and pitfalls in both the single- and multi-stage applications of DEA. We propose a simple procedure to investigate the robustness of DEA results. The procedure maintains the relative computational simplicity of DEA and is easy to apply and interpret. Using this procedure, we examine the robustness of the results reported in two published DEA studies and find that, indeed, pitfalls occur in practical applications. We conclude with recommendations for researchers applying the technique and implications for managers.     

Combining DEA with location analysis for the effective consolidation of services in the health sector

One of the most important activities of strategic planning in a health-care system is the effective allocation of scarce resources. Most of such strategies are attempting to create more efficient systems based on better organizational and management structures. Therefore, it is necessary to develop systematic models and evaluation methods that will support a strategic planning process that addresses issues such as the location of services and the effective use of resources such as equipment, funds or workforce. Such modeling approaches need to quantify the effect of changes in the location of providers, the opening or closure of providers, and the dynamic transformations of the services offered at each provider. In this paper we propose a methodology that takes into account health service provider efficiencies based on multiple measures. These efficiencies are then employed to determine health providers' locations and service allocations, which include new services distribution as well as existing services redistribution. This approach employs data envelopment analysis (DEA) and integer programming (IP) location allocation models and can be used as both an immediate evaluation tool and a long-term planning aid.     

基于Time Petri Nets的UML时序图分析

引入一种称为Clock的变迁,用来改进TPNs,讨论了如何将时序图转换为基于Clock变迁的TPNs,使这种TPNs能正确反映时序图的流程和时间约束。最后,利用普通Petri网的可达性分析技术对时序图模型进行了分析和验证。     

Assessing the efficiency of supply chain scheduling algorithms using data envelopment analysis

Where different supply chain planning algorithms are used, generally similar results may pose some challenges on the differentiating powers of evaluating different production schedules because of the increasing complexity of a supply chain network structure. For the comparison purpose, performance evaluation of different supply chain planning algorithms aims to use different supply chains models with different demands, capacities, and commonality through efficiency perspective by using a modified network rational data envelopment analysis (DEA) model. The proposed DEA model has the abilities: (1) to treat only undesirable outputs that exist without normal output, and the situation where input and output are both zero by introducing two new parameters to denote the maximum inventory and amount of delayed demands of a given node in a given time period; and (2) to evaluate the effect of the undesirable outputs/inputs on efficiency with assumption that they leave the system at the end of the current time period and re-enter the system at the beginning of the next time period. To prove the effectiveness of this DEA model, eighteen scenarios with different demands, capacities, and multiple periods are compared. In addition, this study tests the DEA model on a wafer testing/probing operation of a leading global semiconductor manufacturing and testing company in Taiwan by internal supply chain perspective. Results show that the DEA model proposed in this study can be used to assess the efficiency of a real-world operation with undesirable outputs/inputs, such as inventory and delayed demands.     

Compositional time Petri nets and reduction rules

This paper introduces compositional time Petri net (CTPN) models. A CTPN is a modularized time Petri net (TPN), which is composed of components and connectors. The paper also proposes a set of component-level reduction rules for TPNs. Each of these reduction rules transforms a TPN component to a very simple one while maintaining the net's external observable timing properties. Consequently, the proposed method works at a coarse level rather than at an individual transition level. Therefore, one requires significantly fewer applications to reduce the size of the TPN under analysis than those existing ones for TPNs. The use and benefits of CTPNs and reduction rules are illustrated by modeling and analyzing the response time of a command and control system to its external arriving messages.     

A combined neural network and DEA for measuring efficiency of large scale datasets

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement of the efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper proposes a neural network back-propagation Data Envelopment Analysis to address this problem for the very large scale datasets now emerging in practice. Neural network requirements for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of large datasets. Finally, the back-propagation DEA algorithm is applied to five large datasets and compared with the results obtained by conventional DEA.     

Decision models for sustainable groundwater planning and control

An integrated approach to the sustainable planning and control of groundwater resources is presented. Physical/chemical models describing the groundwater system (i.e., the water flows and physical and chemical behavior of the pollutants) are embedded as constraints in the optimization problem. Two main kinds of decision problems are considered: control problems, conditioned by real time information, and planning problems. A specific case study relevant to the application of a receding-horizon control scheme, with the objective of optimizing water extraction from a set of wells, is presented, considering different demand scenarios.     

Optimal profit-maximizing system design data envelopment analysis models

This research introduces a new type of data envelopment analysis (DEA) model termed the optimal system design (OSD) DEA model. Conventional DEA models evaluate DMUs’ performances given their known input and output data. The OSD DEA models take this one step further. They optimally design a DMU’s resource allocation in terms of profit maximization given the DMU’s total available budget. The need to design optimal systems is quite common and is sometimes necessary in practice. In actual fact, this study demonstrates that through the OSD DEA models, we can provide DMUs with more information than optimal portfolios of resources such as optimal budgets and budget congestion, i.e., the more the budget is consumed, the less the maximal profit. The proposed OSD DEA models are linear programs, and thus can be solved by the standard LP solvers to obtain DMUs’ optimal designs. However, to derive the DMUs’ corresponding optimal budgets, and to verify if the DMUs provide evidence of budget congestion, we need to modify the solvers, which may not be trivial. Therefore, this study exploits the special structures of the models to develop a simple solution method that can directly not only derive both a DMU’s optimal design and optimal budget, but can also check for the existence of budget congestion.     

An intelligent decision support system for forecasting and optimization of complex personnel attributes in a large bank

Developing decision support system (DSS) can overcome the issues with personnel attributes and specifications. Personnel specifications have greatest impact on total efficiency. They can enhance total efficiency of critical personnel attributes. This study presents an intelligent integrated decision support system (DSS) for forecasting and optimization of complex personnel efficiency. DSS assesses the impact of personnel efficiency by data envelopment analysis (DEA), artificial neural network (ANN), rough set theory (RST), and K-Means clustering algorithm. DEA has two roles in this study. It provides data to ANN and finally it selects the best reduct through ANN results. Reduct is described as a minimum subset of features, completely discriminating all objects in a data set. The reduct selection is achieved by RST. ANN has two roles in the integrated algorithm. ANN results are basis for selecting the best reduct and it is used for forecasting total efficiency. Finally, K-Means algorithm is used to develop the DSS. A procedure is proposed to develop the DSS with stated tools and completed rule base. The DSS could help managers to forecast and optimize efficiencies by selected attributes and grouping inferred efficiency. Also, it is an ideal tool for careful forecasting and planning. The proposed DSS is applied to an actual banking system and its superiorities and advantages are discussed.     

A procedure for large-scale DEA computations

Data envelopment analysis (DEA), a performance evaluation method, measures the relative efficiency of a particular decision making unit (DMU) against a peer group. Most popular DEA models can be solved using standard linear programming (LP) techniques and therefore, in theory, are considered as computationally easy. However, in practice, the computational load cannot be neglected for large-scale—in terms of number of DMUs—problems. This study proposes an accelerating procedure that properly identifies a few “similar” critical DMUs to compute DMU efficiency scores in a given set. Simulation results demonstrate that the proposed procedure is suitable for solving large-scale BCC problems when the percentage of efficient DMUs is high. The computational benefits of this procedure are significant especially when the number of inputs and outputs is small, which are most widely reported in the literature and practices.     

An automated multi-step calibration procedure for a river system model

Predicted climate change impact on future water availability in the Murray–Darling Basin (MDB) has highlighted the need for a whole of basin model that incorporates various physical and management characteristics for planning and operational purposes. Modelling platforms such as eWater Source Integrated Modelling System (Source) offer a useful framework in this regard, but at present lack automated calibration techniques to parameterise river system models.This paper presents an automated river system calibration procedure which is robust, repeatable, transparent and systematic. The procedure allows for river network calibration (as opposed to isolated reach by reach calibration), since this has more utility for basin planning and prediction. The calibration procedure routs upstream flow, estimates ungauged inputs via rainfall–runoff (RR) models, and estimates flow based split (distributary) functions and loss functions in complex river systems.This procedure was tested in the Northern Murray–Darling Basin (MDB) and results from the Border Rivers catchment are presented. The results from the Border Rivers case study demonstrate the applicability of the procedure with median calibration and evaluation NSE values of 0.88 and 0.79, respectively. The use of this procedure in the Border Rivers region has highlighted the likelihood of changing stream channel connections at higher flows in the lower reaches of the river network.     

Symbolic unfolding of parametric stopwatch Petri nets

We address the problem of unfolding safe parametric stopwatch time Petri nets (PSwPNs), i.e., safe time Petri nets (TPNs) possibly extended with time parameters and stopwatches. We extend the notion of branching process to account for the dates of the occurrences of events and thus define a symbolic unfolding for PSwPNs. In the case of TPNs we also propose a method based on our so-called time branching processes to compute a finite complete prefix of the symbolic unfolding. The originality of our work relies on a precise handling of direct conflicts between events, and the analysis of their effects on the constraints between the firing dates of those events.     

Generalization of I-O-W model for waste management policy analysis and national planning

The generalization of the input-output-waste (I-O-W) model for different types of resources is presented in this paper as a new paradigm of national consistency modelling. This incorporates waste management (WM) policy analysis in a national planning context. The I-O-W model is an extension of the input-output model that formulates the waste aspects explicitly. The systematic analysis of intersectoral flow of different resources and wastes is important at the macro level for preparing resource plans that can be further integrated to frame overall economic plans. The paper briefly overviews the I-O-W model and generalizes it for five basic resources, i.e. material, energy, manpower, capital and services. The resource balance has been established by treating input to be equal to output plus waste for each resource independently as well as for the overall flow. The inputs required and the possible outputs for the generalized I-O-W model are listed. An illustrative example has been given that works out the direct, indirect and total energy. flows for a hypothetical economy. The scheme of integration is presented for overall WM policy analysis and national planning.     

‘True Concurrency’ Semantics for Time Petri Nets with Weak Time and Persistent Atomic Policies

Programming and Computer Software - Dense-Time Petri nets (TPNs), where time intervals for transition firings are assigned, are now a well-established model, which is used to describe and study...     

The voting analytic hierarchy process method for discriminating among efficient decision making units in data envelopment analysis

Making optimal use of available resources has always been of interest to humankind, and different approaches have been used in an attempt to make maximum use of existing resources. Limitations of capital, manpower, energy, etc., have led managers to seek ways for optimally using such resources. In fact, being informed of the performance of the units under the supervision of a manager is the most important task with regard to making sensible decisions for managing them. Data envelopment analysis (DEA) suggests an appropriate method for evaluating the efficiency of homogeneous units with multiple inputs and multiple outputs. DEA models classify decision making units (DMUs) into efficient and inefficient ones. However, in most cases, managers and researchers are interested in ranking the units and selecting the best DMU. Various scientific models have been proposed by researchers for ranking DMUs. Each of these models has some weakness(es), which makes it difficult to select the appropriate ranking model. This paper presents a method for ranking efficient DMUs by the voting analytic hierarchy process (VAHP). The paper reviews some ranking models in DEA and discusses their strengths and weaknesses. Then, we provide the method for ranking efficient DMUs by VAHP. Finally we give an example to illustrate our approach and then the new method is employed to rank efficient units in a real world problem.     

Reachability Problems and Abstract State Spaces for Time Petri Nets with Stopwatches

Several extensions of Time Petri nets (TPNs) have been proposed for modeling suspension and resumption of actions in timed systems. We first introduce a simple class of TPNs extended with stopwatches (SwTPNs), and present a semi-algorithm for building exact representations of the behavior of SwTPNs, based on the known state class method for Time Petri nets. Then, we prove that state reachability in SwTPNs and all similar models is undecidable, even when bounded, which solves an open problem. Finally, we discuss overapproximation methods yielding finite abstractions of their behavior for a subclass of bounded SwTPNs, and propose a new one based on a quantization of the polyhedra representing temporal information. By adjusting a parameter, the exact behavior can be approximated as closely as desired. The methods have been implemented, experiments are reported.     

A Computationally Efficient, Consistent Bootstrap for Inference with Non-parametric DEA Estimators

We develop a tractable, consistent bootstrap algorithm for inference about Farrell?CDebreu efficiency scores estimated by non-parametric data envelopment analysis (DEA) methods. The algorithm allows for very general situations where the distribution of the inefficiencies in the input-output space may be heterogeneous. Computational efficiency and tractability are achieved by avoiding the complex double-smoothing procedure in the algorithm proposed by Kneip et al. (Econometric Theory 24:1663?C1697, 2008). In particular, we avoid technical difficulties in the earlier algorithm associated with smoothed estimates of a density with unknown, nonlinear, multivariate bounded support requiring complicated reflection methods. The new procedure described here is relatively simple and easy to implement: for particular values of a pair of smoothing parameters, the computational complexity is the same as the (inconsistent) naive bootstrap. The resulting computational speed allows the bootstrap to be iterated in order to optimize the smoothing parameters. From a practical viewpoint, only standard packages for computing DEA efficiency estimates, i.e., solving linear problems, are required for implementation. The performance of the method in finite samples is illustrated through some simulated examples.