Managing uncertainty in major equipment procurement in engineering projects

A better management of time uncertainty in major equipment procurement in engineering construction projects can significantly contribute to project performance. A survey study shows that time buffer is a popularly used approach to protect project schedule from activity duration variation and uncertainty. The problem is that there are repetitive time allowances inserted in the procurement supply chain process and these time buffers are used ineffectively, thus leading to considerable time wastage. Relevant lessons from supply chain management and critical chain project management are combined and applied to create an enhanced critical supply chain management model for major equipment procurement to achieve better management of time uncertainty. This model does not perceive uncertainty purely as a threat, but also as an opportunity to reduce procurement cycle times.     

Techniques for the mathematical analysis of neural networks

This expository paper covers the following topics: (1) a very brief introduction to neural networks for those unfamiliar with the basic concepts; (2) an equality brief survey of various mathematical approaches to neural systems with an emphasis on approximation theory; (3) an algorithmic approach to the analysis of networks developed by this author using the tools of numerical linear algebra. This approach is novel and was first proposed by the author in (1990).

A detailed analysis of one popular algorithm (the delta rule) will be given, indicating why one implementation leads to a stable numerical process, whereas an initially attractive variant (essentially a form of steepest descent) does not. Similar considerations apply to the backpropagation algorithm. The effect of filtering and other preprocessing of the input data will also be discussed systematically, with a new result on the effect of linear filtering on the rate of convergence of the delta rule.     

Neural network as a simulation metamodel in economic analysis of risky projects

An artificial neural network (ANN) model for economic analysis of risky projects is presented in this paper. Outputs of conventional simulation models are used as neural network training inputs. The neural network model is then used to predict the potential returns from an investment project having stochastic parameters. The nondeterministic aspects of the project include the initial investment, the magnitude of the rate of return, and the investment period. Backpropagation method is used in the neural network modeling. Sigmoid and hyperbolic tangent functions are used in the learning aspect of the system. Analysis of the outputs of the neural network model indicates that more predictive capability can be achieved by coupling conventional simulation with neural network approaches. The trained network was able to predict simulation output based on the input values with very good accuracy for conditions not in its training set. This allowed an analysis of the future performance of the investment project without having to run additional expensive and time-consuming simulation experiments.     

Classical versus Bayesian risks in acceptance sampling: a sensitivity analysis

Assuming a beta prior distribution on the fraction defective, $p$ , failure-censored sampling plans for Weibull lifetime models using classical (or average) and Bayesian (or posterior) producer’s and consumer’s risks are designed to determine the acceptability of lots of a given product. The average risk criterion provides a certain assurance that good (bad) lots will be accepted (rejected), whereas the posterior risk criterion provides a determined confidence that an accepted (rejected) lot is indeed good (bad). The performance of classical and Bayesian risks are analyzed in developing sampling plans when the lifetime variable follows the Weibull distribution. Several figures and tables illustrate the sensitivity of the risks and optimal sample sizes for selected censoring levels and specifications according to the available prior information on $p$ . The analysis clarifies the distinction among the different risks for a given sampling plan, and the effect of the prior knowledge on the required sample size. The study shows that, under uncertainty in the prior variance of $p$ , the designs using Bayesian risks are more appropriate.     

Techniques for the assessment of influence in DEA

In this paper, we address the problem of assessing the influence of a given unit in the sample when evaluating efficiency by using both radial and nonradial DEA models. According to the values of some new measures we define, the efficient units exhibiting a higher deal of influence will be classified for a further checking. Then the analyst will have to decide whether they are contaminated by data errors or not. The interest of these techniques lies in that they provide rules to detect influential observations which may avoid data checking, which is often costly (particularly with large samples).     

On the Haezendonck-Goovaerts risk measure for extreme risks

In this paper, we are interested in the calculation of the Haezendonck-Goovaerts risk measure, which is defined via a convex Young function and a parameter q∈(0,1) representing the confidence level. We mainly focus on the case in which the risk variable follows a distribution function from a max-domain of attraction. For this case, we restrict the Young function to be a power function and we derive exact asymptotics for the Haezendonck-Goovaerts risk measure as q↑1. As a subsidiary, we also consider the case with an exponentially distributed risk variable and a general Young function, and we obtain an analytical expression for the Haezendonck-Goovaerts risk measure.     

Bounds for functions of multivariate risks

Li et al. [Distributions with Fixed Marginals and Related Topics, vol. 28, Institute of Mathematics and Statistics, Hayward, CA, 1996, pp. 198-212] provide bounds on the distribution and on the tail for functions of dependent random vectors having fixed multivariate marginals. In this paper, we correct a result stated in the above article and we give improved bounds in the case of the sum of identically distributed random vectors. Moreover, we provide the dependence structures meeting the bounds when the fixed marginals are uniformly distributed on the k-dimensional hypercube. Finally, a definition of a multivariate risk measure is given along with actuarial/financial applications.     

Objective Bayesian analysis for competing risks model with Wiener degradation phenomena and catastrophic failures

In this paper, the objective Bayesian method is applied to investigate the competing risks model involving both catastrophic and degradation failures. By modeling soft failure as the Wiener degradation process, and hard failures as a Weibull distribution, we obtain the noninformative priors (Jefferys prior and two reference priors) for the parameters. Moreover, we show that their posterior distributions have good properties and we propose Gibbs sampling algorithms for the Bayesian inference based on the Jefferys prior and two reference priors. Some simulation studies are conducted to illustrate the superiority of objective Bayesian method. Finally, we apply our methods to two real data examples and compare the objective Bayesian estimates with the other estimates.     

Wavelet Techniques for the Fictitious-Domain-Lagrange-Multiplier-Approach

We consider second order elliptic boundary value problems when essential boundary conditions are enforced with the aid of Lagrange multipliers. This is combined with a fictitious domain approach into which the physical domain is embedded. The resulting saddle point problem will be discretized in terms of wavelets, resulting in an operator equation in ₂. Stability of the discretization and consequently the uniform boundedness of the condition number of the finite-dimensional operator independent of the discretization is guaranteed by an appropriate LBB condition. For the iterative solution of the saddle point system, an incomplete Uzawa algorithm is employed. It can be shown that the iterative scheme combined with a nested iteration strategy is asymptotically optimal in the sense that it provides the solution up to discretization error on discretization level J in an overall amount of iterations of order O(N _J ), where N _J is the number of unknowns on level J. Finally, numerical results are provided.     

Bounds for the sum of dependent risks having overlapping marginals

We describe several analytical and numerical procedures to obtain bounds on the distribution function of a sum of n dependent risks having fixed overlapping marginals. As an application, we produce bounds on quantile-based risk measures for portfolios of financial and actuarial interest.     

Tail asymptotics for the sum of two heavy-tailed dependent risks

Let X ₁ , X ₂ denote positive heavy-tailed random variables with continuous marginal distribution functions F ₁ and F ₂, respectively. The asymptotic behavior of the tail of X ₁ +X ₂ is studied in a general copula framework and some bounds and extremal properties are provided. For more specific assumptions on F ₁ , F ₂ and the underlying dependence structure of X ₁ and X ₂, we survey explicit asymptotic results available in the literature and add several new cases.Supported by the Austrian Science Fund Project P-18392.     

Lagrangian heuristics for scheduling new product development projects in the pharmaceutical industry

To stay ahead of their competition, pharmaceutical firms must make effective use of their new product development (NPD) capabilities by efficiently allocating its analytical, clinical testing and manufacturing resources across various drug development projects. The resulting project scheduling problems involve coordinating hundreds of testing and manufacturing activities over a period of several quarters. Most conventional integer programming approaches are computationally impractical for problems of this size, while priority rule-driven heuristics seldom provide consistent solution quality. We propose a Lagrangian decomposition (LD) heuristic that exploits the special structure of these problems. Some resources (typically manpower) are shared across all on-going projects while others (typically equipment) are specific to individual project categories. Our objective function is a weighted discounted cost expressed in terms of activity completion times. The LD heuristics were subjected to a comprehensive experimental study based on typical operational instances. While the conventional “Reward–Risk” priority rule heuristic generates duality gaps between 47–58%, the best LD heuristic achieves duality gaps between 10–20%. The LD heuristics also yield makespan reductions of over 30% over the Reward–Risk priority rule.     

The role of feedback dynamics in disruption and delay on the nature of disruption and delay (D&D) in major projects

The idea that small disruptions and delays can cause serious consequences to the life of a major project, well beyond that which might be easily attributed to their direct impact, is well established. Nevertheless, the nature of this ‘delay and disruption’ is still not fully understood. This paper discusses some of the issues and difficulties in gaining a full understanding. In particular it presents the variety of ways in which disruptions occur, and the variety of consequences that may unfold. It also focuses attention on a number of issues that arise when ‘normal’ methods of analysis of complex projects might be used, for example, the analysis and costing of change orders and the use of network analysis. The role of dynamic feedback and the ‘portfolio effect’ is introduced, particularly with reference to project acceleration and changing productivity.     

A classification tree approach for the modeling of competing risks in discrete time

Advances in Data Analysis and Classification - Cause-specific hazard models are a popular tool for the analysis of competing risks data. The classical modeling approach in discrete time consists of...     

Optimal policies for the sizing and timing of software maintenance projects

We present a model to determine the optimal point for maintaining a software application. We also address the question: given that a maintenance project has been initiated, should maintenance effort continue till the project is completed? Most previous literature has implicitly assumed that it is optimal to complete a maintenance project once it has been initiated. We analyze two policies: a work-based policy and a time-based policy. In the work-based policy, a fixed amount of work needs to be completed, and the time taken to accomplish the work is random. In the time-based policy, a fixed amount of time is allocated to maintenance, but a random amount of work is completed. We examine similarities and differences between the above two policies and provide insights into the management of software maintenance projects. A key insight of this study is that under a variety of situations, partial maintenance is suboptimal.     

Performance of the heuristic procedures for constrained projects with progress payments

All large scale resource constrained projects involve cash flows occurring during their life cycle. Several recent studies consider the problem of scheduling projects to maximise the net present value (NPV) of these cash flows. Their basic common assumption is that cash flows are mainly associated with specific events and they occur at event realisation times. An alternative assumption, which can be more realistic, is that cash inflows occur periodically, for example every month, as progress payments. This article considers the problem of maximising NPV given the alternative assumption. Three different heuristic rules are developed. The performance of these heuristic rules is analysed through a full factorial experiment with 108 scheduling conditions. The results indicate that three rules provide near-optimal schedules with respect to NPV maximisation while producing time schedules that do not delay the project completion time extensively.     

Final-offer arbitration in major league baseball: A nonparametric analysis

This study extends the non-parametric methodology for empirical efficiency analysis to allow for a double frontier based on perspective and applies the model to final-offer arbitration in major league baseball. Arbitration eligible players perceive their worth relative to other players who earn more with no better performance. Owners, on the other hand, perceive a player's value relative to other players performing as well with lower salaries. The two different perspectives give rise to different perceived frontiers. The purpose of this paper is to analyze arbitration using this approach.     

Business models for public emission reduction projects

This study investigates two business models for public emission reduction projects that promote low-carbon behavior, issue corresponding carbon credits, and facilitate the trade of carbon credits at the individual level. We construct two dynamic game-theoretical models to investigate the project owner's business model selection strategy, optimal operational decisions, and the effectiveness of the two business models in reducing carbon emissions.