Determining production level under uncertainty using fuzzy simulation and bootstrap technique, a case study

This paper is aimed to report the consequences in the use of the well-known bootstrap technique in single-phase current-fed full-bridge resonant inverters (SPCFFBRIs) operating with and without reactive energy. Based on both simulations (using the PSPICE[Formula: see text] software) and experiments it is shown that this technique is not feasible for this type of inverters when the load is predominantly reactive. The bootstrap technique conveniently operates when the load is purely resistive or resonant. The unfeasibility in the use of the bootstrap technique with reactive or nonresonant loads comes from the fact that the load voltage is applied to the bootstrap capacitor and, consequently, to the gate of the floating MOSFET, producing an imminent damage both in the control circuit and in the floating MOSFET. Therefore, other alternatives for feeding the floated MOSFETs must be considered.

Several different simulation techniques, such as discrete event simulation (DES), system dynamics (SD) and agent-based modelling (ABM), have been used to model complex construction systems such as construction processes and project management practices; however, these techniques do not take into account the subjective uncertainties that exist in many construction systems. Integrating fuzzy logic with simulation techniques enhances the capabilities of those simulation techniques, and the resultant fuzzy simulation models are then capable of handling subjective uncertainties in complex construction systems. The objectives of this chapter are to show how to integrate fuzzy logic and simulation techniques in construction modelling and to provide methodologies for the development of fuzzy simulation models in construction. In this chapter, an overview of simulation techniques that are used in construction is presented. Next, the advancements that have been made by integrating fuzzy logic and simulation techniques are introduced. Methodologies for developing fuzzy simulation models are then proposed. Finally, the process of selecting a suitable simulation technique for each particular aspect of construction modelling is discussed.

In real situations particularly in business processes, the probability distributions are not known or imprecise and conventional modeling approaches may be questionable. Fuzzy simulation is capable of modeling uncertainties and vagueness. This paper introduces a novel approach based of fuzzy business process simulation (FBPS) and data envelopment analysis (DEA) for re-engineering, assessment and optimization of a purchasing management system. An actual purchasing system is studied and various data were collected through standard methods. The users of the purchasing management verified that the goals of a re-engineering model are to create a process-oriented approach in order to reduce purchasing process cycle time, faster customer response time and lower cost. The results of fuzzy simulation approach are compared with conventional simulation by t-test. It is shown the performance of the actual system is modeled and improved by the fuzzy simulation approach of this study. The results of fuzzy simulation are more accurate and similar to real situations than previous results by conventional simulation. In addition, DEA is used to identify optimal solution among proposed alternatives. This is the first study that introduces an integrated FBPS-DEA for optimization of purchasing management systems.

In this paper fuzzy set theory was applied to discrete event simulation to model uncertainty in input data. Various approaches to fuzzy simulation have been proposed in the literature, even if many of the problems are still to be solved. The key points are how to manage the simulation event list and how to update the fuzzy simulation clock. These two tasks are mainly based on the ranking algorithm. In the following, the classical algorithms were applied to rank temporal fuzzy sets and the results obtained were compared with the ones obtained by the proposed ranking algorithm. The comparison was performed by analyzing a simple case study in the manufacturing field. The results show how the new ranking algorithm can be very useful in a fuzzy simulation environment.

Fuzzy age-dependent replacement policy and SPSA algorithm based-on fuzzy simulation

The effect of environmental uncertainty and strategic applications of IS on a firm’s performance

One goal of numerical simulation in ocean acoustics is the accurate prediction of acoustic field properties in complex environments. In this context, the objective assessment of both sonar performance and associated signal-processing schemes operating within uncertain littoral environments pose significant challenges for numerical simulation. Some of the issues involved with using numerical models for the prediction of sonar performance in the presence of incomplete environmental information are addressed. To achieve credibility as a prediction tool, the simulation results should be held to the same standards as those associated with experimental results, i.e., the numerical predictions should include error bars or confidence intervals that represent a quantitative measure of the environmental uncertainty that is always present. Providing a rational, quantitative assessment of system uncertainty in complex environments is a nontrivial task. Incomplete environmental information occurs at multiple space-time scales within both the oceanographic and acoustic modeling phases; this uncertainty can propagate, interact, and amplify in the course of transmitting and processing the signal. It is argued that environmental uncertainty should be directly incorporated into the numerical simulation process so that it links directly to the physics of wave propagation. [Work supported by NRL Base funding through ONR.]

In this paper we study the path choice problem of material transportation in emergency based on the analysis of fuzzy concept. The effect of fuzzy factors on road is mainly considered to obtain an optimal calculation of path choice. A routing model with random probability for emergency rescue center is proposed with utilization of the theory of uncertain programming with fuzzy probability. The uncertain fuzzy random probability simulation, Bellman-ford algorithm and relaxation technique are used to solve the model. At last an example is given to demonstrate this method.

As a hybrid methodology to estimate VaR, that combines the use of parametric modelling with the use of bootstrapping techniques, filtered historical simulation (FHS) should not be sensitive to the use of alternative distributions assumed in the filtering stage. However, recent studies (Kuester et al. 2006) have found that the distribution used in the filtering stage can influence the VaR estimates obtained in the context of this methodology. Using Extreme Value Theory (EVT) this paper explains that the VaR estimates for lower probabilities should not be sensitive to the distribution assumed in the filtering stage of the FHS method. However, for higher probabilities, the EVT results do not hold and therefore the use of alternative distributions might impact the VaR estimates. These theoretical results are tested using both simulated and real data. Three different realistic data generating processes were considered to generate several series of simulated returns. Additionally, three competing models, differing in the innovations assumption, were tested: a normal-GARCH, a t-GARCH and a skew-t-GARCH. Our backtesting results indicate that FHS can forecast VaR with accuracy for data which exhibits a high incidence of zeros, time-varying skewness, asymmetric effects to return shocks on volatility, as well as other stylized facts. Importantly, our results for the simulated data demonstrate that, for lower probabilities, the choice of the distribution assumed in the filtering stage has no impact on the performance of FHS as an accurate method to forecasting VaR. Additionally, 40 years of daily data on six well known active stock indices are used to empirically evaluate the FHS VaR estimates. Four competing GARCH-type specifications, combined with three different innovation assumptions (normal, Student-t and skew-Student t), are used to capture time series dynamics. Based on a sample of several VaR probabilities, the results of the dynamic quantile (DQ) tests clearly indicate that the use of asymmetric GARCH models (specifically GJR and GJR in Mean) generally improve the VaR forecasting performance of FHS. In addition, the choice of a skew-Student t distribution for the innovation process slightly improves the performance results of the GJR in Mean model. When different VaR probabilities are used, the choice of an appropriate model specification seems to be more important than the choice of a suitable distribution assumption. With respect to the lower VaR probability tested (1%), the results show that, as expected, the VaR estimate is very similar regardless of the GARCH model and distribution assumed.

The hybrid metaheuristics algorithms (HMHAs) have gained a considerable attention for their capability to solve difficult problems in different fields of science. This chapter introduces some applications of HMHAs in solving inventory theory problems. Three basic inventory problems, joint replenishment EOQ problem, newsboy problem, and stochastic review problem, in certain and uncertain environments such as stochastic, rough, and fuzzy environments with six different applications, are considered. Several HMHAs such as genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), harmony search (HS), variable neighborhood search (VNS), and bees colony optimization (BCO) methods are used to solve the inventory problems. The proposed metaheuristics algorithms also are combined with fuzzy simulation, rough simulation, Pareto selecting and goal programming approaches. The computational performance of all of them, on solving these three optimization problems, is compared together.

The aim of the present study is to develop and implement a methodology that accounts for parameter variability and uncertainty in the presence of qualitative and semi-quantitative information (fuzzy simulations) as well as when some parameters are better quantitatively defined than others (fuzzy-probabilistic approach). The fuzzy simulations method consists of (i) representing parameter uncertainty and variability by fuzzy numbers and (ii) simulating predictions by solving the pharmacokinetic model. The fuzzy-probabilistic approach includes an additional transformation between fuzzy numbers and probability density functions. To illustrate the proposed method a diazepam WBPBPK model was used where the information for hepatic intrinsic clearance determined by in vitro-in vivo scaling was semi-quantitative. The predicted concentration time profiles were compared with those resulting from a Monte Carlo simulation. Fuzzy simulations can be used as an alternative to Monte Carlo simulation.

Fuzzy programming with fuzzy decisions and fuzzy simulation-based genetic algorithm

This paper develops an approach to three-dimensional source tracking in an uncertain ocean environment using a horizontal line array (HLA). The tracking algorithm combines matched-field focalization for environmental (seabed and water column) and source-bearing model parameters with the Viterbi algorithm for range-depth estimation and includes physical constraints on source velocity. The ability to track a source despite environmental uncertainty is examined using synthetic test cases for various track geometries and with varying degrees of prior information for environmental parameters. Performance is evaluated for a range of signal-to-noise ratios in terms of the probability of estimating a track within acceptable position/depth errors. The algorithm substantially outperforms tracking with poor environmental estimates and generally obtains results close to those obtained with exact environmental knowledge. The approach is also applied to measured narrowband data recorded on a bottom-moored HLA in shallow water (the Barents Sea) and shown to successfully track both a towed submerged source and a surface ship in cases where simpler tracking algorithms failed.

Environmental Uncertainty and IT Governance: A Moral Hazard Perspective

This paper uses three different Malmquist productivity index (MPI) decompositions to measure Greek seaports' productivity for the period 2006–10. Bootstrap techniques are applied in order for confidence intervals of MPIs and their components to be constructed, and to verify if the indicated changes are statistically significant. Finally, a second-stage nonparametric analysis is applied identifying the effect of seaports' size on their productivity levels. The results reveal that the number of terminals is a crucial determinant of seaports' productivity levels. Additionally, it appears that the high length of Greek seaports has a negative influence on their productivity levels over the years.