The effect of product variety on supply-chain performance

We present a stylized model for analyzing the effect of product variety on supply-chain performance for a supply chain with a single manufacturer and multiple retailers. The manufacturer produces multiple products on a shared resource with limited capacity and the effect of changeovers on supply-chain cost is due primarily to setup time rather than setup cost. We show that the expected replenishment lead time and the retailers' costs are concave increasing in product variety and that the increase is asymptotically linear. Thus, if setup times are significant, the effect of product variety on cost is substantially greater than that suggested by the risk-pooling literature for perfectly flexible manufacturing processes, where the cost increases proportionally to the square root of product variety. We demonstrate that disregarding the effect of product variety on lead time can lead to poor decisions and can lead companies to offer product variety that is greater than optimal. The results of our analysis enable decision-makers to quantify the effect of product variety on supply-chain performance and thus to determine the optimal product variety to offer. The results can also be used to evaluate how changes in the manufacturing process, the supply-chain structure, and the customer demand rate can improve the performance of supply chains with high product variety.     

A flexible and generic approach to dynamic modelling of supply chains

In this paper, we present a new modelling approach for realistic supply chain simulation. The model provides an experimental environment for informed comparison between different supply chain policies. A basic simulation model for a generic node, from which a supply chain network can be built, has been developed using an object-oriented approach. This generic model allows the incorporation of the information and physical systems and decision-making policies used by each node. The object-oriented approach gives the flexibility in specifying the supply chain configuration and operation decisions, and policies. Stochastic simulations are achieved by applying Latin Supercube Sampling to the uncertain variables in descending order of importance, which reduces the number of simulations required. We also present a case study to show that the model is applicable to a real-life situation for dynamic stochastic studies.     

Improving the robustness in railway station areas

In order to improve the robustness of a railway system in station areas, this paper introduces an iterative approach to successively optimize the train routing through station areas and to enhance this solution by applying some changes to the timetable in a tabu search environment. We present our vision on robustness and describe how this vision can be used in practice. By introducing the spread of the trains in the objective function for the route choice and timetabling module, we improve the robustness of a railway system. Using a discrete event simulation model, the performance of our algorithms is evaluated based on a case study for the Brussels’ area. The computational results indicate an average improvement in robustness of 6.2% together with a decrease in delay propagation of about 25%. Furthermore, the effect of some measures like changing the train offer to further increase the robustness is evaluated and compared.     

Exact solutions to the double Sine-Gordon equation

The double Sine-Gordon equation (DSG) with arbitrary constant coefficients is studied by F-expansion method, which can be thought of as an over-all generalization of the Jacobi elliptic function expansion since F here stands for every one of the Jacobi elliptic functions (even other functions). We first derive three kinds of the generic solutions of the DSG as well as the generic solutions of the Sine-Gordon equation (SG), then in terms of Appendix A, many exact periodic wave solutions, solitary wave solutions and trigonometric function solutions of the DSG are separated from its generic solutions. The corresponding results of the SG, which is a special case of the DSG, can also be obtained.     

Focused ultrasonic therapy planning: Metamodeling,optimization, visualization

We present a generic approach for focused ultrasonic therapy planning on the basis of numerical simulation, multi-objective optimization, stochastic analysis and visualization in virtual environments. A realistic test case is used to demonstrate the approach. RBF metamodeling of simulation results is performed for continuous representation of two optimization objectives. The non-convex Pareto front of the objectives is determined by means of non-dominated set and local improvement algorithms. Uncertainties of metamodeling are estimated by means of a cross-validation procedure. The 3D visualization in virtual environment framework Avango allows detailed inspection of MRT images, the corresponding material model and spatial distribution of the resulting thermal dose.     

Inference for constant-stress accelerated degradation test based on Gamma process

This paper proposes a Gamma constant-stress accelerated degradation model based on the principle of the degradation mechanism invariance. The maximum likelihood estimators of the parameters of the proposed model are derived. Based on Cornish–Fisher expansion, the approximate confidence interval for the shape parameter of the Gamma degradation process is developed. Since it is difficult to obtain the exact confidence intervals for other model parameters and some quantities such as the mean degradation in unit time, the quantile and the reliability function of the lifetime at the normal stress level, the generalized confidence intervals for these quantities are proposed. The percentiles of the proposed generalized pivotal quantities can be obtained by the simulation. The performances of the proposed confidence intervals are evaluated by the Monte Carlo simulation method. In the simulation study, the proposed confidence intervals are compared with the Wald and the bootstrap-p confidence intervals. The simulation results show that the proposed confidence intervals outperform the Wald and the bootstrap-p confidence intervals in terms of the coverage percentage. Finally, a real example is used to illustrate the proposed procedures.     

The availability of unmanned air vehicles: a post-case study

An Unmanned Air Vehicle (UAV) is an unmanned, remotely controlled, small air vehicle. It has an important role in anti-surface warfare. This implies over-the-horizon detection, classification, targeting and battle damage assessment. To perform these tasks several UAVs are needed to assist or interchange with each other. An important problem is to determine how many UAVs are needed in this respect. The answer depends on the characteristics of the UAV and its mission. The UAV availability problem is very complex and the usual method to solve such a problem is simulation. A disadvantage of simulation is that it can be very time-consuming. Hence it is not very suitable for sensitivity analysis. Moreover, since simulation gives mere approximations and is not very generic, theoretical insights are hardly gained. In this paper we show how such a complex problem can still be tackled analytically by using a basic model from reliability theory, viz., a 1-out-of-n system with cold standby, ample repair facility and general life time and repair distributions.     

Modeling centralized organization of organizational change

Organizations change with the dynamics of the world. To enable organizations to change, certain structures and capabilities are needed. As all processes, a change process has an organization of its own. In this paper it is shown how within a formal organization modeling approach also organizational change processes can be modeled. A generic organization model (covering both organization structure and behavior) for organizational change is presented and formally evaluated for a case study. This model takes into account different phases in a change process considered in Organization Theory literature, such as unfreezing, movement and refreezing. Moreover, at the level of individuals, the internal beliefs and their changes are incorporated in the model. In addition, an internal mental model for (reflective) reasoning about expected role behavior is included in the organization model.     

The Use of Soft Systems Methodology in the Development of a Simulation of Out-patient Services at Watford General Hospital

The effective and efficient provision of out-patient services may be assisted by the appropriate use of discrete event simulation. Resource use must be carefully monitored and suitable control actions must be taken, if service level and quality are to be maintained, whilst keeping cost increases to a minimum. In itself, simulation provides no means by which system activities may be identified. The use of soft systems methodology as a tool to aid in activity identification for simulation modelling is being explored. The investigation focuses on the effects the participative nature of soft systems methodology has on the acceptability of a simulation model. This case study forms part of a research objective of developing a generic approach to link soft systems methodology and simulation. Its aim is to provide analysts and managers with a process which may assist in planning strategies for health care.     

Efficient computer experiment-based optimization through variable selection

A computer experiment-based optimization approach employs design of experiments and statistical modeling to represent a complex objective function that can only be evaluated pointwise by running a computer model. In large-scale applications, the number of variables is huge, and direct use of computer experiments would require an exceedingly large experimental design and, consequently, significant computational effort. If a large portion of the variables have little impact on the objective, then there is a need to eliminate these before performing the complete set of computer experiments. This is a variable selection task. The ideal variable selection method for this task should handle unknown nonlinear structure, should be computationally fast, and would be conducted after a small number of computer experiment runs, likely fewer runs (N) than the number of variables (P). Conventional variable selection techniques are based on assumed linear model forms and cannot be applied in this “large P and small N” problem. In this paper, we present a framework that adds a variable selection step prior to computer experiment-based optimization, and we consider data mining methods, using principal components analysis and multiple testing based on false discovery rate, that are appropriate for our variable selection task. An airline fleet assignment case study is used to illustrate our approach.     

Singular perturbations and vanishing passage through a turning point

The paper deals with planar slow-fast cycles containing a unique generic turning point. We address the question on how to study canard cycles when the slow dynamics can be singular at the turning point. We more precisely accept a generic saddle-node bifurcation to pass through the turning point. It reveals that in this case the slow divergence integral is no longer the good tool to use, but its derivative with respect to the layer variable still is. We provide general results as well as a number of applications. We show how to treat the open problems presented in Artés et al. (2009) [1] and Dumortier and Rousseau (2009) [13], dealing respectively with the graphics DI2a and DF1a from Dumortier et al. (1994) [14].     

On level sets of marginal functions

The investigation of level sets of marginal functions is motivated by several aspects of standard and generalized semi-infinite programming. The feasible- set M of such a prob­lem is easily seen to be a level set of the marginal function corresponding to the lower level problem. In the present paper we study the local structure of M at feasible bound­ary points in the generic case. A codimension formula shows that there is a wide range of these generic situations, but that the number of active indices is always bounded by the state space dimension. We restrict our attention to two special subcases

In the first case, where the number of active indices is maximalM is shown to be locally diffeomorphic to the non-negative orthant. This situation is well-known from finite and also from standard semi-infinite programming. However, in the second case a generic situation arises which is typical for generalized semi-infinite programming. Here, the active index set is a singleton, and M can exhibit a re-entrant corner or even local non-closedness, depending on whether the Mangasarian-Fromovitz constraint qualifica­tion holds at the active index. If an objective function is minimized over M then in the setting of the second case a local minimizer cannot occur     

Controllability,observability and the solution of AX - XB = C

A closed-form finite series representation of the unique solution X of the matrix equation AX ? XB=C is developed. Using this representation, the image, kernel, and rank of X are related to the controllable and unobservable subspaces of the (A, C) and (C, B) pairs respectively. Bounds on the rank of X are obtainedin terms of the dimensions of these subspaces. In the case that C has unitary rank, an exact calculation of rank X is made. The generic rank of X with A, B fixed and C generic is evaluated.     

Stochastic Enumeration Method for Counting NP-Hard Problems

We present a new generic sequential importance sampling algorithm, called stochastic enumeration (SE) for counting #P-complete problems, such as the number of satisfiability assignments and the number of perfect matchings (permanent). We show that SE presents a natural generalization of the classic one-step-look-ahead algorithm in the sense that it: Runs in parallel multiple trajectories instead of a single one; Employs a polynomial time decision making oracle, which can be viewed as an n-step-look-ahead algorithm, where n is the size of the problem. Our simulation studies indicate good performance of SE as compared with the well-known splitting and SampleSearch methods.     

An implicit least squares algorithm for nonlinear rational model parameter estimation

In this study a new insight into least squares regression is identified and immediately applied to estimating the parameters of nonlinear rational models. From the beginning the ordinary explicit expression for linear in the parameters model is expanded into an implicit expression. Then a generic algorithm in terms of least squares error is developed for the model parameter estimation. It has been proved that a nonlinear rational model can be expressed as an implicit linear in the parameters model, therefore, the developed algorithm can be comfortably revised for estimating the parameters of the rational models. The major advancement of the generic algorithm is its conciseness and efficiency in dealing with the parameter estimation problems associated with nonlinear in the parameters models. Further, the algorithm can be used to deal with those regression terms which are subject to noise. The algorithm is reduced to an ordinary least square algorithm in the case of linear or linear in the parameters models. Three simulated examples plus a realistic case study are used to test and illustrate the performance of the algorithm.     

Quantile regression metamodeling: Toward improved responsiveness in the high-tech electronics manufacturing industry

Both technology and market demands within the high-tech electronics manufacturing industry change rapidly. Accurate and efficient estimation of cycle-time (CT) distribution remains a critical driver of on-time delivery and associated customer satisfaction metrics in these complex manufacturing systems. Simulation models are often used to emulate these systems in order to estimate parameters of the CT distribution. However, execution time of such simulation models can be excessively long limiting the number of simulation runs that can be executed for quantifying the impact of potential future operational changes. One solution is the use of simulation metamodeling which is to build a closed-form mathematical expression to approximate the input–output relationship implied by the simulation model based on simulation experiments run at selected design points in advance. Metamodels can be easily evaluated in a spreadsheet environment “on demand” to answer what-if questions without needing to run lengthy simulations. The majority of previous simulation metamodeling approaches have focused on estimating mean CT as a function of a single input variable (i.e., throughput). In this paper, we demonstrate the feasibility of a quantile regression based metamodeling approach. This method allows estimation of CT quantiles as a function of multiple input variables (e.g., throughput, product mix, and various distributional parameters of time-between-failures, repair time, setup time, loading and unloading times). Empirical results are provided to demonstrate the efficacy of the approach in a realistic simulation model representative of a semiconductor manufacturing system.     

Testing Homogeneity in Weibull Error in Variables Models

We discuss properties of the score statistics for testing the null hypothesis of homogeneity in a Weibull mixing model in which the group effect is modelled as a random variable and some of the covariates are measured with error. The statistics proposed are based on the corrected score approach and they require estimation only under the conventional Weibull model with measurement errors and does not require that the distribution of the random effect be specified. The results in this paper extend results in Gimenez, Bolfarine, and Colosimo (Annals of the Institute of Statistical Mathematics, 52, 698–711, 2000) for the case of independent Weibull models. A simulation study is provided. An erratum to this article can be found at     

Lapse risk in life insurance: Correlation and contagion effects among policyholders’ behaviors

The present paper proposes a new methodology to model the lapse risk in life insurance by integrating the dynamic aspects of policyholders’ behaviors and the dependency of the lapse intensity on macroeconomic conditions. Our approach, suitable to stable economic regimes as well as stress scenarios, introduces a mathematical framework where the lapse intensity follows a dynamic contagion process, see Dassios and Zhao (2011). This allows to capture both contagion and correlation potentially arising among insureds’ behaviors. In this framework, an external market driven jump component drives the lapse intensity process depending on the interest rate trajectory: when the spread between the market interest rates and the contractual crediting rate crosses a given threshold, the insurer is likely to experience more surrenders. A log-normal dynamic for the forward rates is introduced to build trajectories of an observable market variable and mimic the effect of a macroeconomic triggering event based on interest rates on the lapse intensity. Contrary to previous works, our shot-noise intensity is not constant and the resulting intensity process is not Markovian. Closed-form expressions and analytic sensitivities for the moments of the lapse intensity are provided, showing how lapses can be affected by massive copycat behaviors. Further analyses are then conducted to illustrate how the mean risk varies depending on the model’s parameters, while a simulation study compares our results with those obtained using standard practices. The numerical outputs highlight a potential misestimation of the expected number of lapses under extreme scenarios when using classical stress testing methodologies.