Sequencing a Set of Alternatives Under Time Constraints

We consider a problem of sequencing a set of alternatives (i.e. manufacturing methods, job applicants or target journals) available for selection to complete a project. Associated with each alternative are the probability of successful completion, the completion time, and the reward obtained upon successfully completing the alternative. The optimal sequencing strategy that maximizes the expected present value of total rewards, is derived based on a simple ordering parameter. We further consider an extension in which one of the alternatives will not be available for selection if not selected by a certain time, and another extension in which the selection process is allowed only for a limited period of time. We propose solution strategies to the selection and sequencing problem under time constraints.     

Convergence Study of Explicit Co-Simulation Approaches with Respect to Subsystem Solver Settings

Coupling different subsystem simulators can be accomplished by a co-simulation [1, 2]. For this purpose, the subsystem solvers are coupled by appropriate input and output variables. In order to analyze the stability of the coupled simulation, not only the coupling technique must be taken into account, but also the subsystem integrators. On the one hand, the stability of the co-simulation is influenced by the extrapolation of the coupling variables and by the macro-step size. On the other hand, the numerical errors arising from the subsystem solvers may directly affect the coupled simulation. The focus of this paper lies on the question, how the subsystem solvers influence the co-simulation. Therefore, numerical studies regarding the numerical stability and the convergence order have been carried out by using a co-simulation test model. We restrict ourselves to explicit co-simulation techniques, based on a zero-stable applied-force coupling approach. (© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A simulation-based test of stochastic multicriteria acceptability analysis using achievement functions

Stochastic multicriteria acceptability analysis using achievement functions (SMAA-A) is a preference model for discrete-choice decision making that inverts the traditional goal programming process by asking what combinations of aspirations are necessary to make each alternative the preferred one, rather than what alternative is preferred given a set of aspirations. In this paper, we test the ability of the model to discern good-performing alternatives from poorly-performing ones using a simulation study. Simulation results show that a suitably detailed construction of the acceptability index is particularly important, and that the resulting model can be fruitfully applied in the selection of a shortlist of alternatives from a larger set with only very limited decision maker involvement.     

Modeling of mold filling of Al gravity casting and validation with X-ray in-situ observation

In the mold filling simulation, element parameters including volume filled ratio, surface dimensionless distance, and surface filled ratio, were proposed to describe the shape and location of free surfaces in DFDM (Direct Finite Difference Method) elements. A model of the filling process was established, specially taking into account the mass, momentum and heat transfer in the vicinity of free surfaces. It was applied to an experimental AC4C (Al–7Si–0.4Mg) gravity casting. With a special X-ray apparatus, in-situ observation and record of actual mold filling process of the casting were carried out. The simulation results were validated and analyzed by comparing with the observation. The liquid flowing in the casting runner and ingate as well as the evolution of free surfaces were analyzed and discussed.     

Asymptotical stability for complex dynamical networks via link dynamics

From the perspective of large-scale system, a complex dynamical network can be regarded as the interconnected system with the node subsystem and link subsystem, which implies that the node subsystem and the link subsystem are the two main bodies of dynamical behaviors of network. Therefore, the whole stability of network is influenced by not only the dynamics of node subsystem but also the dynamics of link subsystem. According to the above view, the wholly asymptotical stability (WAS) is defined in this paper for the complex dynamical network with the model of differential equations. The WAS is used to describe the node subsystem achieves asymptotical stability when link subsystem achieves also the asymptotic stability in Lyapunov sense. For the WAS of the complex dynamical network, the corresponding criteria are derived by checking whether certain matrices are Hurwitz. The analysis results show that even if the isolated nodes are not asymptotically stable in Lyapunov sense, employing the dynamics of link can also force the node subsystem to achieve the asymptotical stability. Finally, the simulation examples show the validity of methods in this paper.     

Parameters optimization of selected casting processes using teaching–learning-based optimization algorithm

In the present work, mathematical models of three important casting processes are considered namely squeeze casting, continuous casting and die casting for the parameters optimization of respective processes. A recently developed advanced optimization algorithm named as teaching–learning-based optimization (TLBO) is used for the parameters optimization of these casting processes. Each process is described with a suitable example which involves respective process parameters. The mathematical model related to the squeeze casting is a multi-objective problem whereas the model related to the continuous casting is multi-objective multi-constrained problem and the problem related to the die casting is a single objective problem. The mathematical models which are considered in the present work were previously attempted by genetic algorithm and simulated annealing algorithms. However, attempt is made in the present work to minimize the computational efforts using the TLBO algorithm. Considerable improvements in results are obtained in all the cases and it is believed that a global optimum solution is achieved in the case of die casting process.     

Local Asymptotic Normality in Extreme Value Index Estimation

This paper deals with the estimation of the extreme value index in local extreme value models. We establish local asymptotic normality (LAN) under certain extreme value alternatives. It turns out that the central sequence occurring in the LAN expansion of the likelihood process is up to a rescaling procedure the Hill estimator. The central sequence plays a crucial role for the construction of asymptotic optimal statistical procedures. In particular, the Hill estimator is asymptotically minimax.     

The number of feasible designs in a compatibility matrix

Many design and planning problems consist of a number of distinct subsystems. Generally, there are several possible alternatives for design of a subsystem. However, an alternative for one subsystem may be incompatible with an alternative for another subsystem. Thus, a feasible design is one that incorporates one alternative for each subsystem such that no pairwise incompatibilities exist. Several such design and planning problems have been formulated as compatibility matrices. The feasible designs can be identified by using an efficient algorithm. This paper shows that, in general, the exact number of feasible designs decreases exponentially with the increase in the number of incompatible pairs. This finding should motivate more potential users to employ the compatibility matrix approach.     

凝固过程的温度场数值模拟

通过对铸件凝固过程中各换热边界条件的研究,建立了凝固过程的二维非稳态温度场计算数学模型;并运用了有限差分方法对模型进行离散,得到大型方程组,并利用超松驰迭代法(即SOR法)解该方程组,据此,利用Turbo C编制了计算机程序.上机运行结果表明,可较满意地模拟凝固过程温度场的分布.     

Robust planning of blending activities at refineries

Refinery operation planning is a complex task since refinery processes and inventories are tightly interconnected. We study refinery planning when ships are loaded with a blend of components and where arrival times of ships are uncertain. Any delay in ship arrival may result in overfull component tanks which results in less efficient blending alternatives, reduced process operations or even shut downs. We propose a planning approach where we use robust optimization as a decision tool. By using robust optimization uncertainty in arrival times is explicitly dealt with and the resulting plan and schedule will always be feasible. The approach includes a flexible way to describe and model uncertainties. To compare the robust approach with a traditional deterministic approach, we use a simulation process. Computational results from a case study and simulations show that the proposed methodology is substantially better than a deterministic approach.     

Generating pareto-optimal alternatives by a nonfeasible hierarchical method

A hierarchical algorithm for generating Pareto-optimal alternatives for convex multicriteria problems is derived. At the upper level, values for Lagrange multipliers of the coupling constraints are first given. Then at the subsystems, Pareto-optimal values are determined for the subsystem objectives, whereby an additional term or an additional objective is included due to the Lagrange multipliers. In the subsystem optimizations, the coupling equations between the subsystems are not satisfied; therefore, the method is called nonfeasible. Finally, the upper level checks which of the subsystem solutions satisfy the coupling constraints; these solutions are Pareto-optimal solutions for the overall system.     

Ordinal judgments in multiattribute decision analysis

The article discusses the contradiction between the ambiguity of human judgment in a multicriterion environment and the exactness of the assessments required in the majority of the decision-making methods. Preferential information from the decision makers in the ordinal form (e.g., “more preferable”, “less preferable”, etc.) is argued to be more stable and more reliable than cardinal input. Ways of obtaining and using ordinal judgments for rank ordering of multiattribute alternatives are discussed. The effectiveness of the step-wise procedure of using ordinal tradeoffs for comparison of alternatives is evaluated. We introduce the notion of ordinal tradeoffs, presentation of ordinal tradeoffs as a flexible three-stage process, a paired joint ordinal scale (PJOS), and evaluation of the effectiveness of the three-stage process. Simulation results examine the sensitivity of the number of pairwise comparisons required for given numbers of criteria and categories within criteria, as well as the number of alternatives analyzed. This simulation shows that ordinal pairwise comparisons provide sufficient power to discriminate between 75% and 80% of the alternatives compared. While the proportional number of pairwise comparisons relative to the maximum possible decreases with the number of criteria and categories, the method is relatively insensitive to the number of alternatives considered.     

Spatial approximation of stochastic convolutions

We study linear stochastic evolution partial differential equations driven by additive noise. We present a general and flexible framework for representing the infinite dimensional Wiener process, which drives the equation. Since the eigenfunctions and eigenvalues of the covariance operator of the process are usually not available for computations, we propose an expansion in an arbitrary frame. We show how to obtain error estimates when the truncated expansion is used in the equation. For the stochastic heat and wave equations, we combine the truncated expansion with a standard finite element method and derive a priori bounds for the mean square error. Specializing the frame to biorthogonal wavelets in one variable, we show how the hierarchical structure, support and cancelation properties of the primal and dual bases lead to near sparsity and can be used to simplify the simulation of the noise and its update when new terms are added to the expansion.     

Verified reduction of a model for a continuous casting process

The casting of metals is known to involve the complex interaction of turbulent momentum and heat transfer in the presence of solidification, and it is believed that computational fluid dynamical (CFD) techniques are required to model it correctly. Here, using asymptotic methods, we demonstrate that the key quantities obtained in an earlier CFD model for a particular continuous casting process – ostensibly for a pure metal, but equally for an alloy of eutectic composition – can be recovered using a much simpler model that takes into account just the heat transfer, requiring the numerical solution of a two-phase Stefan problem. Combining this with a more recent asymptotic thermomechanical model for the same continuous casting process, we postulate that it should be possible, with the additional help of algebraic manipulation, to reduce a model that takes into account turbulent momentum and heat transfer in the melt and the thermomechanics in the solid shell to one formulated in terms of only heat transfer, without adversely affecting model predictions.     

Group technology in production management: The short horizon planning level

We consider a set of parts divided into subsets called part types, determined in such a way that the parts belonging to the same part type are manufactured using the same sequence of tasks (i.e. the same working process). We are looking for a partition of the set of part types into subsets called part families, and for a partition of the set of tasks into subsets called production subsystems defined as follows: (1) the number of part families and the number of production subsystems are equal, (2) one (one only one) production subsystem corresponds to each part family, (3) one (and only one) part family corresponds to each production subsystem, (4) the previous partitions minimize the number of tasks performed in a production subsystem different from that which corresponds to the part family containing the part involved. We give a fast algorithm which leads to a good solution depending on the initial set of part families. We also propose an algorithm to find a ‘good’ initial set of part families.     

Multiobjective strategies in power systems planning

The rapidly changing decision environment in electrical systems planning calls for a new approach in planning procedures and mathematical tools. The method proposed in this study decomposes the decision making into two strata, one at the strategy setting, the other at the project selection level. For the strategy setting level, a multiobjective dynamic linear programming model is used in generating system expansion decision alternatives. The suitability of the modelling approach is considered from the various aspects such as congruence, flexibility, transparency, and accuracy.An application of the model to the Turkish electrical system is also presented, where expansion alternatives to the year 2000 are generated. Three criteria were observed: cost, environmental impact and risk.     

Negotiating wisely: Considerations based on MCDM/MAUT

Negotiation, both an art and a science, is important in business and in personal life. To negotiate intelligently, we need a strategy to help identify when, for what, and how we should negotiate. We consider a one-to-many negotiation problem such as a house-purchase process in which there is one buyer and many sellers. The alternatives are evaluated using multiple criteria, but only one criterion (such as price) is to be settled by negotiation. We use the Best Alternative To a Negotiated Agreement (BATNA) as a dynamic measure of negotiating strength, and develop a systematic quantitative iterative approach to assist in the negotiation process. We explore using simulation the efficacy of negotiating for more than one alternative at the same time. The objective of our approach is to help a negotiator achieve a good, hopefully an optimal, result effectively.     

Wavelet-Type Expansion of the Rosenblatt Process

The Rosenblatt process is an important example of self-similar stationary increments stochastic processes whose finite-dimensional distributions are non-Gaussian with all their moments finite. We show that the Rosenblatt process admits a wavelet-type expansion which is almost surely convergent uniformly on compact intervals and which can be thought as decorrelating the high frequencies. Our wavelet expansion of the Rosenblatt process is different from standard wavelet decompositions used in the wavelet literature. It nevertheless yields natural approximations to the Rosenblatt process, possesses a multiresolution-like structure and can be used for simulation of the Rosenblatt process in practice based on the usual Mallat-type pyramidal algorithm.     

Simulation-based framework to improve patient experience in an emergency department

The global economic crisis has a significant impact on healthcare resource provision worldwide. The management of limited healthcare resources is further challenged by the high level of uncertainty in demand, which can lead to unbalanced utilization of the available resources and a potential deterioration of patient satisfaction in terms of longer waiting times and perceived reduced quality of services. Therefore, healthcare managers require timely and accurate tools to optimize resource utility in a complex and ever-changing patient care process. An interactive simulation-based decision support framework is presented in this paper for healthcare process improvement. Complexity and different levels of variability within the process are incorporated into the process modeling phase, followed by developing a simulation model to examine the impact of potential alternatives. As a performance management tool, balanced scorecard (BSC) is incorporated within the framework to support continual and sustainable improvement by using strategic-linked performance measures and actions. These actions are evaluated by the simulation model developed, whilst the trade-off between objectives, though somewhat conflicting, is analysed by a preference model. The preference model is designed in an interactive and iterative process considering decision makers preferences regarding the selected key performance indicators (KPIs). A detailed implementation of the framework is demonstrated on an emergency department (ED) of an adult teaching hospital in north Dublin, Ireland. The results show that the unblocking of ED outflows by in-patient bed management is more effective than increasing only the ED physical capacity or the ED workforce.