Mansooreh Mollaghasemi

Usability engineering of virtual environments (VEs): identifying multiple criteria that drive effective VE system design

Designing usable and effective interactive virtual environment (VE) systems is a new challenge for system developers and human factors specialists. In particular, traditional usability principles do not consider characteristics unique to VE systems, such as the design of wayfinding and navigational techniques, object selection and manipulation, as well as integration of visual, auditory and haptic system outputs. VE designers must enhance presence, immersion, and system comfort, while minimizing sickness and deleterious after effects. Through the development of a multi-criteria assessment technique, the current effort categorizes and integrates these VE attributes into a systematic approach to designing and evaluating VE usability. Validation exercises suggest this technique, the Multicriteria Assessment of Usability for Virtual Environments (MAUVE) system, provides a structured approach for achieving usability in VE system design and evaluation. Applications for this research include military, entertainment, and any other interactive system that seeks to provide an enjoyable and effective user experience.

A branch-and-bound algorithm for the early/tardy machine scheduling problem with a common due-date and sequence-dependent setup time

The single-machine early/tardy (E/T) scheduling problem is addressed in this research. The objective of this problem is to minimize the total amount of earliness and tardiness. Earliness and tardiness, are weighted equally and the due date is common and large (unrestricted) for all jobs. Machine setup time is included and is considered sequence-dependent. When sequence-dependent setup times are included, the complexity of the problem increases significantly and the problem becomes NP-hard. In the literature, only mixed integer programming formulation is available to optimally solve the problem at hand. In this paper, a branch-and-bound algorithm (B&B) is developed to obtain optimal solutions for the problem. As it will be shown, the B&B solved problems three times larger than what has been reported in the literature.

Multicriteria design of manufacturing systems through simulation optimization

Simulation is a popular tool for the design and analysis of manufacturing systems. The popularity of simulation is due to its flexibility, its ability to model systems when analytical methods have failed, and its ability to model the time dynamic behavior of systems. However, in and of itself, simulation is not a design tool; therefore, in order to optimize a simulation model, it often must be used in conjunction with an optimum-seeking method. This paper describes an interactive (decision maker-computer) methodology for multiple response optimization of simulation models. This approach is based on a multiple criteria optimization technique called the STEP method. The proposed methodology is illustrated with an example involving the optimization of a manufacturing system. >

Ontologies for supply chain simulation modeling

Simulation might be an effective decision support tool in supply chain management. The review of supply chain simulation modeling methodologies revealed some issues one of which is the practicability of simulation in the supply chain environment. The supply chain environment is dynamic, information intensive, geographically dispersed, and heterogeneous. In order to develop usable supply chain simulation models, the models should be feasibly applicable in the supply chain environment. Distributed simulation models have been used by several researchers, however, their complexity and usability hindered their continuation. In this paper, a new approach is proposed. The approach is based on ontologies to integrate several supply chain views and models, which captures the required distributed knowledge to build simulation models. The ontology core is based on the SCOR model as the widely shared supply chain concepts. The ontology can define any supply chain and help the user to build the required simulation models

Comparison of global search methods for design optimization using simulation

A methodology for the application of global search methods for optimizing the results of a computer simulation is presented. Specific global optimization methods including simulated annealing, genetic algorithms, and Bayesian techniques are discussed in terms of their strengths and weaknesses as applied to this methodology. In particular, the effects of simulation time, constraints, dimensionality, and computational complexity are considered as they relate to the choice of algorithms. Simulated annealing and genetic algorithms perform similarly, yet differ in many ways from the class of Bayesian algorithms. Bayesian algorithms spend additional computation time in modeling all past values of the unknown function in an effort to minimize the number of evaluations of the function. These methods would be the algorithms of choice for determining the optimal design via simulation, provided the number of design variables is less than 10 and the time required to run a single simulation is large compared with the time it takes the algorithm to determine the next point.<<ETX>>

Multicriteria optimization of simulation models

The authors suggest a framework for the multicriteria optimization of simulation models by first discussing the unique difficulties of this problem area along with important problem characteristics, and then discussing the way that these problem characteristics would affect the choice of a particular technique. The problem of manufacturing system optimization is addressed. Various techniques, along with their advantages and disadvantages, are discussed and categorized according to the timing of the articulation of the required preference (tradeoff) information with respect to the optimization.<<ETX>>

Modeling the space shuttle

We summarize our methodology for modeling space shuttle processing using discrete event simulation. Why the project was initiated, what the overall goals were, how it was funded, and who were the members of the project team are identified. We describe the flow of the space shuttle flight hardware through the supporting infrastructure and how the-model was created to accurately portray the space shuttle. The input analysis methodology that was used to populate the model elements with probability distributions for process durations is described in the paper. Verification, validation, and experimentation activities are briefly summarized.

Supply chain simulation modeling made easy: an innovative approach

Simulation modeling and analysis requires skills and scientific background to be implemented. This is vital for this powerful methodology to deliver value to the company adopting it. There are several practices to implement and rely on simulation modeling for strategic and operational decision making, including hiring simulation engineers, building internal simulation team, or contract consultants. These practices are different in terms of budget, time to implement, and returns. In this paper, an innovative approach is described that provide a simulation solution that is affordable at the same time can be quickly implemented, it consists of generic interface that captures the information and structure of the supply chain then automatically generates simulation models. The user, which not necessarily a simulation expert, can quickly jump to the analysis and evaluation of scenarios. The paper presents a case study where the approach was implemented to model, simulate, and analyze NASAs Space Exploration Supply-Chain.

An Adaptive Multiobjective Approach to Evolving ART Architectures

In this paper, we present the evolution of adaptive resonance theory (ART) neural network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to simultaneously evolve the weights and the topology of three well-known ART architectures; fuzzy ARTMAP (FAM), ellipsoidal ARTMAP (EAM), and Gaussian ARTMAP (GAM). We refer to the resulting architectures as MO-GFAM, MO-GEAM, and MO-GGAM, and collectively as MO-GART. The major advantage of MO-GART is that it produces a number of solutions for the classification problem at hand that have different levels of merit [accuracy on unseen data (generalization) and size (number of categories created)]. MO-GART is shown to be more elegant (does not require user intervention to define the network parameters), more effective (of better accuracy and smaller size), and more efficient (faster to produce the solution networks) than other ART neural network architectures that have appeared in the literature. Furthermore, MO-GART is shown to be competitive with other popular classifiers, such as classification and regression tree (CART) and support vector machines (SVMs).

Multiobjective simulation optimization using an enhanced genetic algorithm

This paper presents an improved genetic algorithm approach, based on new ranking strategy, to conduct multiobjective optimization of simulation modeling problems. This approach integrates a simulation model with stochastic nondomination-based multiobjective optimization technique and genetic algorithms. New genetic operators are introduced to enhance the algorithm performance of finding Pareto optimal solutions and its efficiency in terms of computational effort. An elitism operator is employed to ensure the propagation of the Pareto optimal set, and a dynamic expansion operator to increase the population size. An importation operator is adapted to explore some new regions of the search space. Moreover, new concepts of stochastic and significant dominance are introduced to improve the definition of dominance in stochastic environments.