Analyses of team performance in a dynamic task environment

Teamwork, a central component of team research, is not readily observable and must be inferred from the manner in which teams operate. Of particular interest is the measurement and evaluation of teamwork. The goal of this paper is to explore the assessment of team data using a temporal accuracy measure called the Relative Accuracy Index (RAI). For the statistical analysis, the generalized mixed model was applied. This model is applicable for binomial data and takes into account the correlation structure within team members. We describe the statistical procedure in detail, aiming to guide researchers who encounter similar problems. Using our statistical analysis, we found that participants whose training focused on coordination activities outperformed those whose training did not. Moreover, we found that workload stress accentuates the difference.     

Knowledge-based approach to improving micromarketing decisions in a data-challenged environment

With the growing popularity of micromarketing strategies, more companies are tailoring their marketing efforts to meet individual consumer need based on analysis of consumer-level data. However, there are two major obstacles in the way to effective micromarketing strategies: (1) data limitation at the consumer-level making it difficult to construct robust and accurate promotional response function and (2) misplaced usage of a resource allocation approach effective in a data-rich environment to data-challenged environment. These obstacles lead to suboptimal marketing resource allocation decisions.This paper presents a knowledge-based approach specifically designed for effectively allocating marketing resources in the micromarketing environment. A team of domain experts provide knowledge in refining and imputing data to overcome data limitation issues, as well as identifying consumer-level constraints to strengthen the integer programming model’s performance.Results indicate that this approach is transparent to all levels of management, adaptable to changes in environment, and easy to implement. In addition, there is tremendous potential for improving not only profitability but also growing the company’s intellectual capital. When this approach is applied to a random sales territory, it outperforms the traditional method by more than 19% in profit.     

Time windows-based team performance measures: a framework to measure team performance in dynamic environments

In this paper, we present a framework for implementing Team Performance Measures based on a temporal accuracy measure called the relative accuracy index (RAI) to evaluate and compare team performance in a command-and-control human-in-the-loop simulation. The framework allows researchers to collect and analyze team outcomes in an unbiased objective manner based on a temporal performance measure known as a Time Window. Our framework provides experimenters and subject matter experts the necessary tools to evaluate performance in terms of task demands. We also provide a sample analysis of individual and team performance using the RAI.     

Cognitive diversity and team performance in a complex multiple task environment

This article examines the multiple effects of cognitive diversity in teams operating complex human-machine-systems. The study employed a PC-based multiple-task environment, called the Cabin Air Management System, which models a process control task in the operational context of a spacecraft's life support system. Two types of cognitive diversity were examined: system understanding and team specialization. System understanding referred to the depth of understanding team members were given during training (low-level procedure-oriented vs. high level knowledge-oriented training). Team specialization referred to the degree to which knowledge about system fault scenarios was distributed between team members (specialized vs. non-specialized). A total of 72 participants took part in the study. After having received 4.5 h of training on an individual basis, participants completed a 1-h experimental session, in which they worked in two-person teams on a series of fault scenarios of varying difficulty. Measures were taken of primary and secondary task performance, system intervention and information sampling strategies, system knowledge, subjective operator state, communication patterns and conflict. The results provided evidence for the benefits of cognitive diversity with regard to system understanding. This manifested itself in better primary task performance and more efficient manual system control. No advantages were found for cognitive diversity with regard to specialization. There was no effect of cognitive diversity on intra-team conflict, with conflict levels generally being very low. The article concludes with a discussion of the implications of the findings for the engineering of cognitive diversity in teams operating complex human-machine-systems.     

Cognitive diversity and team performance in a complex multiple task environment

This article examines the multiple effects of cognitive diversity in teams operating complex human-machine-systems. The study employed a PC-based multiple-task environment, called the Cabin Air Management System, which models a process control task in the operational context of a spacecraft's life support system. Two types of cognitive diversity were examined: system understanding and team specialization. System understanding referred to the depth of understanding team members were given during training (low-level procedure-oriented vs. high level knowledge-oriented training). Team specialization referred to the degree to which knowledge about system fault scenarios was distributed between team members (specialized vs. non-specialized). A total of 72 participants took part in the study. After having received 4.5 h of training on an individual basis, participants completed a 1-h experimental session, in which they worked in two-person teams on a series of fault scenarios of varying difficulty. Measures were taken of primary and secondary task performance, system intervention and information sampling strategies, system knowledge, subjective operator state, communication patterns and conflict. The results provided evidence for the benefits of cognitive diversity with regard to system understanding. This manifested itself in better primary task performance and more efficient manual system control. No advantages were found for cognitive diversity with regard to specialization. There was no effect of cognitive diversity on intra-team conflict, with conflict levels generally being very low. The article concludes with a discussion of the implications of the findings for the engineering of cognitive diversity in teams operating complex human-machine-systems.     

Reactive navigation in dynamic environment using a multisensorpredictor

A reactive navigation system for an autonomous mobile robot in unstructured dynamic environments is presented. The motion of moving obstacles is estimated for robot motion planning and obstacle avoidance. A multisensor-based obstacle predictor is utilized to obtain obstacle-motion information. Sensory data from a CCD camera and multiple ultrasonic range finders are combined to predict obstacle positions at the next sampling instant. A neural network, which is trained off-line, provides the desired prediction on-line in real time. The predicted obstacle configuration is employed by the proposed virtual force based navigation method to prevent collision with moving obstacles. Simulation results are presented to verify the effectiveness of the proposed navigation system in an environment with multiple mobile robots or moving objects. This system was implemented and tested on an experimental mobile robot at our laboratory. Navigation results in real environment are presented and analyzed.     

Copying of dynamic structures in a pascal environment

Two problems involving the use of dynamic lists in Pascal are (1) the limited size of the heap and (2) the difficulty in creating copies of complex multi-linked structures. Both these problems can be solved by writing the appropriate segment of memory to a file on disk. The heap segment may then be released. To recover the structure during a later phase of processing, the contents of the file can be transferred back onto the heap. This paper presents an algorithm to implement this solution.     

Real-time fluid simulation in a dynamic virtual environment

Simulating physically realistic complex fluid behaviors in a distributed interactive simulation (DIS) presents a challenging problem for computer graphics researchers. The authors consider how solving the 2D Navier-Stokes equations via a computational fluid dynamics method lets us map surfaces into 3D and achieves realistic real-time fluid surface behaviours     

Decomposition of dynamic team decision problems

This paper considers the question "when can a dynamic team problem be decomposed into several smaller dynamic team problems?" A team is partitioned into several groups of members. Precedence relation a nd nestedness relation of information among these groups are defined. Then the concepts of independent partition and sequential partition of a team are introduced, which are partitions satisfying some conditions on their precedence relation, nestedness relation, and cost function. It is shown that if a team has an independent (sequential) partition, the problem can be decomposed into several independent (related) subproblems. It is also shown that the finest independent (sequential) partition is unique. In addition, decomposition of decentralized control problems is discussed.     

Obstacle avoidance in a dynamic environment: a collision coneapproach

A novel collision cone approach is proposed as an aid to collision detection and avoidance between irregularly shaped moving objects with unknown trajectories. It is shown that the collision cone can be effectively used to determine whether collision between a robot and an obstacle (both moving in a dynamic environment) is imminent. No restrictions are placed on the shapes of either the robot or the obstacle, i.e., they can both be of any arbitrary shape. The collision cone concept is developed in a phased manner starting from existing analytical results that enable prediction of collision between two moving point objects. These results are extended to predict collision between a point and a circular object, between a point and an irregularly shaped object, between two circular objects, and finally between two irregularly shaped objects. Using the collision cone approach, several strategies that the robot can follow in order to avoid collision, are presented. A discussion on how the shapes of the robot and obstacles can be approximated in order to reduce computational burden is also presented. A number of examples are given to illustrate both collision prediction and avoidance strategies of the robot     

Robust airline schedule design in a dynamic scheduling environment

In the past decade, major airlines in the US have moved from banked hub-and-spoke operations to de-banked hub-and-spoke operations in order to lower operating costs. In Jiang and Barnhart (2009) [1], it is shown that dynamic airline scheduling, an approach that makes minor adjustments to flight schedules in the booking period by re-fleeting and re-timing flight legs, can significantly improve utilization of capacity and hence increase profit. In this paper, we develop robust schedule design models and algorithms to generate schedules that facilitate the application of dynamic scheduling in de-banked hub-and-spoke operations. Such schedule design approaches are robust in the sense that the schedules produced can more easily be manipulated in response to demand variability when embedded in a dynamic scheduling environment. In our robust schedule design model, we maximize the number of potentially connecting itineraries weighted by their respective revenues. We provide two equivalent formulations of the robust schedule design model and develop a decomposition-based solution approach involving a variable reduction technique and a variant of column generation. We demonstrate, through experiments using data from a major U.S. airline that the schedule generated can improve profitability when dynamic scheduling is applied. It is also observed that the greater the demand variability, the more profit our robust schedules achieve when compared to existing ones.     

Detection workload in a dynamic grid-based intrusion detection environment

Denial-of-service (DoS) and distributed denial-of-service (DDoS) are two of the most serious and destructive network threats on the Internet. Hackers, exploiting all kinds of malicious packages to attack and usurp network hosts, servers and bandwidth, have seriously damaged enterprise, campus and government network systems. Many network administrators employ intrusion detection systems (IDSs) and/or firewalls to protect their systems. However, some systems lose most of their detection and/or protection capabilities when encountering a huge volume of attack packets. In addition, some detection resources may fail due to hardware and/or software faults.     

Agent-based computer vision in a dynamic, real-time environment

For computer vision systems to operate in many real-world environments, processing must occur in real-time under dynamic conditions. An agent-based methodology offers an approach to increase flexibility and scalability to accommodate the demands of a real-time, dynamic environment. This paper presents an agent-based architecture that uses a utility optimization technique to guarantee that important vision tasks are fulfilled even under resource constraints. To ensure that the processing of vision tasks is both reliable and flexible, multiple behaviors are utilized to accomplish the vision application's requirements. A vision behavior consists of a grouping of vision algorithms and a set of service levels associated with these algorithms. Utility functions are adopted to evaluate the performance of all possible behaviors that can address the requirements of a vision application within resource constraints. The maximum overall utility corresponds to the optimal behavior. Two example systems using this model are presented to show the applicability of the architecture. Experimental results show that this agent-based architecture outperforms traditional non-agent-based approaches.     

Human-systems interaction for virtual environment team training

Virtual environments are increasingly used to support collaborative activities and distance learning. However, few are designed to support students, instructors and simulations in multi-participant team training. This paper describes the Training Studio, a system for authoring, delivering and evaluating multi-participant team training in an immersed virtual environment. The Training Studio focuses on human-systems interaction, allowing multiple students to learn and perform team tasks. The Training Studio supports collaborative learning for either single or multi-participant activity. This is accomplished through the use of agents which are assigned to students to act as personal mentors or missing team members. Conducting team training within a virtual environment introduces complexities and issues unique to team training and multiple-participant virtual environments. This paper describes our approach to virtual environment team training, discussing issues confronted and resulting design considerations and implementations.     

Plant location decisions in the ethanol industry: a dynamic and spatial analysis

Computational Economics - As a process relevant to the evolution of modern agriculture in North America, in this research we examine simulated ethanol plant location decisions. Due to the inherent...     

The dynamic linear exponential Gaussian team problem

The dynamic team problem for a linear system with Gaussian noise, exponential of a quadratic performance index, and one-step delayed sharing information pattern is considered. It is shown, via dynamic programming, that the multistage problem can be decomposed into a series of static team problems. Moreover, the optimal policy of theith team member at timekis an affine function of both the one-step predicted Kalman filter estimate and theith team member's observation at timek. Efficient algorithms are available for determining the gains of this affine controller. This model and solution are applied to an approximate resource allocation problem associated with a defense network, and a numerical example is discussed.     

A simulation study of sequencing and maintenance decisions in a dynamic job shop

This paper presents a simulation study of the performance of a job shop, where the equipment is subject to failure. We evaluate the effectiveness of some maintenance scheduling techniques under conditions involving shop load, job sequencing rule, preventive maintenance policy and maintenance capacity.     

Tool selection-embedded optimal assembly planning in a dynamic manufacturing environment

This paper presents an approach for tool selection-embedded optimal assembly planning in dynamic manufacturing environments. It aims to embed assembly tools into the planning process of assembly sequences in a dynamic shop-floor. The experimental results demonstrate that the developed approach is efficient and practical for a high fidelity assembly sequence with alternatives of assembly-tool sets. The dynamic assembly planning can efficiently support product assembly by generating feasible assembly sequences. It provides an effective design-aiding tool to virtually deal with various what-if scenarios regarding product assembly. In particular, the Web-based application developed in this research can be incorporated into a high-performance design and manufacturing environment on the Web, forming a distributed, collaborative and globally networked tool for product assembly planning.     

Identification of linear dynamic systems operating in a networked environment

This paper studies a networked system identification problem, which aims at identifying mathematical models required in networked control/estimation/filtering systems. Specifically, we consider the off-line identification of open-loop stable linear time-invariant processes working in a networked environment. In the networked environment, how the actuators (D/A conversion) operate plays a key role in the complexity of the related identification problems. In particular, it is reasonable to consider the configuration of event-driven actuators subject to random network-induced delays and packet dropouts; as a result, the networked identification problem is formulated as the one to identify continuous-time linear time-invariant models, based on the general non-uniformly non-synchronized sampled data. A modified version of the simplified refined instrumental variable method is proposed to solve this problem, and is validated in a networked identification experiment based on the Matlab/Simulink simulator TrueTime.