Georgiy Levchuk

Normative design of organizations. I. Mission planning

This paper presents a design methodology for synthesizing organizations to execute complex missions efficiently. It focuses on devising mission planning strategies to optimally achieve mission goals while optimally utilizing organizations resources. Effective planning is often the key to successful completion of the mission, and conversely, mission failure can often be traced back to poor planning. Details on subsequent phases of the design process to construct the mission-driven human organizations are discussed in a companion paper.

Normative design of organizations. II. Organizational structure

For pt.I. see ibid., p. 346-59. This paper presents a multiobjective structural optimization process of designing an organization to execute a specific mission. We provide mathematical formulations for optimization problems arising in Phases II and III of our organizational design process and polynomial algorithms to solve the corresponding problems. Our organizational design methodology applies specific optimization techniques at different phases of the design, efficiently matching the structure of a mission (in particular, the one defined by the courses of action obtained from mission planning) to that of an organization. It allows an analyst to obtain an acceptable tradeoff among multiple mission and design objectives, as well as between computational complexity and solution efficiency (desired degree of suboptimality).

Normative design of project-based organizations-Part III: modeling congruent, robust, and adaptive organizations

In Parts I and II of this paper, we presented a three-phase iterative optimization process to design normative organizations. Such organizations are mission-based in that they are organized to perform a given task and then are dissolved. The objectives of the present paper are to 1) define and classify the processes of strategy and structural adaptation in organizations in response to mission and environmental changes, 2) extend our three-phase design methodology to construct robust and adaptive organizations, and 3) analyze the effects of mission parameters on their performance. We investigate the performance of organizations through internal workload and external coordination measures for individual DMs, as well as workload distribution as the overall organizational measure.

Optimal grouping algorithm for a group decision feedback detector in synchronous CDMA communications

The group decision feedback (GDF) detector is studied in this letter. Given the maximum group size, a grouping algorithm is proposed. It is shown that the proposed grouping algorithm maximizes the symmetric energy of the multiuser detection system. Furthermore, based on a set of lower bounds on asymptotic group effective energy (AGEE) of the GDF detector, it is shown that the proposed grouping algorithm, in fact, maximizes the AGEE lower bound for every group of users. The theoretical analysis of the grouping algorithm enables the offline estimation of the computational cost and the performance of a GDF detector. The computational complexity of a GDF detector is exponential in the largest size of the groups. Simulation results are presented to verify the theoretical conclusions. The results from this letter can be applied to the decision feedback detector by setting the maximum group size to one.

A class of coordinate descent methods for multiuser detection

A class of coordinate descent methods is proposed for the joint detection of binary symbols of K users in a synchronous correlated waveform multiple-access (CWMA) channel with Gaussian noise. We consider the detection problem as one of optimizing a quadratic objective function with binary constraints on decision variables. The proposed coordinate descent methods, while still maintaining a low computational complexity, are shown to provide as much as two orders of magnitude improvement in the probability of error, especially in situations where the existing methods do not perform well. The paper concludes with a discussion of how the proposed methods can be further improved.

Design and analysis of robust and adaptive organizations

The objectives of this paper are to (i) define and classify the processes of strategy and structural adaptation in organizations in response to mission and environmental changes, (ii) apply our modified design methodology to construct robust, adaptive, and flexible (both robust and adaptive) organizations; and (iii) analyze the effects of mission parameters on their performance. We investigate the performance of organizations through dynamic metrics, which include a performance based congruence measure, as well as DM activity and task workload measures as functions of time.

A Markov Decision Problem Approach to Goal Attainment

A new Markov decision problem (MDP)-based method for managing goal attainment (GA), which is the process of planning and controlling actions that are related to the achievement of a set of defined goals in the presence of resource and time constraints, is proposed. Specifically, we address the problem as one of optimally selecting a sequence of actions to transform the system and/or its environment from an initial state to a desired state. We begin with a method of explicitly mapping an action-GA graph to an MDP graph and developing a dynamic programming (DP) recursion to solve the MDP problem. For larger problems having exponential complexity with respect to the number of goals, we propose guided search algorithms such as AO*, AOepsiv*, and greedy search techniques, whose search power rests on the efficiency of their heuristic evaluation functions (HEFs). Our contribution in this part stems from the introduction of a new problem-specific HEF to aid the search process. We demonstrate reductions in the computational costs of the proposed techniques through performance comparison with standard DP techniques. We conclude this paper with a method to address situations in which alternative strategies (e.g., second best) are required. The new extended AO* algorithm identifies alternative control sequences for attaining the organizational goals.

Normative framework and computational models for simulating and assessing command and control processes

Abstract We present a normative methodology and computational framework to assess the effectiveness and efficiency of command and control (C2) organizations. Our process is based on quantitative representations of the organization and mission, and utilizes normative models of team and individual decision making. Our assessment methodology has been applied to evaluate the benefits of the Sensing and Patrolling Enablers Yielding Effective Security system—a ground-based decentralized C3I system comprised of emerging and existing sensing, SA/C2, and Shaping technologies. To facilitate the assessment analysis, our models have been implemented using a computational agent framework for the Distributed Dynamic Decision making (DDD) virtual simulation platform.

Visualization of graphical information fusion results

Graphical fusion methods are popular to describe distributed sensor applications such as target tracking and pattern recognition. Additional graphical methods include network analysis for social, communications, and sensor management. With the growing availability of various data modalities, graphical fusion methods are widely used to combine data from multiple sensors and modalities. To better understand the usefulness of graph fusion approaches, we address visualization to increase user comprehension of multi-modal data. The paper demonstrates a use case that combines graphs from text reports and target tracks to associate events and activities of interest visualization for testing Measures of Performance (MOP) and Measures of Effectiveness (MOE). The analysis includes the presentation of the separate graphs and then graph-fusion visualization for linking network graphs for tracking and classification.

Capturing and Building Expertise in Virtual Worlds

Model-driven simulation can make the design and delivery of instruction more efficient and effective. We describe two computational models that support both the design and delivery of instruction. BEST (the Benchmarked Experiential System for Training) can guide experts through the space of domain problems during the knowledge engineering phase of instructional design; it can guide trainees through the space of training objectives during instruction. PRESTO (Pedagogically Relevant Engineering of Scenarios for Training Objectives) builds scenarios on the fly to elicit the knowledge of experts during instructional design, and to satisfy the instructional objectives of trainees.