Candra Meirina

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.

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.

Dynamic multiple fault diagnosis with imperfect tests

Fault diagnosis is the process of identifying the failure sources of a malfunctioning system by observing their effects at various test points. It has a number of applications in engineering and medicine. In this paper, we present a near-optimal algorithm for dynamic multiple fault diagnosis in complex systems. This problem involves on-board diagnosis of the most likely set of faults and their time-evolution based on blocks of unreliable test outcomes over time. The dynamic multiple fault diagnosis (dMFD) problem is an intractable NP-hard combinatorial optimization problem. Consequently, we decompose the dMFD problem into a series of decoupled sub-problems, and develop a successive Lagrangian relaxation algorithm (SLRA) with backtracking to obtain a near-optimal solution for the problem. SLRA solves the sub-problems at each sample point by a Lagrangian relaxation method, and shares Lagrange multipliers at successive time points to speed up convergence. In addition, we apply a backtracking technique to further maximize the likelihood of obtaining the most likely evolution of failure sources and to minimize the effects of imperfect tests.

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.