Yuri N. 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.

An overview of decision networks and organizations

The paper summarizes recent results on both binary and M-ary distributed hypothesis testing problems with decision makers (DMs) organized in structured decision networks. The general problem of finding an optimal organizational structure and decision strategy for such networks is formulated as a functional optimization problem. A normative model to study the effect of interactions between task structure and organizational design on the performance of hierarchical organizations is presented. A binary signal detection model is considered to illustrate the joint impact of organizational design and of task environment on the organizational decision performance. The concept of a congruent organizational structure (i.e., a structure that achieves centralized performance with minimal communication) is introduced, and a graph decomposition algorithm to synthesize congruent structures is discussed.

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.

Robust action strategies to induce desired effects

A new methodology is given in this paper to obtain a near-optimal strategy (i.e., specification of courses of action over time), which is also robust to environmental perturbations (unexpected events and/or parameter uncertainties), to achieve the desired effects. A dynamic Bayesian network (DBN)-based stochastic mission model is employed to represent the dynamic and uncertain nature of the environment. A genetic algorithm is applied to search for a near-optimal strategy with DBN serving as a fitness evaluator. The joint probability of achieving the desired effects (namely, the probability of success) at specified times is a random variable due to uncertainties in the environment. Consequently, we focus on signal-to-noise ratio (SNR), a measure of the mean and variance of the probability of success, to gauge the goodness of a strategy. The resulting strategy will not only have a high likelihood of inducing the desired effects, but will also be robust to environmental uncertainties.

Design of adaptive organizations

This paper introduces a comprehensive methodology for synthesizing adaptive decision-making organizations to complete a complex joint-operations mission. We present a multiobjective organizational design algorithm with the embedded strategy adaptation and structural reconfiguration schemes to produce an adaptive organizational structure. The synthesis of adaptive organizations is illustrated via an example under multiple scenarios of anomalies. The results of this paper form a foundation for current research on organizational adaptation.

Optimization algorithms in organizational design: optimality and complexity

Presents a classification of the optimization problems arising in the normative design of organizations to execute specific missions. The use of specific optimization algorithms for different phases of the design process leads to an efficient matching between the mission structure and that of an organization and its resources/constraints. It allows an analyst to obtain an acceptable trade-off among multiple objectives and constraints, as well as between computational complexity and solution efficiency (desired degree of sub-optimality).