A unifying framework for duality and modeling in robust linear programs

In this paper, our major theme is a unifying framework for duality in robust linear programming. We show that there are two pair of dual programs allied with a robust linear program; one in which the primal is constructed to be “ultra-conservative” and one in which the primal is constructed to be “ultra-optimistic.” Furthermore, as one would expect, if the uncertainly in the primal is row-based, the corresponding uncertainty in the dual is column-based, and vice-versa. Several examples are provided that illustrate the properties of these primal and dual models.     

多响应稳健参数设计的贝叶斯建模与优化

针对多响应稳健参数设计问题,在贝叶斯统计建模的框架下,结合质量损失函数和后验概率方法构建了一种新的优化模型.该方法不仅运用后验概率方法评估了各响应落在规格限内的期望概率(即优化结果的可靠性),而且运用质量损失函数度量了多变量过程的稳健性.此外,进一步地结合实例讨论了期望概率对优化结果的影响、联合概率与边缘概率之间的关系以及如何获得质量损失与后验概率之间的最佳平衡点.研究结果表明:所提方法能够在优化过程中较好地兼顾多元过程的稳健性和优化结果的可靠性,从而为实现多响应稳健参数设计提供了各方面(如多元过程的稳健性、优化结果的可靠性)均较满意的优化结果.     

Differentiation in leader–member exchange: A hierarchical linear modeling approach

This paper explores possible antecedents and consequences of leader–member exchange (LMX) differentiation. LMX differentiation is a group-level construct, defined as the degree to which members working with the same leader differ in terms of their LMX relationship quality with their leader. Specifically, we hypothesize that leaders’ personal universalistic values are negatively correlated with LMX differentiation. LMX differentiation, in turn, moderates the lower-level effect that LMX has on performance evaluation. Using surveys from leaders and subordinates as well as archival data from six companies, we found support for these arguments. However, we did not find support for a hypothesized cross-level moderation effect of differentiation on the effect LMX has on interactional justice. Implications for theory and discussion of future research directions are also addressed.     

Characterizing performance in socio-technical systems: A modeling framework in the domain of nuclear power

In modeling complex socio-technical systems, the critical nature of many interactions between human and automation are often unrecognized until the system experiences catastrophic consequences. One reason for this is that the systems are generally very robust and serious problems are rare. Another reason is the difficulty in modeling the variety of interactions in a manner compatible with analytical and/or computational models of the technological components of the system. Organizational issues and extraneous influences add to these modeling difficulties. We have developed a modeling framework that integrates certain ideas from social networks, graph theory, and simulation, all tied together using heuristics that reflect deep domain knowledge. This framework forms the basis for a computational implementation, portions of which have been implemented. Details of the model and the rationale for the model structure are described in this paper.     

A framework for strategic analysis

D.R. Ziemer and P.D. Maycock have been involved with the development and use of strategic analysis techniques at Texas Instruments for nearly a decade. Mr. Ziemer is manager of the Strategic Analysis branch and Mr. Maycock is a Senior Business Analyst in the branch. The activities of the branch, for the most part, support Corporate Development. The framework for strategic analysis is an attempt to summarize the entire range of activities involved in determining alternatives for the strategic allocation of resources. The methodology described can be viewed as an iterative analysis that produces alternative strategy candidates. The analysis techniques discussed are quite straightforward and easy to implement.     

A framework for strategic planning in multinational corporations

In this article the author describes how the adaptation and integration requirements of several corporations within the multinational environment were analyzed. A pattern of planning tasks for multinationals is then described which simplified planning while utilizing further task specialization.     

A framework for examining leadership in extreme contexts

In this review, we develop a framework to guide future research and to examine the execution of leadership in extreme contexts. We start by defining and distinguishing extreme contexts from crisis and other contexts. A five component typology is developed comprised of magnitude of consequences, form of threat, probability of consequences, location in time and physical or psychological–social proximity. We discuss the unique influences these components have on leadership processes in extreme contexts examining the relevance of organization types such as critical action and high reliability organizations. Further, we present a set of factors that may attenuate or intensify the dimensions comprising an extreme context, thus influencing either a team or organization's ability for adaptive leadership response. Ultimately this framework seeks to develop a richer understanding of extreme contexts to advance the future development of contextualized theories of leadership for extreme contexts.     

A control engineering framework for planning

Control Engineering provides a convenient framework for analyzing and understanding the essential features of business planning and control processes. This is because the basic idea of a control hierarchy is fundamental both to Control Engineering and to the control of a business. Control hierarchies in both business and in engineering are information-processing devices. As a business grows in size and complexity, its control hierarchies grow correspondingly more complex (‘The Principle of Hierarchization’); this is in order to manage the increased information-processing load.The authors describe how the basic control diagram can provide valuable insight into the planning and control hierarchies of business systems. The authors emphasize that the ‘Control Engineering’ method of analyzing the management process complements exisiting theories of management planning and control.     

A framework for optimizing managerial decision

There have been numerous attempts to analyze and formulate management problems within the general framework of Management Science and Operations Research, so as to help managers in their decision making problems. This paper focuses on the fundamental aspects of management decision making and introduces a conceptual framework in which problems of management and its different levels of functioning are systematically identified. Management problems are considered within two major categories: Enterprise and Operations problems. In spite of the difficulties in defining a clear-cut boundary between Enterprise and Operations problems, such a broad classification leads to three basic levels of management functioning, namely: Policy making, coordinating and implementation. The universal relationship among these three basic levels of functioning provides a basic functional element called the Management Triad. In contrast to classical higherarchial structure, triad structure provides a methodology to analyze relationships among different levels of management functions in terms of a given decision problem. It is demonstrated that for different decision making problems individuals can shift from one end of the triad to another and perform a different management function.A quantitative approach has been introduced for operations management decision making by adapting Shewhart's control chart philosophy which in turn provides for quasi-automated decisions for corrective action and also for filtering significant signals of change to appropriate management functioning levels.     

A general framework for path convexities

Journal of Combinatorial Optimization - In this work we deal with the so-called path convexities, defined over special collections of paths. For example, the collection of the shortest paths in a...     

A framework for production control in health care organizations

The paper presents a hierarchical framework for production control of hospitals which deals with the balance between service and efficiency, at all levels of planning and control. The framework is based on an analysis of the design requirements for hospital production control systems. These design requirements are translated into the control functions at different levels of planning required for hospital production control. The framework consists of five levels of planning and control: patient planning and control, patient group planning and control, resources planning and control, patient volumes planning and control and strategic planning, though this last level does not make part of production control as such. Each of the levels of the framework is further elaborated in terms of the decisions made regarding patient flows and resources, and the co-ordination of the different planning levels. Implications of the framework are discussed by describing some points where current practice deviates from assumptions made in our approach. Recommendations for future research and development of the planning framework are formulated.     

A framework for production rescheduling in sociotechnical manufacturing environments

No plan survives contact with reality. Despite the rich research base regarding handling uncertainty in production planning and control systems, there is an intellectual gap between theory and practice with regard to handling unforeseen events generated by internal and external factors, such as unforeseen machine downtimes and changes in demand. Motivated by longitudinal observations in two industrial settings and an analysis of the relevant literature, a framework for rescheduling decision-making in the face of unforeseen production events is proposed. In practical settings, the effectiveness of decisions depends on a set of situational factors. The findings of this research can be utilised further to provide guidelines for developing effective decision support principles and systems, addressing the needs of organisational decision-makers.     

A framework for studying practical production scheduling

The aim of this paper is to present what we believe are the most relevant findings and results regarding practical scheduling in order to define practical production scheduling and create a framework that helps researchers to study the various topics that fall under the umbrella of practical production scheduling and to identify the current state of knowledge for each topic. Studies from different fields were analysed and included in this paper, contributing significant knowledge to build a definition of practical production scheduling. Finally, we discuss the applicability that scheduling, as a task, could have in real companies.     

A conceptual framework for nurse staffing management

Hospitals are under increasing pressure to contain cost and nurse staffing has been singled out as an area with high potential for efficiency improvement. Despite the multitude of nurse staffing studies conducted, many important aspects have been neglected and few innovations have been successfully implemented. This paper presents a framework that includes all management tasks that affect staff effectiveness including systems design, forecasting, planning, coordination, and performance monitoring. A consistent terminology is developed and several previously ignored dimensions of the nurse staffing process are discussed. Finally, an outline for a decision-oriented information system is presented.     

A robust optimization model for stochastic aggregate production planning

The aggregate production planning (APP) problem considers the medium-term production loading plans subject to certain restrictions such as production capacity and workforce level. It is not uncommon for management to often encounter uncertainty and noisy data, in which the variables or parameters are stochastic. In this paper, a robust optimization model is developed to solve the aggregate production planning problems in an environment of uncertainty in which the production cost, labour cost, inventory cost, and hiring and layoff cost are minimized. By adjusting penalty parameters, decision-makers can determine an optimal medium-term production strategy including production loading plan and workforce level while considering different economic growth scenarios. Numerical results demonstrate the robustness and effectiveness of the proposed model. The proposed model is realistic for dealing with uncertain economic conditions. The analysis of the tradeoff between solution robustness and model robustness is also presented.     

A note on the duality between matchings and vertex covers in balanced hypergraphs

We present a new Min-Max theorem for an optimization problem closely connected to matchings and vertex covers in balanced hypergraphs. The result generalizes K?nig’s Theorem (Berge and Las Vergnas in Ann N Y Acad Sci 175:32–40, 1970; Fulkerson et al. in Math Progr Study 1:120–132, 1974) and Hall’s Theorem (Conforti et al. in Combinatorica 16:325–329, 1996) for balanced hypergraphs.     

Risk filtering, ranking, and management framework using hierarchical holographic modeling.

This paper contributes a methodological framework to identify, prioritize, assess, and manage risk scenarios of a large-scale system. Qualitative screening of scenarios and classes of scenarios is appropriate initially, while quantitative assessments may be applied once the set of all scenarios (hundreds) has been prioritized in several phases. The eight-phase methodology is described in detail and is applied to operations other than war. The eight phases are as follows: Phase I, Scenario Identification-A hierarchical holographic model (HHM) is developed to describe the system's "as planned" or "success" scenario. Phase II, Scenario Filtering-The risk scenarios identified in Phase I are filtered according to the responsibilities and interests of the current system user. Phase III, Bi-Criteria Filtering and Ranking. Phase IV, Multi-Criteria Evaluation. Phase V, Quantitative Ranking-We continue to filter and rank scenarios based on quantitative and qualitative matrix scales of likelihood and consequence; and ordinal response to system resiliency, robustness, redundancy. Phase VI, Risk Management is performed, involving identification of management options for dealing with the filtered scenarios, and estimating the cost, performance benefits, and risk reduction of each. Phase VII, Safeguarding Against Missing Critical Items--We examine the performance of the options selected in Phase VI against the scenarios previously filtered out during Phases II to V. Phase VIII, Operational Feedback-We use the experience and information gained during application to refine the scenario filtering and decision processes in earlier phases. These eight phases reflect a philosophical approach rather than a mechanical methodology. In this philosophy, the filtering and ranking of discrete scenarios is viewed as a precursor to, rather than a substitute for, consideration of the totality of all risk scenarios.     

A conceptual framework for evaluating designs for corporate innovation

Innovation is becoming mandatory for an increasing number of organizations in order to sustain competitive advantage. This paper explores how designs for corporate entrepreneurship fit within other dimensions of a strategy configuration. To do this, first, seven innovation-related problems are diagnosed. The specific problem patterns arising in three representative approaches to corporate innovation efforts (internal research and development, joint ventures, and acquisition) are discussed. Next, issues related to strategy configuration are examined. The problem-accommodating characteristics associated with specific environmental domain choices and with two routes to competitive advantage are proposed. Hypotheses that reflect the problem characteristics of innovation approaches with the problem propensities resulting from organizational choices of domain and competitive advantages are offered. Specific directions for future research, as well as the practical applications of these concepts, are discussed.     

Hierarchical linear modeling as an example for measuring change over time in a leadership development evaluation context

One of the difficulties in evaluating leadership development is measuring whether and how people change over the time period of the leadership development initiative. Even when change over time is an inherent part of the design and evaluation of leadership development, events may occur outside of the control of evaluators that limit the effectiveness of adequately and accurately assessing change over time. With data from a leadership development initiative designed to account for change over time, this article suggests hierarchical linear modeling (HLM) as a multilevel methodological technique to assess change over time in a leadership development context. This article will use real change over time data from a leadership development initiative and discuss the logic and rationale of HLM. We use HLM as an example of a multilevel methodological tool to investigate typical change over time questions in leadership development evaluation.