Flow Shop Scheduling Problems Under Machine–Dependent Precedence Constraints

The paper considers the flow shop scheduling problems to minimize the makespan, provided that an individual precedence relation is specified on each machine. A fairly complete complexity classification of problems with two and three machines is obtained.     

The evolution and problems of model management research

Models are a key resource for organizational decision making. The diversity, complexity, and reusability of this resource result in the need for model management systems. The construction of a model management system involves a modeling task dimension and a design level dimension. The modeling task dimension consists of model formulation, model representation, and model processing. The design level dimension addresses the architectural requirements of a system from a user's standpoint and a computer system's standpoint.The numerous architectures suggested in the model management systems literature address isolated areas identified by these dimensions. The research surveyed in this paper indicates that the primary focus has been on the system view of model representation. Before model management systems can be widely used in organizations, model management researchers must explore systems that address all areas of the task and design level dimensions.In this paper, we identify and justify the necessary dimensions of model management research. Next, the existing model management research is critically reviewed. Finally, neglected research areas are discussed, and investigations necessary for the development of integrated model management systems are suggested.     

Diagnostic bias in organizational consultation

This article is intended to improve consultation practice by explaining the significance of models, heuristics, and diagnostic aids in organizational diagnosis. The role of diagnostic models in determining the consultant's frame of reference is explained. Evidence that organizational consultants consciously use diagnostic models is provided by citing two independent studies that revealed about 70% of the consultants reported that a diagnostic model was used. The availability, representativeness, and anchoring heuristics used by diagnosticians are summarized and examples of their use are cited. Readers are warned that bias occurs from misapplying the heuristics. However, guidance on correctly applying the heuristics is provided. The development and improvement of diagnostic expertise is explained from the perspective of learning through education as well as feedback received from clients, colleagues (as shadow consultants and as consulting team members), and self. Thus, written from the perspective of a consultant, the article provides a practical presentation on diagnostic bias, supported by theory and empirical research. It should appeal not only to a consulting constituency (i.e. practicing consultants, consultant trainers, and novice consultants), but also to the large number of potential clients (i.e. organizational decision-makers). Future research directions are discussed in terms of (1) the research that has identified relationships between consultant characteristics (e.g. Jungian constructs) and diagnostic information, and (2) the potential of expert systems in organizational diagnosis.