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This paper describes a study that explored human capabilities in supervisory control of a flexible manufacturing system (FMS). Implications of current design strategies concerning the human supervisory role in FMSs are discussed. Also examined are issues in human supervisory control that arise from features characteristic of these types of automated systems. A real-time interactive simulation methodology is described that served as the basis for evaluating human performance. Results are explained in terms of two frameworks: (1) one that incorporated hypothetical dimensions of the mental workload construct into various task conditions; and (2) a simple decision-making model intrinsic to which was the imposition on the human of a relatively well-defined locus of control. The effectiveness of graphics was evaluated as a decision-support device aimed at enhancing human pattern-recognition capabilities.

PurposeThis paper aims to report a study into the levels of abstraction hierarchy (LOAH) in two energy distribution teams. The original proposition for the LOAH was that it depicted five levels of system representation, working from functional purpose through to physical form to determine causes of a malfunction, or from physical form to functional purpose to determine the purpose of system function. The LOAH has been widely used throughout human supervisory control research to explain individual behaviour. The research seeks to focus on the application the LOAH to human supervisory control teams in semi‐automated "intelligent" systems.Design/methodology/approachA series of interviews were conducted in two energy distribution companies.FindingsThe results of the study suggest that people in the teams are predominantly operating at different levels of system representation, depending on their role. Managerial personnel work at functional purpose and abstract function levels, whereas operational personnel work at physical function and physical form levels. It is argued that both types of personnel are part of the wider distributed problem‐solving system, which includes both people and technology.Originality/valueThe research provides useful information on the application of the LOAH to human supervisory control teams in semi‐automated "intelligent" systems.

Primary ethnographic research is drawn on in this paper so as to examine the nature of supervisory systems in two manufacturing organisations which have both, to varying degrees, implemented `new' manufacturing techniques such as just-in-time and total quality management and have organised around `teams'. Debates in industrial sociology and the labour process have understandably concentrated on the implications of such developments for workers; the important and problematic role of supervisors in realising managerial objectives has been largely neglected. This paper analyses the nature of supervision and the role of supervisors/first-line managers within contemporary manufacturing. In contrast to previous studies, which have sought to explain supervisory roles in terms of their link with structural factors such as technology, organisational size and formalisation (Perrow 1970; Woodward 1965), this paper highlights the importance of supervisors as social actors. The analysis demonstrates the dynamic and complex role of supervisors in implementing and adhering to managerial rules while needing to ensure a degree of operational flexibility that relies on informality, particularly in reaching accommodation with labour. These types of contradictory pressures have long been recognised in supervisory work (Roethlisberger 1945) but recent research into developments on the `new' shopfloor has failed adequately to report and conceptualise the increasingly complex position of supervisors and front-line managers.

Autonomous mobile sensor teams are crucial to many civilian and military applications. These robotic teams often operate within a larger supervisory system, involving human operators who oversee the mission and analyze sensory data. Here, both the human and the robotic system sub-components, as well as interactions between them, must be carefully considered in designing effective mission coordination strategies. This dissertation explores a series of representative sub-problems relating to the analysis and coordination of both mobile sensors and human operators within supervisory systems. The content herein is presented in three parts: Part I focuses on coordinating operator behavior independently (operator-focused methods), Part II focuses on coordinating mobile-sensor behavior independently (sensor-focused methods), and Part III focuses on jointly coordinating both operator and mobile sensor behavior (joint methods). The content herein is primarily motivated by a particular application in which Unmanned Aerial Vehicles collect visual imagery to be analyzed by a remotely located operator, although many of the results apply to any system of similar architecture. Specifically, with regard to operator-focused methods, Chapter 2 illustrates how physiological sensing, namely eye tracking, may provide aid in modeling operator behavior and assessing the usability of user interfaces. The results of a pilot usability study in which human observers interact with a supervisory control interface are presented, and eye-tracking data is correlated with various usability metrics. Chapter 3 develops robust scheduling algorithms for determining the ordering in which operators should process sensory tasks to both boost performance and decrease variance. A scenario-based, Mixed-Integer Linear Program (MILP) framework is presented, and is assessed in a series of numerical studies. With regard to sensor-focused methods, Chapters 4 and 5 consider two types of supervisory surveillance missions:Chapter 4 develops a cloud-based coverage strategy for persistent surveillance of planar regions. The scheme operates in a dynamic environment, only requiring sporadic, unplanned data exchanges between a central cloud and the sensors in the field. The framework is shown to provide collision avoidance and, in certain cases, produce convergence to a Pareto-optimal coverage configuration. In chapter 5, a heuristic routing scheme is discussed to produce Dubins tours for persistent surveillance of discrete targets, each with associated visibility and dwell-time constraints. Under some assumptions, the problem is posed as a constrained optimization that seeks a minimum-length tour, while simultaneously constraining the time required to reach the first target. A sampling-based scheme is used to approximate solutions to the constrained optimization. This approach is also shown to have desirable resolution completeness properties.Finally, Chapter 6 explores joint methods for coordinating both operator and sensor behavior in the context of a discrete surveillance mission (similar to that of Chapter 5), in which UAVs collect imagery of static targets to be analyzed by the human operator.In particular, a method is proposed to simultaneously construct UAV routes and operator schedules, with the goal of maintaining the operator's task load within a high-performance regime and preventing unnecessary UAV loitering. The full routing/scheduling problem is posed as a mixed-integer (non-linear) program, which can be equivalently represented as a MILP through the addition of auxiliary variables. For scalability, a MILP-based receding-horizon method is proposed to incrementally construct suboptimal solutions to the full optimization problem, which can be extended using a scenario-based approach (similar to that of Chapter 3) to incorporate robustness to operator uncertainty.