Codifying a crisis: Progressing from information sharing to distributed decision‐making

A key challenge in crisis management is maintaining an adequate information position to support coherent decision‐making between a range of actors. Such distributed decision‐making is often supported by a common operational picture that not only conveys factual information but also attempts to codify a dynamic and vibrant crisis management process. In this paper, we explain why it is so difficult to move from information sharing towards support for distributed decision‐making. We argue that two key processes need to be considered: supporting both the translation of meaning and the transformation of interests between those on the front line and those in the remote response network. Our analysis compares the information‐sharing processes in three large‐scale emergency response operations in the Netherlands. Results indicate that on several occasions the collaborative decision‐making process was hampered because actors limited themselves to factual information exchange. The decision‐making process only succeeds when actors take steps to resolve their varying interpretations and interests. This insight offers important lessons for improving information management doctrines and for supporting distributed decision‐making processes.     

An approach to group decision making with hesitant information and its application in credit risk evaluation of enterprises

Hesitant multiplicative preference relation (HMPR) contains much more comprehensive information than the traditional multiplicative preference relations. The HMPR is a useful tool to help the decision makers express their preferences in group decision making under uncertainty. The key of group decision making with the HMPR is to derive the priority weights from the HMPR. Thus, an efficient and practical priority method should be put forward so as to ensure the reasonability of the final decision result. In order to do that, in this paper, we first introduce the expected value and the geometric average value of hesitant multiplicative element (HME) which is the component of the HMPR. Then from different perspectives, we utilize the error-analysis technique to put forward three novel methods for the priorities of the HMPR, i.e., the expectation value method, the geometric average value method, and the multiplicative deviation method. We also investigate the relationships among these methods, and develop an approach to group decision making with the HMPR by using the methods and the possibility degree formula. Finally, by constructing the indicator system for credit risk evaluation of supply chain enterprises, we make a detailed case study concerning Lu-Zhou-Lao-Jiao (the well-known liquor enterprise in China) to demonstrate our approach.     

A pseudo‐stochastic approach for optimal decision making under limited information: a case of an aggregate production system

In this study, which is both analytical and numerical, we compute the effective information horizon (EIH), i.e., the minimal time interval over which future information is relevant for optimal control and for measuring the performance of a single part‐type production system. Optimal control modeling and process solving, which consider aspects of decision making with limited forecast, are exemplified by a single part‐type production system. Specifically, the analysis reveals practical situations in which there is both a performance loss as well as feasibility violation when only information expected within the planning horizon is considered. The analysis is carried out by developing a pseudo‐stochastic model. We follow previous “pseudo‐stochastic” approaches that solve stochastic control problems by using deterministic, optimal control methods. However, we model the expected influences of all future events, including those that are beyond the planning horizon, as encapsulated by their density functions and not only by their mean values.     

A nonparametric approach to design robust controllers for uncertain systems: Application to an air flow heating system

This paper presents an approach to design robust fixed structure controllers for uncertain systems using a finite set of measurements in the frequency domain. In traditional control system design, usually, based on measurements, a model of the plant, which is only an approximation of the physical system, is first built, and then control approaches are used to design a controller based on the identified model. Errors associated with the identification process as well as the inevitable uncertainties associated with plant parameter variations, external disturbances, measurement noise, etc. are expected to all contribute to the degradation of the performance of such a scheme. In this paper, we propose a nonparametric method that uses frequency-domain data to directly design a robust controller, for a class of uncertainties, without the need for model identification. The proposed technique, which is based on interval analysis, allows us to take into account the plant uncertainties during the controller synthesis itself. The technique relies on computing the controller parameters for which the set of all possible frequency responses of the closed-loop system are included in the envelope of a desired frequency response. Such an inclusion problem can be solved using interval techniques. The main advantages of the proposed approach are: (1) the control design does not require any mathematical model, (2) the controller is robust with respect to plant uncertainties, and (3) the controller structure can be chosen a priori, which allows us to select low-order controllers. To illustrate the proposed method and demonstrate its efficacy, an application to an air flow heating system is presented.     

A multi-level approach to investigate the control of an input device: application to a realistic pointing task

This study investigates the subjects' performance during realistic conditions of control of a joystick. An adapted reciprocal aiming task consisting in driving a virtual vehicle along a slalom course as fast as possible was performed while accuracy constraints were manipulated. Realistic dynamical Interface Screen Relationship between the joystick displacements and the displacements of the vehicle was simulated. Vehicle displacements and motor activity (muscle activity and joint kinematics) were recorded. The results highlighted the applicability of the Fitts' law to more realistic conditions where the use of an input device is performed in an intensive control situation. Besides, biomechanical results suggested that neuromuscular responses were different regarding the direction of movement, whereas the performance at a behavioural level were not affected. Thus, this study demonstrates the interest in considering two different aspects of the user's performance (behavioural and biomechanical ones) to make a better agreement between the device design and users' needs.