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Analysing Tactical Cognitive Systems: Theories, Models and Methods
Published in Peter Berggren, Staffan NäHlinder, Erland Svensson, Assessing Command and Control Effectiveness, 2017
Real-time properties of dynamic decision-making cause certain problems. Initially, decision-makers are not free to make decisions when they feel ready to do so. Instead, the mission environment requires decisions, and a decision-maker – ready or not – has to make these decisions more or less on demand, something which causes stress in dynamic decision-making tasks. In order to cope with this stress, decision-makers have to develop strategies for controlling assigned dynamic tasks and for keeping their own workload at an acceptable level. Additionally, both the system that is to be controlled and the procedures and resources the decision-maker employs to control the system have to be seen and treated as processes. Dynamic decision-making tasks can therefore be characterized as finding a way to use one process to control another process. Lastly, the different time scales involved in dynamic decision-making tasks have to be monitored and taken into consideration. In most situations, the active agents in a dynamic system, such as the directly involved operators and their closest commander or squad leader, operate in a time scale of seconds to minutes. Their commanders and their command and control systems operate in time scales of hours to days. An application of this approach in studies of distributed decision-making in dynamic environments, such as fire-fighting and rescue missions, is described by Brehmer and Svenmarck (1995).
A mixed-model assembly line sequencing problem with parallel stations and walking workers: a case study in the automotive industry
Published in International Journal of Production Research, 2023
Mojtaba Ebrahimi, Mehdi Mahmoodjanloo, Behnam Einabadi, Armand Baboli, Eva Rother
Dynamic decision-making is generally based on historical and real-time information and data. The application of these decisions requires high agility and flexibility in the physical part of the system within a very short reaction time. Moreover, this approach also requires high levels of flexibility in information systems that involve a reconfigurable Internet of Things (IoT). Collaboration between experts in production systems, computer scientists, data scientists, etc. guarantees the applicability and efficiency of the decisions made. This paper also studied the efficiency and applicability of simulation and optimisation tools in a real-world industrial case. In industries, both simulation and mathematical modelling can be used, but complexity of development and reaction time have a big impact on the selection of decision-making approaches. The advantages and disadvantages of using the mathematical modelling method compared with the simulation method (with an optimisation tool) are discussed below, in order to allow for their use in real-world applications. Moreover, the result of these comparisons is summarised in Figure 10.
Dynamic design method of digital twin process model driven by knowledge-evolution machining features
Published in International Journal of Production Research, 2022
Jinfeng Liu, Peng Zhao, Xuwen Jing, Xuwu Cao, Sushan Sheng, Honggen Zhou, Xiaojun Liu, Feng Feng
Nowadays, collaborative manufacturing and intelligent manufacturing driven by digital twin have become the current research direction and manufacturing innovation trend. In particular, constructing DTPM is seen as the key technology for intelligent process design. The above studies provide solutions for real-time simulation, process optimisation, and dynamic decision-making in the machining workshop. However, most of the literatures focused on process equipment modeling based on digital twin, and little attention is paid to the modeling of processing evolution feature. The DTPM which can achieving real-time monitoring and simulation has an important role in improving the processing quality and reducing repeated corrections.
Analysis of Bullwhip effect: A Behavioral Approach
Published in Supply Chain Forum: An International Journal, 2019
Maaz Hasan Khan, Salma Ahmed, Danish Hussain
BDG employs opaque rules, implicit learning and dynamic decisions (Guastello 2013). Opaque rules indicate that an individual player is not given explicit instructions so that they cannot do the exact mental mapping of the system running in the form of a simulation. Dynamic decision-making is more complex as it considers the problem in real-life manner considering all the possible changes to the environment of the dynamic system that may occur at any instance. Hardman and Hardman (2009) state that BDG involves dynamic decision making hence making the system more complex without fixed rules whereas conscientious people are good follower of rules. The rules in the BDG are not very explicit, and a player has to make his own decision indicating that one facet of Conscientiousness which is helpful in achieving good job performance in other professional jobs is rendered not as useful in the BDG. This kind of decision-making is quite different from other professional jobs as they involve implicit learning which means learning the more subtle things in pursuit of the explicit things such as the broader goals (Seger 1994). To perform better in tasks which require implicit learning one must have better cognitive ability whereas several studies have found a negative association between the Conscientiousness trait and cognitive ability (Murray et al. 2014). Moutafi, Furnham, and Paltiel (2004) have proposed an Intelligence Compensation Hypotheses (ICH) with subsequent replications interpreted as support to hypotheses. The author asserts that because of low cognitive ability an individual becomes more conscientious than his companion, who scores high on cognitive ability, for the same task. This is quite indicative that for a novel and complex situation like BDG, which require implicit learning one must not only be achievement-oriented, dutiful and self-disciplined but should also possess the better cognitive ability.