scholarly journals A decentralized ITS architecture for efficient distribution of traffic task management

2018 ◽  
Author(s):  
Amilcare Francesco Santamaria ◽  
Mauro Tropea ◽  
Peppino Fazio ◽  
Pierfrancesco Raimondo ◽  
Floriano De Rango ◽  
...  
Keyword(s):  
Task Management ◽  
Efficient Distribution

Factors affecting task management in aviation

2004 ◽  
Author(s):  
Cristina Iani ◽  
Christopher D. Wickens
Keyword(s):  
Task Management ◽  
Factors Affecting

Team leadership and its personal background in the studengroup

2020 ◽  
Vol 1 (100) ◽  
pp. 42-49
Author(s):  
A.M. Pysarenko ◽  
Keyword(s):  
Team Leadership ◽  
Leadership Team ◽  
Specific Situation ◽  
Task Management ◽  
Leadership Qualities ◽  
Organizational Skills ◽  
Self Development ◽  
Management Functions ◽  
Psychological Features ◽  
Difficult Situations

The article anylyses the theoretical and methodological basis for the study of the problem of team leadership in the student environment: the importance of team formation in the student environment, the essence of the concept of "leadership", the psychological components of effective team leadership. Team leadership is seen as the ability of a leader to gain authority in one’s group, thereby gaining the primary right to make group decisions, as well as to recognize the strengths of others and delegate task management functions to others. Also, command leadership is seen as a process of allocating authoritative personalities in a group and facilitating them to develop leadership qualities of other members of the group, which leads to the emergence of coordinated teamwork. It is noted that tactics of the leader’s influence on the group can determine the effectiveness of team leadership. His typical actions, internal psychological features, ability to update the desired features in a specific situation. The authors consider the internal psychological features of students, which determine the command style of leadership, as follows: flexibility, originality, critical thinking, orientation to solving problems in difficult situations; desire for cooperation, diplomacy, ability to manage and resolve conflicts, organizational skills, communication skills; striving for self-development and self-improvement. The essence of the empirical study of the psychological components of effective team leadership in a student environment is highlighted.

scholarly journals Optimal placement design for picking and packing in distribution centers

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110106
Author(s):  
John Rios ◽  
Rodrigo Linfati ◽  
Daniel Morillo-Torres ◽  
Iván Derpich ◽  
Gustavo Gatica
Keyword(s):  
Optimal Allocation ◽  
Programming Model ◽  
Operating Cost ◽  
Nonlinear Integer Programming ◽  
Operating Costs ◽  
Optimal Placement ◽  
Distribution Centers ◽  
Suitable Space ◽  
Efficient Distribution

An efficient distribution center (DC) is one that receives, stores, picks and packs products into new logistics units and then dispatches them to points of sale at the minimal operating cost. The picking and packing processes represent the highest operating cost of a DC, and both require a suitable space for their operation. An effective coordination between these zones prevents bottlenecks and has a direct impact on the DC’s operational results. In the existing literature, there are no studies that optimize the distribution of the picking and packing areas simultaneously while also reducing operating costs. This article proposes an integer nonlinear integer programming model that minimizes order preparation costs. It does so by predicting customer demand based on historical data and defining the ideal area for picking and packing activities. The model is validated through a real case study of seven clients and fifteen products. It achieves a [Formula: see text] reduction in operating costs when the optimal allocation of the picking and packing areas is made.

scholarly journals An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1788
Author(s):  
Gomatheeshwari Balasekaran ◽  
Selvakumar Jayakumar ◽  
Rocío Pérez de Prado
Keyword(s):  
Execution Time ◽  
Autonomous Vehicles ◽  
Rapid Development ◽  
Single Layer ◽  
Battery Life ◽  
Learning Approaches ◽  
Task Management ◽  
Inference System ◽  
Safe Driving ◽  
Self Driving Cars

With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms).Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods.

scholarly journals Severity-Mapped Vibrotactile Cues to Support Interruption Management with Weather Messaging in the General Aviation Cockpit

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 341
Author(s):  
Carolina Rodriguez-Paras ◽  
Johnathan T. McKenzie ◽  
Pasakorn Choterungruengkorn ◽  
Thomas K. Ferris
Keyword(s):  
Situation Awareness ◽  
Failure Modes ◽  
General Aviation ◽  
Flight Training ◽  
Mountainous Terrain ◽  
Task Management ◽  
Training Device ◽  
Cognitive Demands ◽  
Weather Information ◽  
Interruption Management

Despite the increasing availability of technologies that provide access to aviation weather information in the cockpit, weather remains a prominent contributor to general aviation (GA) accidents. Pilots fail to detect the presence of new weather information, misinterpret it, or otherwise fail to act appropriately on it. When cognitive demands imposed by concurrent flight tasks are high, the risks increase for each of these failure modes. Previous research shows how introducing vibrotactile cues can help ease or redistribute some of these demands, but there is untapped potential in exploring how vibratory cues can facilitate “interruption management”, i.e., fitting the processing of available weather information into flight task workflow. In the current study, GA pilots flew a mountainous terrain scenario in a flight training device while receiving, processing, and acting on various weather information messages that were displayed visually, in graphical and text formats, on an experimental weather display. Half of the participants additionally received vibrotactile cues via a connected smartwatch with patterns that conveyed the “severity” of the message, allowing pilots to make informed decisions about when to fully attend to and process the message. Results indicate that weather messages were acknowledged more often and faster when accompanied by the vibrotactile cues, but the time after acknowledgment to fully process the messages was not significantly affected by vibrotactile cuing, nor was overall situation awareness. These findings illustrate that severity-encoded vibrotactile cues can support pilot awareness of updated weather as well as task management in processing weather messages while managing concurrent flight demands.

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