scholarly journals Intelligent Sensors for Real-Time Decision-Making

Automation ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 62-82
Author(s):  
Tiago Coito ◽  
Bernardo Firme ◽  
Miguel S. E. Martins ◽  
Susana M. Vieira ◽  
João Figueiredo ◽  
...  
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Dynamic Scheduling ◽  
Automatic Identification ◽  
Use Case ◽  
Processing Times ◽  
Intelligent Sensors ◽  
Simultaneous Integration ◽  
Business Data ◽  
Scheduling Tool

The simultaneous integration of information from sensors with business data and how to acquire valuable information can be challenging. This paper proposes the simultaneous integration of information from sensors and business data. The proposal is supported by an industrial implementation, which integrates intelligent sensors and real-time decision-making, using a combination of PLC and PC Platforms in a three-level architecture: cloud-fog-edge. Automatic identification intelligent sensors are used to improve the decision-making of a dynamic scheduling tool. The proposed platform is applied to an industrial use-case in analytical Quality Control (QC) laboratories. The regulatory complexity, the personalized production, and traceability requirements make QC laboratories an interesting use case. We use intelligent sensors for automatic identification to improve the decision-making of a dynamic scheduling tool. Results show how the integration of intelligent sensors can improve the online scheduling of tasks. Estimations from system processing times decreased by over 30%. The proposed solution can be extended to other applications such as predictive maintenance, chemical industry, and other industries where scheduling and rescheduling are critical factors for the production.

scholarly journals Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1019
Author(s):  
Shengluo Yang ◽  
Zhigang Xu ◽  
Junyi Wang
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Real Time ◽  
Dynamic Scheduling ◽  
Scheduling Problems ◽  
Permutation Flowshop ◽  
Scheduling System ◽  
Single Action ◽  
Intelligent Decision Making ◽  
Intelligent Decision

Dynamic scheduling problems have been receiving increasing attention in recent years due to their practical implications. To realize real-time and the intelligent decision-making of dynamic scheduling, we studied dynamic permutation flowshop scheduling problem (PFSP) with new job arrival using deep reinforcement learning (DRL). A system architecture for solving dynamic PFSP using DRL is proposed, and the mathematical model to minimize total tardiness cost is established. Additionally, the intelligent scheduling system based on DRL is modeled, with state features, actions, and reward designed. Moreover, the advantage actor-critic (A2C) algorithm is adapted to train the scheduling agent. The learning curve indicates that the scheduling agent learned to generate better solutions efficiently during training. Extensive experiments are carried out to compare the A2C-based scheduling agent with every single action, other DRL algorithms, and meta-heuristics. The results show the well performance of the A2C-based scheduling agent considering solution quality, CPU times, and generalization. Notably, the trained agent generates a scheduling action only in 2.16 ms on average, which is almost instantaneous and can be used for real-time scheduling. Our work can help to build a self-learning, real-time optimizing, and intelligent decision-making scheduling system.

A Highly Efficient and Optimised Simulation Based Multi objective Decision-Making for FMS Control

2018 ◽  
Vol 6 (6) ◽  
pp. 98
Author(s):  
Shreyanshu Parhi ◽  
S. C. Srivastava
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Manufacturing Industry ◽  
Simulation Software ◽  
Shop Floor ◽  
Simulation Tool ◽  
Routing Flexibility ◽  
Decision System ◽  
Multi Objective ◽  
Arena Simulation

Optimized and efficient decision-making systems is the burning topic of research in modern manufacturing industry. The aforesaid statement is validated by the fact that the limitations of traditional decision-making system compresses the length and breadth of multi-objective decision-system application in FMS.  The bright area of FMS with more complexity in control and reduced simpler configuration plays a vital role in decision-making domain. The decision-making process consists of various activities such as collection of data from shop floor; appealing the decision-making activity; evaluation of alternatives and finally execution of best decisions. While studying and identifying a suitable decision-making approach the key critical factors such as decision automation levels, routing flexibility levels and control strategies are also considered. This paper investigates the cordial relation between the system ideality and process response time with various prospective of decision-making approaches responsible for shop-floor control of FMS. These cases are implemented to a real-time FMS problem and it is solved using ARENA simulation tool. ARENA is a simulation software that is used to calculate the industrial problems by creating a virtual shop floor environment. This proposed topology is being validated in real time solution of FMS problems with and without implementation of decision system in ARENA simulation tool. The real-time FMS problem is considered under the case of full routing flexibility. Finally, the comparative analysis of the results is done graphically and conclusion is drawn.

BIM-Based Quality Control for Safety Issues in the Design and Construction Phases

2015 ◽  
Vol 9 (3) ◽  
pp. 111 ◽  
Author(s):  
Seunghwa Park ◽  
Inhan Kim
Keyword(s):  
Decision Making ◽  
Traditional Method ◽  
Building Information Modeling ◽  
Safety Management ◽  
Information Modeling ◽  
Use Case ◽  
Safety Issues ◽  
Building Information ◽  
Building Safety

Today’s buildings are getting larger and more complex. As a result, the traditional method of manually checking the design of a building is no longer efficient since such a process is time-consuming and laborious. It is becoming increasingly important to establish and automate processes for checking the quality of buildings. By automatically checking whether buildings satisfy requirements, Building Information Modeling (BIM) allows for rapid decision-making and evaluation. In this context, the work presented here focuses on resolving building safety issues via a proposed BIM-based quality checking process. Through the use case studies, the efficiency and usability of the devised strategy is evaluated. This research can be beneficial in promoting the efficient use of BIM-based communication and collaboration among the project party concerned for improving safety management. In addition, the work presented here has the potential to expand research efforts in BIM-based quality checking processes.

An introduction to advance care planning: practice at the frontline

2018 ◽  
Author(s):  
Anjali Mullick ◽  
Jonathan Martin
Keyword(s):  
Decision Making ◽  
Clinical Practice ◽  
Communication Skills ◽  
Advance Care Planning ◽  
Use Case ◽  
Care Planning ◽  
Practical Advice ◽  
Future Care ◽  
Case Vignettes ◽  
Advance Care

Advance care planning (ACP) is a process of formal decision-making that aims to help patients establish decisions about future care that take effect when they lose capacity. In our experience, guidance for clinicians rarely provides detailed practical advice on how it can be successfully carried out in a clinical setting. This may create a barrier to ACP discussions which might otherwise benefit patients, families and professionals. The focus of this paper is on sharing our experience of ACP as clinicians and offering practical tips on elements of ACP, such as triggers for conversations, communication skills, and highlighting the formal aspects that are potentially involved. We use case vignettes to better illustrate the application of ACP in clinical practice.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

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