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.

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

scholarly journals Multi-Objective Model Predictive Control for Real-Time Operation of a Multi-Reservoir System

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1898 ◽  
Author(s):  
Nay Myo Lin ◽  
Xin Tian ◽  
Martine Rutten ◽  
Edo Abraham ◽  
José M. Maestre ◽  
...  
Keyword(s):  
Decision Making ◽  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Nsga Ii ◽  
Multi Objective ◽  
Reservoir System ◽  
Time Operation ◽  
Real Time Operation ◽  
Objective Model

This paper presents an extended Model Predictive Control scheme called Multi-objective Model Predictive Control (MOMPC) for real-time operation of a multi-reservoir system. The MOMPC approach incorporates the non-dominated sorting genetic algorithm II (NSGA-II), multi-criteria decision making (MCDM) and the receding horizon principle to solve a multi-objective reservoir operation problem in real time. In this study, a water system is simulated using the De Saint Venant equations and the structure flow equations. For solving multi-objective optimization, NSGA-II is used to find the Pareto-optimal solutions for the conflicting objectives and a control decision is made based on multiple criteria. Application is made to an existing reservoir system in the Sittaung river basin in Myanmar, where the optimal operation is required to compromise the three operational objectives. The control objectives are to minimize the storage deviations in the reservoirs, to minimize flood risks at a downstream vulnerable place and to maximize hydropower generation. After finding a set of candidate solutions, a couple of decision rules are used to access the overall performance of the system. In addition, the effect of the different decision-making methods is discussed. The results show that the MOMPC approach is applicable to support the decision-makers in real-time operation of a multi-reservoir system.

scholarly journals FASTEN IIoT: An Open Real-Time Platform for Vertical, Horizontal and End-To-End Integration

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5499
Author(s):  
Felipe S. Costa ◽  
Silvia M. Nassar ◽  
Sergio Gusmeroli ◽  
Ralph Schultz ◽  
André G. S. Conceição ◽  
...  
Keyword(s):  
Real Time ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Business Models ◽  
Production Systems ◽  
Transformation Process ◽  
Digital Transformation ◽  
Shop Floor ◽  
Decentralized Decision Making ◽  
End To End

The Industry 4.0 paradigm, since its initial conception in Germany in 2011, has extended its scope and adoption to a broader set of technologies. It is being considered as the most vital mechanism in the production systems lifecycle. It is the key element in the digital transformation of manufacturing industry all over the world. This scenario imposes a set of major unprecedented challenges which require to be overcome. In order to enable integration in horizontal, vertical, and end-to-end formats, one of the most critical aspects of this digital transformation process consists of effectively coupling digital integrated service/products business models with additive manufacturing processes. This integration is based upon advanced AI-based tools for decentralized decision-making and for secure and trusted data sharing in the global value. This paper presents the FASTEN IIoT Platform, which targets to provide a flexible, configurable, and open solution. The platform acts as an interface between the shop floor and the industry 4.0 advanced applications and solutions. Examples of these efforts comprise management, forecasting, optimization, and simulation, by harmonizing the heterogeneous characteristics of the data sources involved while meeting real-time requirements.

The modelling and application of cross-scale human behavior in realizing the shop-floor digital twin

Digital Twin ◽  
2021 ◽  
Vol 1 ◽  
pp. 11
Author(s):  
Tingyu Liu ◽  
Mengming Xia ◽  
Qing Hong ◽  
Yifeng Sun ◽  
Pei Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Human Behavior ◽  
Manufacturing Industry ◽  
Behavior Modeling ◽  
Shop Floor ◽  
Human Behavior Modeling ◽  
Digital Twin ◽  
Production Behavior ◽  
Time Position ◽  
Human Limb

The digital twin shop-floor has received much attention from the manufacturing industry as it is an important way to upgrade the shop-floor digitally and intelligently. As a key part of the shop-floor, humans' high autonomy and uncertainty leads to the difficulty in digital twin modeling of human behavior. Therefore, the modeling system for cross-scale human behavior in digital twin shop-floors was developed, powered by the data fusion of macro-behavior and micro-behavior virtual models. Shop-floor human macro-behavior mainly refers to the role of the human and their real-time position. Shop-floor micro-behavior mainly refers to real-time human limb posture and production behavior at their workstation. In this study, we reviewed and summarized a set of theoretical systems for cross-scale human behavior modeling in digital twin shop-floors. Based on this theoretical system, we then reviewed modeling theory and technology from macro-behavior and micro-behavior aspects to analyze the research status of shop-floor human behavior modeling. Lastly, we discuss and offer opinion on the application of cross-scale human behavior modeling in digital twin shop-floors. Cross-scale human behavior modeling is the key for realizing closed-loop interactive drive of human behavior in digital twin shop-floors.

Multi-Agent Economically Motivated Decision-Making

2014 ◽  
pp. 165-183 ◽  
Author(s):  
Paulo Trigo
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Electricity Markets ◽  
Simulation Tool ◽  
Strategic Behaviour ◽  
Decision Making Processes ◽  
Multi Agent ◽  
Near Future

The key motivation for this chapter is the perception that within the near future, markets will be composed of individuals that may simultaneously undertake the roles of consumers, producers and traders. Those individuals are economically motivated “prosumer” (producer-consumer) agents that not only consume, but can also produce, store and trade assets. This chapter describes the most relevant aspects of a simulation tool that provides (human and virtual) prosumer agents an interactive and real-time game-like environment where they can explore (long-term and short-term) strategic behaviour and experience the effects of social influence in their decision-making processes. The game-like environment is focused on the simulation of electricity markets, it is named ITEM-game (“Investment and Trading in Electricity Markets”), and it is publically available (ITEM-Game, 2013) for any player to explore the role of a prosumer agent.

A dynamic processing methodology of manufacturing data for the automated throughput analysis in cyber-physical production environment

2019 ◽  
Vol 27 (2) ◽  
pp. 155-169 ◽  
Author(s):  
Hyoung Seok Kang ◽  
Ju Yeon Lee ◽  
Sang Do Noh
Keyword(s):  
Real Time ◽  
Manufacturing Industry ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Systematic Research ◽  
Practical Implementation ◽  
Successful Implementation ◽  
Production Environment ◽  
Throughput Analysis ◽  
Systematic Methodology

Various studies have been conducted on cyber-physical production systems (CPPS), a core technology for the implementation of smart manufacturing. However, existing studies are mostly conceptual or at an early stage, such as the proposal of a reference architecture. To achieve the practical implementation of CPPS, a systematic methodology for the collection, processing, and application of the data for CPPS is required. This is because CPPS can be successfully implemented only when processing criteria and application methods for the diverse data that change in real time because of the nature of a manufacturing shop floor are presented. Various technologies and systems have been developed for collecting raw data from a shop floor, but they are mainly focused on the automation of manufacturing. Thus, more detailed and systematic research is required for more efficient application of such technologies using a cyber model, which is the core of CPPS. For this purpose, in this article, a logic-based systematic methodology that can generate a throughput analysis model from the real-time data of a shop floor in a CPPS environment was proposed. Furthermore, logics that perform the Mapping, Scaling, and Calibration of the data of the shop floor into the machine, process, and factory levels were developed and their application to throughput analysis was described through a case study. The results of this study are expected to facilitate the practical implementation of CPPS and contribute to the successful implementation of smart manufacturing and the resultant revival of the manufacturing industry.

Performance Measurement of Implementing Manufacturing Execution System

2006 ◽  
Vol 505-507 ◽  
pp. 1117-1122 ◽  
Author(s):  
Kai Ying Chen
Keyword(s):  
Decision Making ◽  
Performance Measurement ◽  
Real Time ◽  
Computer Application ◽  
Shop Floor ◽  
Manufacturing Execution System ◽  
Analytic Hierarchy ◽  
Multi Attribute Decision Making ◽  
Real Time Information ◽  
Manufacturing Execution

Manufacturing execution system (MES) is a computer application system that integrates all relevant real-time information along every step in a manufacturing process. MES is essential for manufacturers for gathering real-time production line information, supporting manufacturing decision making and increasing manufacturing efficiency. MES is a must for operation control and lot tracking in the shop floor of a plant with complicated manufacturing processes, such as semiconductor manufacturing fab or TFT-LCD manufacturing plant. This paper presents the formulization of performance measurement of implementing MES from several quantitative and qualitative aspects by analyzing the basic functions and objectives of MES and interviewing with some senior consultants and MES related working staff. In addition, multi-attribute decision making technique, analytic hierarchy process (AHP) is used to decide the priority of these performance measurement indices. The results of this research will be useful for a company when MES is an existing system or is a To Be system.

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