Game Theory Based Real-Time Shop Floor Scheduling Strategy and Method for Cloud Manufacturing

2016 ◽  
Vol 32 (4) ◽  
pp. 437-463 ◽  
Author(s):  
Yingfeng Zhang ◽  
Jin Wang ◽  
Sichao Liu ◽  
Cheng Qian
Keyword(s):  
Game Theory ◽  
Real Time ◽  
Cloud Manufacturing ◽  
Shop Floor ◽  
Scheduling Strategy ◽  
Shop Floor Scheduling

scholarly journals Real-Time Production and Logistics Self-Adaption Scheduling Based on Information Entropy Theory

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4507 ◽  
Author(s):  
Wenchao Yang ◽  
Wenfeng Li ◽  
Yulian Cao ◽  
Yun Luo ◽  
Lijun He
Keyword(s):  
Real Time ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Cloud Manufacturing ◽  
Practical Significance ◽  
Shop Floor ◽  
Practical Case ◽  
Entropy Model ◽  
Time Model ◽  
The Real

In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in the factory, such as the industrial Internet of things (IIoT) and cloud manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with a random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.

scholarly journals Real-time Production and Logistics Self-adaption Scheduling based on Information Entropy Theory

2020 ◽  
Author(s):  
Wenchao Yang ◽  
Wenfeng Li ◽  
Yulian Cao ◽  
Yun Luo ◽  
Lijun He
Keyword(s):  
Real Time ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Cloud Manufacturing ◽  
Practical Significance ◽  
Shop Floor ◽  
Practical Case ◽  
Entropy Model ◽  
Time Model ◽  
The Real

In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in factory, such as the Industrial Internet of Things (IIoT) and Cloud Manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.

Real-time dynamic shop floor scheduling using Evolutionary Algorithms

2002 ◽  
Vol 79 (2) ◽  
pp. 113-120 ◽  
Author(s):  
J. Käschel ◽  
Tobias Teich ◽  
Bernd Zacher
Keyword(s):  
Evolutionary Algorithms ◽  
Real Time ◽  
Shop Floor ◽  
Time Dynamic ◽  
Shop Floor Scheduling

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.

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