scholarly journals Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4948 ◽  
Author(s):  
Moussa Abderrahim ◽  
Abdelghani Bekrar ◽  
Damien Trentesaux ◽  
Nassima Aissani ◽  
Karim Bouamrane
Keyword(s):  
Industry 4.0 ◽  
Job Shop ◽  
Experimental Tests ◽  
Automated Guided Vehicles ◽  
Lead Times ◽  
Battery Management ◽  
Production Lines ◽  
Operations Scheduling ◽  
Proposed Model ◽  
Time Minimization

The industry 4.0 concepts are moving towards flexible and energy efficient factories. Major flexible production lines use battery-based automated guided vehicles (AGVs) to optimize their handling processes. However, optimal AGV battery management can significantly shorten lead times. In this paper, we address the scheduling problem in an AGV-based job-shop manufacturing facility. The considered schedule concerns three strands: jobs affecting machines, product transport tasks’ allocations and AGV fleet battery management. The proposed model supports outcomes expected from Industry 4.0 by increasing productivity through completion time minimization and optimizing energy by managing battery replenishment. Experimental tests were conducted on extended benchmark literature instances to evaluate the efficiency of the proposed approach.

scholarly journals A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0

2021 ◽  
Vol 13 (14) ◽  
pp. 7684
Author(s):  
Raja Awais Liaqait ◽  
Shermeen Hamid ◽  
Salman Sagheer Warsi ◽  
Azfar Khalid
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Intelligent Manufacturing ◽  
Distributed Scheduling ◽  
Lead Times ◽  
Shop Scheduling ◽  
Third Phase ◽  
Customized Production

Scheduling plays a pivotal role in the competitiveness of a job shop facility. The traditional job shop scheduling problem (JSSP) is centralized or semi-distributed. With the advent of Industry 4.0, there has been a paradigm shift in the manufacturing industry from traditional scheduling to smart distributed scheduling (SDS). The implementation of Industry 4.0 results in increased flexibility, high product quality, short lead times, and customized production. Smart/intelligent manufacturing is an integral part of Industry 4.0. The intelligent manufacturing approach converts renewable and nonrenewable resources into intelligent objects capable of sensing, working, and acting in a smart environment to achieve effective scheduling. This paper aims to provide a comprehensive review of centralized and decentralized/distributed JSSP techniques in the context of the Industry 4.0 environment. Firstly, centralized JSSP models and problem-solving methods along with their advantages and limitations are discussed. Secondly, an overview of associated techniques used in the Industry 4.0 environment is presented. The third phase of this paper discusses the transition from traditional job shop scheduling to decentralized JSSP with the aid of the latest research trends in this domain. Finally, this paper highlights futuristic approaches in the JSSP research and application in light of the robustness of JSSP and the current pandemic situation.

scholarly journals The vulnerability of securing IoT production lines and their network components in the Industry 4.0 concept

2020 ◽  
Vol 53 (2) ◽  
pp. 11237-11242
Author(s):  
Tibor Horak ◽  
Zuzana Cervenanska ◽  
Ladislav Huraj ◽  
Pavel Vazan ◽  
Jan Janosik ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Production Lines

scholarly journals A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting

2021 ◽  
Vol 9 (4) ◽  
pp. 383
Author(s):  
Ting Yu ◽  
Jichao Wang
Keyword(s):  
Lead Time ◽  
Wave Period ◽  
Data Driven ◽  
Lead Times ◽  
Proposed Model ◽  
Data Driven Approach ◽  
Mean Wave Period ◽  
Scattering Index ◽  
Gated Recurrent Unit ◽  
Methods Numerical

Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time.

scholarly journals IMPROVING THE RELIABILITY OF BODY ASSEMBLY LINE EQUIPMENT

1996 ◽  
Vol 03 (01) ◽  
pp. 11-24 ◽  
Author(s):  
KAKURO AMASAKA ◽  
HIROHISA SAKAI
Keyword(s):  
Conventional Method ◽  
Assembly Line ◽  
Failure Modes ◽  
Lead Times ◽  
Reliability Engineering ◽  
Production Lines ◽  
Line Production ◽  
Equipment Reliability ◽  
New Models ◽  
Short Time

It is necessary to establish higher levels of equipment reliability in a short time, the market demands ever shorter lead times for the release of new models. Also, the demand for new-model cars is very strong immediately after their introduction. The conventional method for enhancing equipment reliability is by screening alone. However, this requires screening operations on production lines and so has been an obstacle to line production and prevented shortening of lead times. We are now able to dramatically enhance equipment reliability in a very short time by detecting failure modes and forecasting the number of occurrences using a scientific technique based on reliability engineering.

One-of-a-kind Production in Cyber-Physical Production Systems Considering Machine Failures

2021 ◽  
pp. 1-20
Author(s):  
Guido Vinci Carlavan ◽  
Daniel Alejandro Rossit
Keyword(s):  
Industry 4.0 ◽  
Information Technologies ◽  
Job Shop ◽  
Production Systems ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Due Dates ◽  
Efficient Manner ◽  
Large Variability ◽  
Cyber Physical Production Systems

Industry 4.0 proposes the incorporation of information technologies at all levels of the production process. By incorporating these technologies, Industry 4.0 provides new tools for production planning processes, allowing to address problems in an innovative and efficient manner. From these technologies and tools, it is that in this work a One-of-a-Kind Production (OKP) process is approached, where the products tend to be highly customized. OKP implies working with a very large variability within production, demanding very efficient planning systems. For this, a planning model based on CONWIP-type strategies was proposed, which seeks to level the production of a shop floor configured in the form of a job shop. Even more, for having a more realistic shop-floor representation, machine failures have been included in the model. In turn, different dispatching rules were proposed to study the performance and analyze the behaviour of the system. From the results obtained, it is observed that, when the production demand is very exigent in relation with the capacity of the system, the dispatching rules that analyze the workload generated by each job tend to perform better. However, when the demand on the capacity of the production system is less intense, the rules associated with due dates are the ones that obtain the best results.

An E2GPGP-GASA-Based Multi-Agent Job Shop Scheduling System

2012 ◽  
Vol 505 ◽  
pp. 65-74
Author(s):  
Lin Lin Lu ◽  
Xin Ma ◽  
Ya Xuan Wang
Keyword(s):  
Large Scale ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Simulation Software ◽  
Prototype System ◽  
Shop Scheduling ◽  
Model Combining ◽  
Proposed Model ◽  
Multi Agent ◽  
Global Planning

In this paper, a job shop scheduling model combining MAS (Multi-Agent System) with GASA (Simulated Annealing-Genetic Algorithm) is presented. The proposed model is based on the E2GPGP (extended extended generalized partial global planning) mechanism and utilizes the advantages of static intelligence algorithms with dynamic MAS. A scheduling process from ‘initialized macro-scheduling’ to ‘repeated micro-scheduling’ is designed for large-scale complex problems to enable to implement an effective and widely applicable prototype system for the job shop scheduling problem (JSSP). Under a set of theoretic strategies in the GPGP which is summarized in detail, E2GPGP is also proposed further. The GPGP-cooperation-mechanism is simulated by using simulation software DECAF for the JSSP. The results show that the proposed model based on the E2GPGP-GASA not only improves the effectiveness, but also reduces the resource cost.

scholarly journals Analysis of the job shop system with transport and setup times in deadlock-free operating conditions

2012 ◽  
Vol 22 (4) ◽  
pp. 417-425 ◽  
Author(s):  
Jolanta Krystek ◽  
Marek Kozik
Keyword(s):  
Comparative Research ◽  
Completion Time ◽  
Job Shop ◽  
Optimization Criterion ◽  
Operating Conditions ◽  
Setup Times ◽  
Automated Guided Vehicles ◽  
Transport Time ◽  
Priority Algorithms

This paper presents a generalized job-shop problem taking into consideration transport time between workstations and setups machines in deadlock-free operating conditions. The automated transportation system, employing a number of automated guided vehicles is considered. The completion time of all jobs was applied as the optimization criterion. The created computational application was used to solve this problem in which chosen priority algorithms (FIFO, LIFO, LPT, SPT, EDD and LWR) were implemented. Various criteria were used to assess the quality of created schedules. Numerical results of the comparative research were presented for various criteria and rules of the priority

Industrial Maintenance Entering the Industry 4.0 Era

2021 ◽  
pp. 229-250
Author(s):  
Pedro Fernandes Anunciação ◽  
Vitor Manuel Lemos Dinis ◽  
Francisco Madeira Esteves
Keyword(s):  
Asset Management ◽  
Industry 4.0 ◽  
Information Technologies ◽  
Industrial Revolution ◽  
Response Times ◽  
Production Lines ◽  
Fourth Industrial Revolution ◽  
Industrial Companies ◽  
Management Condition ◽  
Industrial Maintenance

Industry 4.0 marks the beginning of the so-called fourth industrial revolution. The new emerging information technologies, such as internet of things, cloud computing, machine learning, artificial intelligence, among others, have challenged the management and organization of industrial companies. They have now shorter market response times, higher quality requirements, and customization needs, which challenges many industrial areas from production to maintenance, from design to asset management. The maintenance and asset management condition and the reliability of production lines are closely linked and constitute key areas of good industrial operation. This work seeks to present a roadmap proposal for the management of industrial assets from maintenance management. In addition, it seeks to identify the key elements for a roadmap design and proposes a set of management questions to assess maintenance maturity.

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