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.

scholarly journals Defining accurate delivery dates in make to order job-shops managed by workload control

2020 ◽  
Author(s):  
Davide Mezzogori ◽  
Giovanni Romagnoli ◽  
Francesco Zammori
Keyword(s):  
Job Shop ◽  
Industrial Applications ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Time Variability ◽  
Due Dates ◽  
Job Shops ◽  
Predictive Tool ◽  
Workload Control ◽  
Tardy Jobs

Abstract Workload control (WLC) is a lean oriented system that reduces queues and waiting times, by imposing a cap to the workload released to the shop floor. Unfortunately, WLC performance does not systematically outperform that of push operating systems, with undersaturated utilizations levels and optimized dispatching rules. To address this issue, many scientific works made use of complex job-release mechanisms and sophisticated dispatching rules, but this makes WLC too complicated for industrial applications. So, in this study, we propose a complementary approach. At first, to reduce queuing time variability, we introduce a simple WLC system; next we integrate it with a predictive tool that, based on the system state, can accurately forecast the total time needed to manufacture and deliver a job. Due to the non-linearity among dependent and independent variables, forecasts are made using a multi-layer-perceptron; yet, to have a comparison, the effectiveness of both linear and non-linear multi regression model has been tested too. Anyhow, if due dates are endogenous (i.e. set by the manufacturer), they can be directly bound to this internal estimate. Conversely, if they are exogenous (i.e. set by the customer), this approach may not be enough to minimize the percentage of tardy jobs. So, we also propose a negotiation scheme, which can be used to extend exogenous due dates considered too tight, with respect to the internal estimate. This is the main contribution of the paper, as it makes the forecasting approach truly useful in many industrial applications. To test our approach, we simulated a 6-machines job-shop controlled with WLC and equipped with the proposed forecasting system. Obtained performances, namely WIP levels, percentage of tardy jobs and negotiated due dates, were compared with those of a set classical benchmark, and demonstrated the robustness and the quality of our approach, which ensures minimal delays.

Intelligente Werkstattfertigung*/Intelligent job shop manufacturing - Control mechanisms for job shop manufacturing of series products using the German “Industrie 4.0” approach

2015 ◽  
Vol 105 (04) ◽  
pp. 184-189
Author(s):  
E. Uhlmann ◽  
B. Schallock ◽  
F. Otto
Keyword(s):  
Production Control ◽  
Job Shop ◽  
Production Systems ◽  
Industrie 4.0 ◽  
Innovative Technology ◽  
Shop Floor ◽  
Control Mechanisms ◽  
Utilisation Rate ◽  
Cyber Physical Production Systems ◽  
And Control

Die „intelligente selbstorganisierende Werkstattproduktion“ (iWePro) folgt dem Konzept einer dezentralisierten Produktionssteuerung. Erstmalig wird die Anwendung der Selbstorganisation auf die Serienproduktion von Automobilkomponenten untersucht, die momentan nach Lean-Prinzipien für große Stückzahlen verkettet aufgebaut ist. Zukünftig soll mit dem Werkstattprinzip schwankenden Auslastungen entgegengewirkt werden. Die Fertigungssteuerung für die dadurch wahlfrei zugreifbaren Produktionsmaschinen lässt sich konventionell kaum, wohl aber mit Zukunftskonzepten und Industrie 4.0-Technologien umsetzen.   “Intelligent self-organizing shop floor production” (iWePro) uses the concept of decentralized production control solutions. For the first time, a concept of self-organization is applied to the production of car components, which are currently a moving line according to traditional lean production large batch principles. In the future, the traditional shop floor structure of disconnected machines should guarantee a higher utilisation rate but needs innovative technology and control mechanisms for cyber-physical production systems (CPPS).

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 Automation technology as a key component of the Industry 4.0 production development path

2021 ◽  
Author(s):  
Christian Brecher ◽  
Aleksandra Müller ◽  
Yannick Dassen ◽  
Simon Storms
Keyword(s):  
Industry 4.0 ◽  
Information Technologies ◽  
Production Systems ◽  
Product Variety ◽  
Transformation Process ◽  
Smart Manufacturing ◽  
Production Development ◽  
Flexible Production Systems ◽  
Current Production ◽  
Automation Technology

AbstractSince 2011, the Industry 4.0 initiative is a key research and development direction towards flexible production systems in Germany. The objective of the initiative is to deal with the challenge of an increased production complexity caused by various factors such as increasing global competition between companies, product variety, and individualization to meet customer needs. For this, Industry 4.0 envisions an overarching connection of information technologies with the production process, enabling smart manufacturing. Bringing current production systems to this objective will be a long transformation process, which requires a coherent migration path. The aim of this paper is to represent an exemplary production development way towards Industry 4.0 using eminent formalization approaches and standardized automation technologies.

scholarly journals Context-Based Resilience in Cyber-Physical Production System

2021 ◽  
Author(s):  
Ada Bagozi ◽  
Devis Bianchini ◽  
Valeria De Antonellis
Keyword(s):  
Production Systems ◽  
Complex Structure ◽  
High Volume ◽  
Sensor Data ◽  
Shop Floor ◽  
Human In The Loop ◽  
Operator Interface ◽  
Physical Elements ◽  
Cyber Physical Production Systems ◽  
Smart Machine

AbstractCyber-physical systems are hybrid networked cyber and engineered physical elements that record data (e.g. using sensors), analyse them using connected services, influence physical processes and interact with human actors using multi-channel interfaces. Examples of CPS interacting with humans in industrial production environments are the so-called cyber-physical production systems (CPPS), where operators supervise the industrial machines, according to the human-in-the-loop paradigm. In this scenario, research challenges for implementing CPPS resilience, promptly reacting to faults, concern: (i) the complex structure of CPPS, which cannot be addressed as a monolithic system, but as a dynamic ecosystem of single CPS interacting and influencing each other; (ii) the volume, velocity and variety of data (Big Data) on which resilience is based, which call for novel methods and techniques to ensure recovery procedures; (iii) the involvement of human factors in these systems. In this paper, we address the design of resilient cyber-physical production systems (R-CPPS) in digital factories by facing these challenges. Specifically, each component of the R-CPPS is modelled as a smart machine, that is, a cyber-physical system equipped with a set of recovery services, a Sensor Data API used to collect sensor data acquired from the physical side for monitoring the component behaviour, and an operator interface for displaying detected anomalous conditions and notifying necessary recovery actions to on-field operators. A context-based mediator, at shop floor level, is in charge of ensuring resilience by gathering data from the CPPS, selecting the proper recovery actions and invoking corresponding recovery services on the target CPS. Finally, data summarisation and relevance evaluation techniques are used for supporting the identification of anomalous conditions in the presence of high volume and velocity of data collected through the Sensor Data API. The approach is validated in a food industry real case study.

scholarly journals Simulation von Cyber-Physischen Produktionssystemen in einer Forschungsinfrastruktur

2020 ◽  
Author(s):  
Iris Gräßler
Keyword(s):  
Communication Technology ◽  
Information Acquisition ◽  
Industry 4.0 ◽  
Production Control ◽  
Production Systems ◽  
General Concept ◽  
Radio Network ◽  
Systems Research ◽  
Digital Twins ◽  
Cyber Physical Production Systems

The article describes the setup of an experimentation and validation environment by extending a production laboratory: All relevant elements of the production laboratory were equipped with computer systems, so-called "industry 4.0 boxes", and interconnected via a peer-to-peer radio network. The "industry 4.0 boxes" are used to upgrade dedicated sensors for recording machine behaviour and communication technology to be integrated into decentralized production control. In addition, digital twins were implemented to map machine and user behaviour, enable control and support information acquisition and processing. Thereby, a research infrastructure is created for research on potentials of cyber-physical production systems. Research outcomes will be used as a decision basis for companies and for validation of production optimizations. This paper describes the concept and implementation of industry 4.0 functionalities and derives a general concept of simulation platforms for CPPS.

Integration of Cutting-Edge Interoperability Approaches in Cyber-Physical Production Systems and Industry 4.0

2021 ◽  
pp. 144-172
Author(s):  
Luis Alberto Estrada-Jimenez ◽  
Terrin Pulikottil ◽  
Nguyen Ngoc Hien ◽  
Agajan Torayev ◽  
Hamood Ur Rehman ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Production Systems ◽  
Heterogeneous Systems ◽  
Smart Manufacturing ◽  
Use Case ◽  
Integrative Framework ◽  
Data Formats ◽  
Exchange Information ◽  
Cyber Physical Production Systems ◽  
Physical Components

Interoperability in smart manufacturing refers to how interconnected cyber-physical components exchange information and interact. This is still an exploratory topic, and despite the increasing number of applications, many challenges remain open. This chapter presents an integrative framework to understand common practices, concepts, and technologies used in trending research to achieve interoperability in production systems. The chapter starts with the question of what interoperability is and provides an alternative answer based on influential works in the field, followed by the presentation of important reference models and their relation to smart manufacturing. It continues by discussing different types of interoperability, data formats, and common ontologies necessary for the integration of heterogeneous systems and the contribution of emerging technologies in achieving interoperability. This chapter ends with a discussion of a recent use case and final remarks.

A Data Mining Based Dispatching Rules Selection System for the Job Shop Scheduling Problem

2019 ◽  
Vol 18 (01) ◽  
pp. 35-56
Author(s):  
M. Habib Zahmani ◽  
B. Atmani
Keyword(s):  
Data Mining ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Scheduling Problem ◽  
Dispatching Rule ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Combining Data

Identifying the best Dispatching Rule in order to minimize makespan in a Job Shop Scheduling Problem is a complex task, since no Dispatching Rule is better than all others in different scenarios, making the selection of a most effective rule which is time-consuming and costly. In this paper, a novel approach combining Data Mining, Simulation, and Dispatching Rules is proposed. The aim is to assign in real-time a set of Dispatching Rules to the machines on the shop floor while minimizing makespan. Experiments show that the suggested approach is effective and reduces the makespan within a range of 1–44%. Furthermore, this approach also reduces the required computation time by using Data Mining to determine and assign the best Dispatching Rules to machines.

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