Maintenance on Job Shop Industry: Review and Analysis

2016 ◽  
Vol 842 ◽  
pp. 365-372 ◽  
Author(s):  
Herman Budi Harja ◽  
Tri Prakosa ◽  
Yatna Yuwana Martawirya
Keyword(s):  
Manufacturing Systems ◽  
Job Shop ◽  
Flow Shop ◽  
Dynamic Condition ◽  
Maintenance Optimization ◽  
Reliability Growth ◽  
Monitoring Cost ◽  
Reliability Modeling ◽  
Maintenance Schedule ◽  
Reliability Improvement

This paper presents overviews about reliability and maintainability of equipment especially for job-shop manufacturing systems. The job shop industry has the characteristics of a more dynamic production than flow shop industries, where products with a variety of great but small amounts. Its dynamic condition certainly contributes directly to the failure rate and reliability growth of equipment. Therefore, proper maintenance should be done as the reliability improvement. Stages of reliability improvement are reliability modeling, reliability analysis and maintenance optimization. This stage is based on reliability growth of equipment that is indicated the deterioration process of failure components, it can be build from maintenance data history or condition data monitoring.. Cost is often considered in points of a maintenance schedule. This cost was affected by minimizing the negative effects of maintenance and maximizing the benefit of production. The attention at reliability and maintenance optimization is a well researches area until now. This paper presents a brief review of existing reliability and maintenance research. Several reliable methods in this area are discussed and maintenance on job-shop industry as future prospects is investigated. It is shown in this paper that some aspect in the area of maintenance on job-shop industry steel needs to be deeply developed.

And The World Came To See: New Manufacturing System Designs and Industrial Revolutions

2002 ◽  
Author(s):  
J. T. Black ◽  
David S. Cochran
Keyword(s):  
System Design ◽  
Systems Engineering ◽  
Lean Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Job Shop ◽  
Flow Shop ◽  
The World ◽  
Industrial Revolutions ◽  
New System

AND THE WORLD CAME TO SEE. When a new manufacturing system design (MSD) is developed by a company or a group of companies, the rest of the world comes to those factories to learn about the new system. In the last 200 years, three new factory designs have evolved, called the job shop, the flow shop and the lean shop. Each is based on a new system design — a functional design, a product flow design and a linked cell design. New factory designs lead to new industrial leaders and even new industrial revolutions (IR’s). Two appendixes are included: One outlines the implementation strategy for the lean shop and the other is a discussion of lean manufacturing from the viewpoint of K. Hitomi, Japanese professor of manufacturing systems engineering.

scholarly journals Novel Complexity Indicator of Manufacturing Process Chains and Its Relations to Indirect Complexity Indicators

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Vladimir Modrak ◽  
Zuzana Soltysova
Keyword(s):  
Manufacturing Process ◽  
Material Flow ◽  
Manufacturing Systems ◽  
Job Shop ◽  
Flow Shop ◽  
Manufacturing Processes ◽  
Process Chains

Manufacturing systems can be considered as a network of machines/workstations, where parts are produced in flow shop or job shop environment, respectively. Such network of machines/workstations can be depicted as a graph, with machines as nodes and material flow between the nodes as links. The aim of this paper is to use sequences of operations and machine network to measure static complexity of manufacturing processes. In this order existing approaches to measure the static complexity of manufacturing systems are analyzed and subsequently compared. For this purpose, analyzed competitive complexity indicators were tested on two different manufacturing layout examples. A subsequent analysis showed relevant potential of the proposed method.

Production Planning Models using Max-Plus Algebra

2013 ◽  
pp. 227-257 ◽  
Author(s):  
Arun N. Nambiar ◽  
Aleksey Imaev ◽  
Robert P. Judd ◽  
Hector J. Carlo
Keyword(s):  
Manufacturing Systems ◽  
Stochastic Systems ◽  
Performance Metrics ◽  
Job Shop ◽  
Flow Shop ◽  
Linear Equations ◽  
Building Blocks ◽  
Buffer Allocation ◽  
Planning Models ◽  
Machining Operations

The chapter presents a novel building block approach to developing models of manufacturing systems. The approach is based on max-plus algebra. Within this algebra, manufacturing schedules are modeled as a set of coupled linear equations. These equations are solved to find performance metrics such as the make span. The chapter develops a generic modeling block with three inputs and three outputs. It is shown that this structure can model any manufacturing system. It is also shown that the structure is hierarchical, that is, a set of blocks can be reduced to a single block with the same three inputs and three output structure. Basic building blocks, like machining operations, assembly, and buffering are derived. Job shop, flow shop, and cellular system applications are given. Extensions of the theory to buffer allocation and stochastic systems are also outlined. Finally, several numerical examples are given throughout the development of the theory.

DYNAMIC SCHEDULING OF MANUFACTURING SYSTEMS WITH MACHINE LEARNING

2001 ◽  
Vol 12 (06) ◽  
pp. 751-762 ◽  
Author(s):  
PAOLO PRIORE ◽  
DAVID DE LA FUENTE ◽  
ALBERTO GOMEZ ◽  
JAVIER PUENTE
Keyword(s):  
Machine Learning ◽  
Manufacturing Systems ◽  
Job Shop ◽  
Dynamic Scheduling ◽  
Flow Shop ◽  
Dispatching Rules ◽  
Heuristic Rules ◽  
Learning Program ◽  
Training Examples ◽  
Definition Of

A common way of scheduling jobs dynamically in a manufacturing system is by means of dispatching rules. The drawback of this method is that the performance of these rules depends on the state the system is in at each moment, and no one rule exists that overrules the rest in all the possible states that the system may be in. It would therefore be interesting to use the most appropriate rule at each moment. To achieve this goal, a scheduling approach which uses machine learning is presented in this paper. The methodology proposed in this paper may be divided into five basic steps. Firstly, definition of the appropriate control attributes for identifying the relevant manufacturing patterns. In second place, creation of a set of training examples using different values of the control attributes. Subsequently, acquiring of heuristic rules by means of a machine learning program. Then, using of the previously calculated heuristic rules to select the most appropriate dispatching rules, and finally testing of the performance of the approach. The approach that we propose is applied to a flow shop system and to a classic job shop configuration. The results demonstrate that this approach produces an improvement in the performance of the system when compared to the traditional method of using dispatching rules.

scholarly journals A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

2021 ◽  
Vol 11 (16) ◽  
pp. 7366
Author(s):  
Paolo Renna ◽  
Sergio Materi
Keyword(s):  
Energy Efficiency ◽  
Renewable Energy ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Job Shop ◽  
Flow Shop ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Future Directions ◽  
Change Mitigation

Climate change mitigation, the goal of reducing CO2 emissions, more stringent regulations and the increment in energy costs have pushed researchers to study energy efficiency and renewable energy sources. Manufacturing systems are large energy consumers and are thus responsible for huge greenhouse gas emissions; for these reasons, many studies have focused on this topic recently. This review aims to summarize the most important papers on energy efficiency and renewable energy sources in manufacturing systems published in the last fifteen years. The works are grouped together, considering the system typology, i.e., manufacturing system subclasses (single machine, flow shop, job shop, etc.) or the assembly line, the developed energy-saving policies and the implementation of the renewable energy sources in the studied contexts. A description of the main approaches used in the analyzed papers was discussed. The conclusion reports the main findings of the review and suggests future directions for the researchers in the integration of renewable energy in the manufacturing systems consumption models.

DEBUGGING PROCESS-ORIENTED DISCRETE SOFTWARE RELIABILITY MODELING

2009 ◽  
Vol 16 (04) ◽  
pp. 357-370 ◽  
Author(s):  
SHINJI INOUE ◽  
NAOKI IWAMOTO ◽  
SHIGERU YAMADA
Keyword(s):  
Discrete Time ◽  
Software Reliability ◽  
Goodness Of Fit ◽  
Growth Models ◽  
Reliability Growth ◽  
Reliability Modeling ◽  
New Approach ◽  
Time Modeling ◽  
Software Reliability Growth ◽  
Discrete Time Modeling

This paper discusses an new approach for discrete-time software reliability growth modeling based on an discrete-time infinite server queueing model, which describes a debugging process in a testing phase. Our approach enables us to develop discrete-time software reliability growth models (SRGMs) which could not be developed under conventional discrete-time modeling approaches. This paper also discuss goodness-of-fit comparisons of our discrete-time SRGMs with conventional continuous-time SRGMs in terms of the criterion of the mean squared errors, and show numerical examples for software reliability analysis of our models by using actual data.

A Two-Stage Hybrid Algorithm for Flexible Job-Shop Scheduling

2011 ◽  
Vol 268-270 ◽  
pp. 476-481
Author(s):  
Li Gao ◽  
Ke Lin Xu ◽  
Wei Zhu ◽  
Na Na Yang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Job Shop ◽  
Flow Shop ◽  
Job Shop Scheduling ◽  
Selection Procedure ◽  
Dynamic Programming Method ◽  
Two Stage ◽  
Simulated Annealing Genetic Algorithm ◽  
Mutation Operators

A mathematical model was constructed with two objectives. A two-stage hybrid algorithm was developed for solving this problem. At first, the man-hour optimization based on genetic algorithm and dynamic programming method, the model decomposes the flow shop into two layers: sub-layer and patrilineal layer. On the basis of the man-hour optimization，A simulated annealing genetic algorithm was proposed to optimize the sequence of operations. A new selection procedure was proposed and hybrid crossover operators and mutation operators were adopted. A benchmark problem solving result indicates that the proposed algorithm is effective.

DECISION SUPPORT FOR A SUSTAINABLE PRODUCTION IN JOB SHOP MANUFACTURING SYSTEMS

10.6036/9917 ◽  
2021 ◽  
Vol 96 (5) ◽  
pp. 455-459
Author(s):  
MAHDI NADERI ◽  
ANTONIO FERNÁNDEZ ULLOA ◽  
JOSÉ ENRIQUE ARES GÓMEZ ◽  
GUSTAVO PELÁEZ LOURIDO
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Job Shop ◽  
Sustainable Production ◽  
Ease Of Use ◽  
Manufacturing Companies ◽  
Strategic Decisions ◽  
Production Environments

Despite the growing importance that is being given to the concepts of sustainability in many areas, not only in industry but also in the economy and public opinion in general, until now, most research has focused, practically, on the analysis of the concepts, but has not addressed, in a comprehensive way, its impact in decision making probably due to the complex relations of interdependence between its different aspects. In this context, MAPSAM (Methodology for the Assessment of Sustainability in Manufacturing Processes and Systems) was created to help the decision-making process, allowing a conscious and transparent assessment by administrators and managers at the different levels of the structure of companies and organisations. This article explains its development and application in a "job shop" type manufacturing system with an approach that allows the integration of economic, environmental and social criteria. MAPSAM is based on the use of various techniques and tools to quantify the importance of each aspect of sustainability and it has been applied in other production environments, being implemented in different systems, analysing their ease of use and evaluating their behaviour. The objective is to show how it helps to make operational, tactical and strategic decisions in the management on these type of manufacturing companies and, specifically, in this contribution we want to highlight its versatility and applicability, by validating it in a certain type of layout. With this new application, MAPSAM increases its possibilities as an innovative instrument that allows companies to make conscious and sustainable decisions in order to be more efficient, fair, supportive and respectful of the environment. Keywords: Manufacturing System, Simulation, Decision Support, Sustainable Production, Decision-Making

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