Modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments

Modeling and Statistical Analysis of Complexity in Manufacturing Systems Under Flow Shop and Hybrid Environments

10.21203/rs.3.rs-647913/v1 ◽

2021 ◽

Author(s):

Germán Herrera Vidal ◽

Jairo Rafael Coronado Hernandez ◽

Claudia Minnaard

Keyword(s):

Statistical Analysis ◽

Structural Design ◽

Manufacturing Systems ◽

Flow Shop ◽

Methodological Approach ◽

Production Costs ◽

Assembly Lines ◽

Definition Of ◽

Buffer Capacities ◽

Design Factors

Abstract In manufacturing systems, there are environments where the elaboration of a product requires a series of sequential operations, involving the configuration of machines by stages, intermediate buffer capacities, definition of assembly lines and routing of parts. The objective of this research is to develop a modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments. The methodological approach starts with the structural modeling, then the measurement of the complexity in the systems is developed, the hypotheses are proposed and finally an experimental and factorial statistical analysis is developed. The results obtained corroborate the hypotheses proposed, where statistically the structural design factors and the variation of production time per stage have a significant influence on the response variable associated to the total complexity. Similarly, there is evidence of correlation between the performance indicators and the variable studied, in which the incidence with production costs stands out.

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Robust scheduling in two-stage assembly flow shop problem with random machine breakdowns: integrated meta-heuristic algorithms and simulation approach

Assembly Automation ◽

10.1108/aa-10-2018-0165 ◽

2019 ◽

Vol 39 (5) ◽

pp. 944-962 ◽

Cited By ~ 2

Author(s):

Sahar Tadayonirad ◽

Hany Seidgar ◽

Hamed Fazlollahtabar ◽

Rasoul Shafaei

Keyword(s):

Manufacturing Systems ◽

Heuristic Algorithms ◽

Flow Shop ◽

Imperialist Competitive Algorithm ◽

Flow Shop Scheduling ◽

Process Time ◽

Two Stage ◽

Content Type ◽

Robust Scheduling ◽

Machine Breakdowns

Purpose In real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously. Design/methodology/approach Owing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions. Findings Finally, experimental study and analysis of variance (ANOVA) is done to investigate the effect of different proposed measures on the performance of the obtained results. ANOVA's results indicate the job and weight of makespan factors have a significant impact on the robustness of the proposed meta-heuristics algorithms. Also, it is obvious that the most effective parameter on the robustness for GA and ICA is job. Originality/value Robustness is calculated by the expected value of the relative difference between the deterministic and actual makespan.

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Hybrid GA-based metaheuristics for production planning and scheduling optimization in intelligent flow-shop manufacturing systems

2011 15th IEEE International Conference on Intelligent Engineering Systems ◽

10.1109/ines.2011.5954777 ◽

2011 ◽

Author(s):

Pavol Semanco ◽

Vladimir Modrak

Keyword(s):

Production Planning ◽

Manufacturing Systems ◽

Flow Shop ◽

Scheduling Optimization ◽

Planning And Scheduling ◽

Production Planning And Scheduling

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

Technology and Society and Engineering Business Management ◽

10.1115/imece2002-39344 ◽

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.

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Optimization Techniques for No-Wait Manufacturing Scheduling: A Review

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.488-489.1114 ◽

2012 ◽

Vol 488-489 ◽

pp. 1114-1118 ◽

Cited By ~ 1

Author(s):

Sagar U. Sapkal ◽

Dipak Laha ◽

Dhiren Kumar Behera

Keyword(s):

Manufacturing Systems ◽

Flow Shop ◽

Optimization Methods ◽

Performance Measure ◽

Optimization Techniques ◽

Flow Shop Scheduling ◽

Scheduling Problem ◽

Manufacturing Scheduling ◽

Academic Fields ◽

No Wait

This paper deals with a general continuous or no-wait manufacturing scheduling problem. Due to its applications in advanced manufacturing systems, no-wait scheduling has gained much attention in both practical and academic fields. Due to its NP-hard nature, most of the contributions focus on development of approximation based optimization methods or heuristics for the problem. Several heuristic procedures have been developed to solve this problem. This paper presents a survey of various methodologies developed to solve no-wait flow shop scheduling problem with the objective of minimizing single performance measure

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

Complexity ◽

10.1155/2017/9102824 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 10

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.

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Statistical analysis and design of semiconductor manufacturing systems

Proceedings of ISSM2000. Ninth International Symposium on Semiconductor Manufacturing (IEEE Cat. No.00CH37130) ◽

10.1109/issm.2000.993681 ◽

2002 ◽

Cited By ~ 1

Author(s):

A. Chen ◽

P.-S. Guo ◽

P. Lin

Keyword(s):

Statistical Analysis ◽

Semiconductor Manufacturing ◽

Manufacturing Systems ◽

Analysis And Design ◽

Statistical Analysis And Design

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A Hybrid Ant Colony Optimization Algorithm for Permutation Flow-Shop Scheduling in Manufacturing Systems and Industrial Process

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.345.438 ◽

2013 ◽

Vol 345 ◽

pp. 438-441

Author(s):

Jing Chen ◽

Xiao Xia Zhang ◽

Yun Yong Ma

Keyword(s):

Ant Colony Optimization ◽

Hybrid Algorithm ◽

Manufacturing Systems ◽

Flow Shop ◽

Ant Colony ◽

Flow Shop Scheduling ◽

Neighborhood Search ◽

Shop Scheduling ◽

Permutation Flow Shop Scheduling ◽

Permutation Flow Shop

This paper presents a novel hybrid ant colony optimization approach (ACO&VNS) to solve the permutation flow-shop scheduling problem (PFS) in manufacturing systems and industrial process. The main feature of this hybrid algorithm is to hybridize the solution construction mechanism of the ant colony optimization (ACO) with variable neighborhood search (VNS) which can also be embedded into the ACO algorithm as neighborhood search to improve solutions. Moreover, the hybrid algorithm considers both solution diversification and solution quality. Finally, the experimental results for benchmark PFS instances have shown that the hybrid algorithm is very efficient to solve the permutation flow-shop scheduling in manufacturing engineering compared with the best existing methods in terms of solution quality.

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A Data-Driven Proactive Scheduling Approach for Hybrid Flow Shop Scheduling Problem

Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability ◽

10.1115/msec2021-61672 ◽

2021 ◽

Author(s):

Dong Han ◽

Wangming Li ◽

Xinyu Li ◽

Liang Gao ◽

Yang Li

Keyword(s):

Manufacturing Systems ◽

Dynamic Scheduling ◽

Flow Shop ◽

Intelligent Manufacturing ◽

Data Driven ◽

Hybrid Flow Shop ◽

Shop Scheduling ◽

Dynamic Events ◽

Proactive Scheduling ◽

Hybrid Flow Shop Scheduling

Abstract As we all know, the COVID-19 pandemic brought a great challenge to manufacturing industry, especially for some traditional and unstable manufacturing systems. It reminds us that intelligent manufacturing certainly will play a key role in the future. Dynamic shop scheduling is also an inevitable hot topic in intelligent manufacturing. However, traditional dynamic scheduling is a kind of passive scheduling mode which takes measures to adjust disturbed scheduling processes after the occurrence of dynamic events. It is difficult to ensure the stability of production because of lack of proactivity. To overcome these shortcomings, manufacturing big data and data technologies as the core driving force of intelligent manufacturing will be used to guide production. Thus, a data-driven proactive scheduling approach is proposed to deal with the dynamic events, especially for machine breakdown. In this paper, the overall procedure of the proposed approach is introduced. More specifically, we first use collected manufacturing data to predict the occurrence of machine breakdowns and provide reliable input for dynamic scheduling. Then a proactive scheduling model is constructed for the hybrid flow shop problem, and an intelligent optimization algorithm is used to solve the problem to realize proactive scheduling. Finally, we design comparative experiments with two traditional rescheduling strategies to verify the effectiveness and stability of the proposed approach.

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