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Automated Bottleneck Analysis of Production Systems: Increasing the applicability of simulation-based multi-objective optimization for bottleneck analysis within industry
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and automation engineering)ORCID iD: 0000-0002-9643-6233
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Manufacturing companies constantly need to explore new management strategies and new methods to increase the efficiency of their production systems and retain their competitiveness. It is of paramount importance to develop new bottleneck analysis methods that can identify the factors that impede the overall performance of their productionsystems so that the optimal improvement actions can be performed. Many of the bottleneck-related research methods developed in the last two decades are aimed mainly at detecting bottlenecks. Due to their sole reliance on historical data and lackof any predictive capability, they are less useful for evaluating the effect of bottleneck improvements.

There is an urgent need for an efficient and accurate method of pinpointing bottlenecks, identifying the correct improvement actions and the order in which these should be carried out, and evaluating their effects on the overall system performance. SCORE (simulation-based constraint removal) is a novel method that uses simulation based multi-objective optimization to analyze bottlenecks. By innovatively formulating bottleneck analysis as a multi-objective optimization problem and using simulation to evaluate the effects of various combinations of improvements, all attainable, maximum throughput levels of the production system can be sought through a single optimization run. Additionally, post-optimality frequency analysis of the Pareto-optimal solutions can generate a rank order of the attributes of the resources required to achieve the target throughput levels. However, in its original compilation, SCORE has a very high computational cost, especially when the simulation model is complex with a large number of decision variables. Some tedious manual setup of the simulation based optimization is also needed, which restricts its applicability within industry, despite its huge potential. Furthermore, the accuracy of SCORE in terms of convergence in optimization theory and correctness of identifying the optimal improvement actions has not been evaluated scientifically.

Building on previous SCORE research, the aim of this work is to develop an effective method of automated, accurate bottleneck identification and improvement analysis that can be applied in industry.

The contributions of this thesis work include:

(1) implementation of a versatile representation in terms of multiple-choice set variables and a corresponding constraint repair strategy into evolutionary multi-objective optimization algorithms;

(2) introduction of a novel technique that combines variable screening enabled initializationof population and variable-wise genetic operators to support a more efficient search process;

(3) development of an automated setup for SCORE to avoid the tedious manual creation of optimization variables and objectives;

(4) the use of ranking distance metrics to quantify and visualize the convergence and accuracy of the bottleneck ranking generated by SCORE.

All these contributions have been demonstrated and evaluated through extensive experiments on scalable benchmark simulation models as well as several large-scale simulation models for real-world improvement projects in the automotive industry.

The promising results have proved that, when augmented with the techniques proposed in this thesis, the SCORE method can offer real benefits to manufacturing companies by optimizing their production systems.

Abstract [sv]

Tillverkningsföretag behöver ständigt utforska nya ledningsstrategier och nya metoder för att påskynda effektiviteten i sina produktionssystem och behålla sin konkurrenskraft. Av yttersta vikt är att utveckla nya flaskhalsanalysmetoder som kan identifierade faktorer som hindrar produktiviteten i produktionssystemen så att optimala förbättringsåtgärderna kan utföras. Många av de flaskhalsrelaterade forskningsmetoder som utvecklats under de senaste två decennierna syftar främst till att upptäcka flaskhalsen. På grund av avsaknaden av förebyggande förmåga är de mindre användbara för att utvärdera effekten av flaskhalsförbättringar.

En effektiv och korrekt metod för identifiering av korrekta förbättringsåtgärder, ordningen de ska utföras i samt dess effekt på produktionssystemets produktivitet är nödvändig. SCORE (simulation-based constraint removal) är en ny metod som möjliggör flaskhalsanalys genom användning av simuleringsbaserad flermålsoptimering. Genom att innovativt formulera flaskhalsanalys till ett flermålsoptimeringsproblem ochanvända simulering för att utvärdera effekterna av olika kombinationer av förbättringar, kan alla uppnåeliga maximala produktivitetsnivåer av produktionssystemet sökas i en enda optimering. Dessutom kan en frekvensanalys på Pareto-optimala lösningar från en sådan optimering generera en rangordning av de systemparameterar som behöver förbättras för att uppnå den önskade produktivitetsnivån. Dessa fördelar med SCORE kan dock endast uppnås med en mycket hög beräkningskostnad, speciellt när simuleringsmodellen är komplex och/eller består av ett stort antal beslutsvariabler. Dessutom innebär formuleringen av det simuleringsbaserade flermålsoptimeringsproblemet mycket manuellt och felbenäget arbete som kan begränsa användbarheten inom industrin, detta trots den enorma potential som metoden erbjuder. Dessutom har noggrannheten i SCORE, när det gäller konvergens i optimeringsteori och korrekthet att identifiera optimala förbättringsåtgärder, inte utvärderats vetenskapligt.

Syftet med denna avhandling är därför att med avstamp i tidigare forskning kring SCORE utveckla en effektiv, automatiserad och korrekt metod för flaskhalsidentifiering och förbättringsanalys som kan tillämpas inom industrin.

Bidrag från detta avhandlingsarbete inkluderar:

(1) implementering av en mångsidig optimeringsvariabel (multiple-choice set variabel) och därtill en reparationsstrategi i evolutionära flermålsoptimeringsalgoritmer(EA);

(2) introducera en ny teknik som baserat på information från en sekventiell screening initialiserar första populationen i en EA samt möjliggör skapandet av variabelvisa genetiska operatorer, båda med syftet att stödja en effektivare sökprocess;

(3) en automatiserad formulering av flermålsoptimeringsproblemet i SCORE för att bespara användarna den stora mängd manuellt och felbenäget arbete med optimeringsvariabler och mål som krävs;

(4) presentera hur upprepad användning av rankningsavstånd (mätetal som visar hur lika/olika två rankningar är varandra) kan användas för att kvantifiera och visualisera konvergens och korrekthet av flaskhalsrankningen genererad av SCORE.

Alla dessa bidrag har demonstrerats och utvärderats genom omfattande experiment på skalbara, benchmark-simuleringsmodeller samt på flera stora simuleringsmodeller som använts i förbättringsprojekt inom fordonsindustrin.

De framgångsrika resultaten har visat att förbättringarna av SCORE-metoden presenterade i detta arbete gör det möjligt för tillverkningsföretag att förvärva verkliga fördelar genom att optimera sina produktionssystem optimalt.

Place, publisher, year, edition, pages
Skövde: University of Skövde , 2018. , p. 218
Series
Dissertation Series ; 23(2018)
Keywords [en]
bottleneck analysis, bottleneck identification, bottleneck improvement, multi-objective optimization, simulation
National Category
Production Engineering, Human Work Science and Ergonomics Information Systems
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-15214ISBN: 978-91-984187-6-7 (print)OAI: oai:DiVA.org:his-15214DiVA, id: diva2:1211903
Public defence
2018-06-08, Portalen, Insikten, Skövde, 13:15 (English)
Opponent
Supervisors
Funder
Knowledge FoundationAvailable from: 2018-06-04 Created: 2018-05-31 Last updated: 2018-06-04Bibliographically approved
List of papers
1. Practical Production Systems Optimization Using Multiple-Choice Sets and Manhattan Distance based Constraints Handling
Open this publication in new window or tab >>Practical Production Systems Optimization Using Multiple-Choice Sets and Manhattan Distance based Constraints Handling
2014 (English)In: Industrial Simulation Conference, Skövde, June 11-13, 2014, 2014, p. 97-103Conference paper, Published paper (Refereed)
Abstract [en]

Many simulation-based optimization packages provide powerful algorithms to solve large-scale system problems. But most of them fall short to offer their users the techniques to effectively handle decision variables that are of multiple-choice type, as well as equality constraints, which can be found in many real-world industrial system design and improvement problems. Hence, this paper introduces how multiple choice sets and Manhattan-distance-based constraint handling can be effectively embedded into a meta-heuristic algorithm for simulation-based optimization. How these two techniques have been applied together to make the improvement of a complex production system, provided by an automotive manufacturer, possible will also be presented.

National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-9387 (URN)2-s2.0-84922203716 (Scopus ID)978-90-77381-83-0 (ISBN)
Conference
ISC'2014, 12th Annual Industrial Simulation Conference, June 11-13, 2014, University of Skövde, Skövde, Sweden
Available from: 2014-06-09 Created: 2014-06-09 Last updated: 2018-05-31Bibliographically approved
2. What Does Multi-Objective Optimization Have to Do with Bottleneck Improvement of Production Systems?
Open this publication in new window or tab >>What Does Multi-Objective Optimization Have to Do with Bottleneck Improvement of Production Systems?
2014 (English)In: Proceedings of The 6th International Swedish Production Symposium 2014 / [ed] Johan Stahre, Björn Johansson & Mats Björkman, 2014Conference paper, Published paper (Refereed)
Abstract [en]

Bottleneck is a common term used to describe the process/operation/person that constrains the performance of the whole system. Since Goldratt introduced his theory of constraint, not many will argue about the importance of identifying and then improving the bottleneck, in order to improve the performance of the entire system. Nevertheless, there exist various definitions of bottleneck, which make bottleneck identification and improvement not a straightforward task in practice. The theory introduced by Production Systems Engineering (PSE) that the bottleneck of a production line is where the infinitesimal improvement can lead to the largest improvement of the average throughput, has provided an inspirational and rigorous way to understand the nature of bottleneck. This is because it conceptually puts bottleneck identification and improvement into a single task. Nevertheless, it is said that a procedure to evaluate how the efficiency increase of each machine would affect the total performance of a line is hardly possible in most practical situations. But is this true?In this paper, we argue how multi-objective optimization fits nicely into the theory introduced by PSE and hence how it can be developed into a practical bottleneck improvement methodology. Numerical results from a real-world application study on a highly complex machining line are provided to justify the practical applicability of this new methodology.

Keywords
Bottleneck Improvement, Production System Simulation, Multi-objective Optimization, Data Mining
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-10359 (URN)978-91-980974-1-2 (ISBN)
Conference
The 6th International Swedish Production Symposium 2014, Gothenburg, September 16 – September 18
Funder
Knowledge Foundation
Available from: 2014-12-08 Created: 2014-12-08 Last updated: 2018-05-31Bibliographically approved
3. Simulation-based multi-objective bottleneck improvement: Towards an automated toolset for industry
Open this publication in new window or tab >>Simulation-based multi-objective bottleneck improvement: Towards an automated toolset for industry
2015 (English)In: Proceedings of the 2015 Winter Simulation Conference / [ed] L. Yilmaz, W. K. V. Chan, I. Moon, T. M. K. Roeder, C. Macal, and M. D. Rossetti, Press Piscataway, NJ: IEEE Press, 2015, p. 2183-2194Conference paper, Published paper (Refereed)
Abstract [en]

Manufacturing companies of today are under pressure to run their production most efficiently in order to sustain their competitiveness. Manufacturing systems usually have bottlenecks that impede their performance, and finding the causes of these constraints, or even identifying their locations, is not a straightforward task. SCORE (Simulation-based COnstraint REmoval) is a promising method for detecting and ranking bottlenecks of production systems, that utilizes simulation-based multi-objective optimization (SMO). However, formulating a real-world, large-scale industrial bottleneck analysis problem into a SMO problem using the SCORE-method manually include tedious and error-prone tasks that may prohibit manufacturing companies to benefit from it. This paper presents how the greater part of the manual tasks can be automated by introducing a new, generic way of defining improvements of production systems and illustrates how the simplified application of SCORE can assist manufacturing companies in identifying their production constraints.

Place, publisher, year, edition, pages
Press Piscataway, NJ: IEEE Press, 2015
Series
Winter Simulation Conference. Proceedings, ISSN 0891-7736
Keywords
Simulation, Optimization, Manufacturing
National Category
Other Mechanical Engineering
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-11919 (URN)10.1109/WSC.2015.7408331 (DOI)000399133902006 ()2-s2.0-84962811728 (Scopus ID)978-1-4673-9743-8 (ISBN)
Conference
WSC '15 Winter Simulation Conference, Huntington Beach, CA, USA — December 06 - 09, 2015
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2018-05-31
4. Automatic identification of constraints and improvement actions in production systems using multi-objective optimization and post-optimality analysis
Open this publication in new window or tab >>Automatic identification of constraints and improvement actions in production systems using multi-objective optimization and post-optimality analysis
2016 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 39, p. 24-37Article in journal (Refereed) Published
Abstract [en]

Manufacturing companies are operating in a severely competitive global market, which renders an urgent need for them to explore new methods to enhance the performance of their production systems in order to retain their competitiveness. Regarding the performance of a production system, it is not sufficient simply to detect which operations to improve, but it is imperative to pinpoint the right actions in the right order to avoid sub-optimizations and wastes in time and expense. Therefore, a more accurate and efficient method for supporting system improvement decisions is greatly needed in manufacturing systems management. Based on research in combining simulation-based multi-objective optimization and post-optimality analysis methods for production systems design and analysis, a novel method for the automatic identification of bottlenecks and improvement actions, so-called Simulation-based Constraint Identification (SCI), is proposed in this paper. The essence of the SCI method is the application of simulation-based multi-objective optimization with the conflicting objectives to maximize the throughput and minimize the number of required improvement actions simultaneously. By using post-optimality analysis to process the generated optimization dataset, the exact improvement actions needed to attain a certain level of performance of the production line are automatically put into a rank order. In other words, when compared to other existing approaches in bottleneck detection, the key novelty of combining multi-objective optimization and post-optimality analysis is to make SCI capable of accurately identifying a rank order for the required levels of improvement for a large number of system parameters which impede the performance of the entire system, in a single optimization run. At the same time, since SCI is basically built a top a simulation-based optimization approach, it is capable of handling large-scale, real-world system models with complicated process characteristics. Apart from introducing such a method, this paper provides some detailed validation results from applying SCI both in hypothetical examples that can easily be replicated as well as a complex, real-world industrial improvement project. The promising results compared to other existing bottleneck detection methods have demonstrated that SCI can provide valuable higher-level information to support confident decision-making in production systems improvement.

Place, publisher, year, edition, pages
Elsevier, 2016
Keywords
Multi-objective optimization, Simulation, Production system, SCI
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology; Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-12020 (URN)10.1016/j.jmsy.2016.02.001 (DOI)000376694200003 ()2-s2.0-84959481904 (Scopus ID)
Available from: 2016-03-08 Created: 2016-03-08 Last updated: 2018-05-31Bibliographically approved
5. Multiple Choice Sets and Manhattan Distance Based Equality Constraint Handling for Production Systems Optimization
Open this publication in new window or tab >>Multiple Choice Sets and Manhattan Distance Based Equality Constraint Handling for Production Systems Optimization
(English)In: Computers & Operations Research, ISSN 0305-0548, E-ISSN 1873-765XArticle in journal (Refereed) Submitted
Abstract [en]

Many simulation-based optimization packages provide powerful algorithms to solve industrialproblems. But most of them fail to oer their users the techniques they needto eectively handle multiple-choice problems involving a large set of decision variableswith mixed types (continuous, discrete and combinatorial) and problems that are highlyconstrained (e.g., with many equality constraints). Yet such issues are found in manyreal-world production system design and improvement problems. Thus, this paper introducesa method to eectively embed multiple choice sets and Manhattan-distancebasedconstraint handling into multi-objective optimization algorithms like NSGA-II andNSGA-III. This paper illustrates and evaluates how these two techniques have been appliedtogether to solve optimal workload, buer and workforce allocation problems. Anexample follows, showing their application to a complex production system improvementproblem at an automotive manufacturer.

Keywords
Production systems, simulation-based optimization, multiple choice set, constraint handling
National Category
Production Engineering, Human Work Science and Ergonomics Information Systems
Research subject
Production and Automation Engineering; Information Systems
Identifiers
urn:nbn:se:his:diva-15192 (URN)
Funder
Knowledge Foundation
Available from: 2018-05-30 Created: 2018-05-30 Last updated: 2018-07-02
6. On the convergence of stochastic simulation-based multi-objective optimization for bottleneck identification
Open this publication in new window or tab >>On the convergence of stochastic simulation-based multi-objective optimization for bottleneck identification
(English)In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588XArticle in journal (Refereed) Submitted
Abstract [en]

By innovatively formulating a bottleneck identication problem into a bi-objective optimization,simulation-based multi-objective optimization (SMO) can be eectively used as a new method for gen-eral production systems improvement. In a single optimization run, all attainable, maximum throughputlevels of the system can be sought through various optimal combinations of improvement changes ofthe resources. Additionally, the post-optimality frequency analysis on the Pareto-optimal solutions cangenerate a rank order of the attributes of the resources required to achieve the target throughput levels.Observing that existing research mainly put emphasis on measuring the convergence of the optimizationin the objective space, leaving no information on when the solutions in the decision space have convergedand stabilized, this paper represents the rst eort in increasing the knowledge about the convergence ofSMO for the rank ordering in the context of bottleneck analysis. By customizing the Spearman's footruledistance and Kendall's tau, this paper presents how these metrics can be used eectively to provide thedesired visual aid in determining the convergence of bottleneck ranking, hence can assist the user todetermine correctly the terminating condition of the optimization process. It illustrates and evaluatesthe convergence of the SMO for bottleneck analysis on a set of scalable benchmark models as well as twoindustrial simulation models. The results have shed promising direction of applying these new metrics tocomplex, real-world applications.

Keywords
bottleneck detection; multi-objective optimization; simulation; convergence; generalized ranking distance
National Category
Production Engineering, Human Work Science and Ergonomics Information Systems
Research subject
Production and Automation Engineering; Information Systems
Identifiers
urn:nbn:se:his:diva-15211 (URN)
Funder
Knowledge Foundation
Available from: 2018-05-31 Created: 2018-05-31 Last updated: 2018-07-02
7. Variables Screening Enabled Multi-Objective Optimization for Bottleneck Analysis of Production Systems
Open this publication in new window or tab >>Variables Screening Enabled Multi-Objective Optimization for Bottleneck Analysis of Production Systems
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Bottleneck analysis can be defined as the process that includes both bottleneck identification and improvement. In the literature most of the proposed bottleneck-related methods address mainly bottleneck detection. By innovatively formulating a bottleneck analysis into a bi-objective optimization method, recent research has shown that all attainable, maximized TH of a production system, through various combinations of improvement changes of the resources, can be sought in a single optimization run. Nevertheless, when applied to simulation-based evaluation, such a bi-objective optimization is computationally expensive especially when the simulation model is complex and/or with a large amount of decision variables representing the improvement actions. The aim of this paper is therefore to introduce a novel variables screening enabled bi-objective optimization that is customized for bottleneck analysis of production systems. By using the Sequential Bifurcation screening technique which is particularly suitable for large-scale simulation models, fewer simulation runs are required to find the most influenacing factors in a simulation model. With the knowledge of these input variables, the bi-objective optimization used in the bottleneck analysis can customize the genetic operators on these variables individually according to their rank of main effects with the target to speed up the entire optimization process. The screening-enabled algorithm is then applied to a set of experiments designed to evaluate how well it performs when the number of variables increases is a scalable, benchmark model, as well as two real-world industrial-scale simulation models found in the automotive industry. The results have illustrated the promising direction of incorporating the knowledge of influencing variables and variable-wise genetic operators into a multi-objective optimization algorithm for bottleneck analysis.

Keywords
Bottleneck Analysis, Multi-Objective Optimization, Sequential Bifurcation
National Category
Production Engineering, Human Work Science and Ergonomics Information Systems
Research subject
Production and Automation Engineering; Information Systems
Identifiers
urn:nbn:se:his:diva-15212 (URN)
Funder
Knowledge Foundation
Available from: 2018-05-31 Created: 2018-05-31 Last updated: 2018-05-31

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