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What Does Multi-Objective Optimization Have to Do with Bottleneck Improvement of Production Systems?
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Volvo Car Corporation, Sweden. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0002-9643-6233
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Volvo Car Corporation, Sweden. (Produktion och Automatiseringsteknik, Production and Automation Engineering)
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.

Place, publisher, year, edition, pages
2014.
Keywords [en]
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: urn:nbn:se:his:diva-10359ISBN: 978-91-980974-1-2 (print)OAI: oai:DiVA.org:his-10359DiVA, id: diva2:769672
Conference
The 6th International Swedish Production Symposium 2014, Gothenburg, September 16 – September 18
Funder
Knowledge FoundationAvailable from: 2014-12-08 Created: 2014-12-08 Last updated: 2018-05-31Bibliographically approved
In thesis
1. Automated Bottleneck Analysis of Production Systems: Increasing the applicability of simulation-based multi-objective optimization for bottleneck analysis within industry
Open this publication in new window or tab >>Automated Bottleneck Analysis of Production Systems: Increasing the applicability of simulation-based multi-objective optimization for bottleneck analysis within industry
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
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:nbn:se:his:diva-15214 (URN)978-91-984187-6-7 (ISBN)
Public defence
2018-06-08, Portalen, Insikten, Skövde, 13:15 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Available from: 2018-06-04 Created: 2018-05-31 Last updated: 2019-07-03Bibliographically approved

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