Practical considerations in the optimization of flow production systems

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<ul><li><p>This article was downloaded by: [University of Illinois Chicago]On: 08 December 2014, At: 14:34Publisher: Taylor &amp; FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK</p><p>International Journal of ProductionResearchPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/tprs20</p><p>Practical considerations in theoptimization of flow productionsystemsHorst Tempelmeier aa Universitt zu Kln, Seminar fr produktionswirtschaft ,Albertus-Magnus-Platz, K ln, D-50923, Germany E-mail:Published online: 14 Nov 2010.</p><p>To cite this article: Horst Tempelmeier (2003) Practical considerations in the optimization offlow production systems, International Journal of Production Research, 41:1, 149-170, DOI:10.1080/00207540210161641</p><p>To link to this article: http://dx.doi.org/10.1080/00207540210161641</p><p>PLEASE SCROLL DOWN FOR ARTICLE</p><p>Taylor &amp; Francis makes every effort to ensure the accuracy of all the information(the Content) contained in the publications on our platform. However, Taylor&amp; Francis, our agents, and our licensors make no representations or warrantieswhatsoever as to the accuracy, completeness, or suitability for any purpose of theContent. Any opinions and views expressed in this publication are the opinions andviews of the authors, and are not the views of or endorsed by Taylor &amp; Francis. Theaccuracy of the Content should not be relied upon and should be independentlyverified with primary sources of information. Taylor and Francis shall not be liablefor any losses, actions, claims, proceedings, demands, costs, expenses, damages,and other liabilities whatsoever or howsoever caused arising directly or indirectly inconnection with, in relation to or arising out of the use of the Content.</p><p>This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden.Terms &amp; Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions</p><p>http://www.tandfonline.com/loi/tprs20http://www.tandfonline.com/action/showCitFormats?doi=10.1080/00207540210161641http://dx.doi.org/10.1080/00207540210161641http://www.tandfonline.com/page/terms-and-conditionshttp://www.tandfonline.com/page/terms-and-conditions</p></li><li><p>int. j. prod. res., 2003, vol. 41, no. 1, 149170</p><p>Practical considerations in the optimization of flow production systems</p><p>HORST TEMPELMEIERy</p><p>In this paper we consider the problems faced by an industrial planner who isresponsible for the design of real-life asynchronous production lines under sto-chastic conditions that may be due to breakdowns or random processing times.Based on real-life system data, it is shown that a number of available algorithmsfor the performance evaluation of a given system configuration as well as analgorithm for determining the optimum buffer configuration can be successfullyapplied in industrial practice.</p><p>1. IntroductionOver the last ten years, much effort has been devoted to the development of</p><p>approaches for the performance analysis and optimization of stochastic flow pro-duction systems with finite buffers between the processing stations. The current bodyof knowledge is presented in the papers of Dallery and Gershwin (1992),Papadopoulos and Heavey (1996) and Gershwin (2000) as well as in several mono-graphs on stochastic models of manufacturing systems, such as Visvanadham andNarahari (1992), Buzacott and Shanthikumar (1993), Papadopoulos et al. (1993),Askin and Standridge (1993), Gershwin (1994), and Altiok (1997).</p><p>In spite of the significant benefits resulting from the application of analyticalplanning methods (an award-winning practical case study performed at Hewlett-Packard is described by Burman et al. 1998), many industrial planners seem to berather reluctant to apply the available methods. As far as we know, only a very smallnumber of companies use analytical flow line models. In many cases, planners haveonly limited knowledge about the existence of practically applicable evaluationmethods.</p><p>If quantitative performance evaluation is carried out at all, then in almost anycase simulation is the only tool used. Optimization problems, such as buffer optimi-zation, are mainly solved through simple trial-and-error approaches, which sufferfrom the severe drawbacks of being both very time-consuming and providing solu-tions that are usually far from optimal. According to an empiricial study conductedin 43 German companies in 1989, analytical planning tools were not applied at all(Schoniger and Spingler 1989). This empirical evidence has not changed much.</p><p>Several researchers provide rules of thumb and general insights acquired throughthe in-depth study of different configurations of several hypothetical flow productionsystems. See Conway et al. (1988), Blumenfeld (1990), Baker (1993), Hillier et al.(1993), Baker et al. (1994), Powell and Pyke (1996), Hillier and So (1996), Liu andLin (1996), So (1997), Powell and Pyke (1998), Hillier (2000), and Enginarlar et al.</p><p>SECOND PROOFS C.K.M. i:/T&amp;F UK/Tprs/Tprs41-1/Prs-2135.3d Int. Journal of Production Research (PRS) Paper 102135 Keyword</p><p>International Journal of Production Research ISSN 00207543 print/ISSN 1366588X online # 2003 Taylor &amp; Francis Ltd</p><p>http://www.tandf.co.uk/journals</p><p>DOI: 10.1080/00207540210161641</p><p>Revision received May 2002{Universitat zu Koln, Seminar fur produktionswirtschaft, Albertus-Magnus-Platz,</p><p>D-50923 Koln, Germany. e-mail: tempelmeier@wiso.uni-koeln.de</p><p>Dow</p><p>nloa</p><p>ded </p><p>by [</p><p>Uni</p><p>vers</p><p>ity o</p><p>f Il</p><p>linoi</p><p>s C</p><p>hica</p><p>go] </p><p>at 1</p><p>4:34</p><p> 08 </p><p>Dec</p><p>embe</p><p>r 20</p><p>14 </p></li><li><p>(2002). Industrial planners often believe that an optimal flow line design can bedeveloped based on the experience they have acquired with similar past projects.</p><p>Unfortunately, in the problem domain considered, the value of experience israther limited. The reason is that even a small change of data or system character-istics may generate a considerably different behaviour of the system under study. Forexample, changing the failure characteristics of a station through the introduction ofa machine based on a different technology or slightly changing the processing time ata station may shift the bottleneck of the system with the need to rearrange the bufferscompletely. As every production line is obviously unique, it jeopardizes the economicefficiency if a flow line planner relies completely upon experience gathered fromobservations of other production lines. Therefore, tools are required that can pro-vide system-specific performance measures in a fast and reliable manner.</p><p>This paper is organized as follows. In section 2 we discuss several characteristicsof real-life production systems that can be covered by analytical planning methods.It is shown, that a planner must be provided with basically three algorithms in orderto find good performance approximations in a wide variety of practical situations.The quality of the algorithms is proved with the help of simulation on the basis ofhypothetical as well as real-life data. In section 3, several types of optimizationproblems that emerge in industrial planning practice are discussed. Section 4 con-tains our conclusion and discusses several issues regarding the practical applicationof the analytical methods described.</p><p>2. Performance analysis of industrial flow production systemsIndustrial factory planners who are responsible for building up sufficient produc-</p><p>tion capacity in an economical way are confronted with a number of design factorsthat have an effect on the throughput of a planned production system subject tostochastic influences (breakdowns, random processing times). In addition to techni-cal considerations, such as the definition of the processes and the specification of therequired production and material handling resources, there are several organiza-tional issues that have to be decided upon. In the following, we deal with the ques-tion of how the negative consequences caused by stochastic phenomena such asbreakdowns or the variability of the processing times can be predicted and howthe resulting loss of throughput can be regained through the introduction ofbuffer spaces between the stations.</p><p>Many flow production systems comprise special-purpose machines that repeti-tively perform a certain number of tasks on a single product type. In this case, theprocessing times are deterministic. Randomness arises from breakdown and repairprocesses. In the following we assume operation-dependent failures. Based on anempirical study, Inman (1999) comes to the conclusion that the widespread assump-tion of exponential times to failure and exponential repair times is acceptable inmany cases.</p><p>There are several situations where stochastic processing times must be taken intoaccount. First, if the task is repetitively performed by a human operator, thenprocessing times will usually be random, as a certain amount of variability is inher-ent in human nature. Empirical studies show that task durations of human operatorswill have a coefficient of variation that is considerably less than one, which would bethe case for an exponential distribution. Secondly, if a number of flexible automaticmachines or robots assigned to a station are able to process a mixture of productvariants in any sequence, thenfrom the point of view of an external observerthe</p><p>150 H. Tempelmeier</p><p>SECOND PROOFS C.K.M. i:/T&amp;F UK/Tprs/Tprs41-1/Prs-2135.3d Int. Journal of Production Research (PRS) Paper 102135 Keyword</p><p>Dow</p><p>nloa</p><p>ded </p><p>by [</p><p>Uni</p><p>vers</p><p>ity o</p><p>f Il</p><p>linoi</p><p>s C</p><p>hica</p><p>go] </p><p>at 1</p><p>4:34</p><p> 08 </p><p>Dec</p><p>embe</p><p>r 20</p><p>14 </p></li><li><p>processing times of the variants can be considered as random. For a recent empiricalanalysis of processing times in automobile welding shops see Inman (1999).</p><p>Many researchers providing algorithms for the analysis of flow production sys-tems with stochastic processing times assume that processing times are exponential.In addition, several approaches are applicable only if the mean processing times,failure characteristics, and buffer sizes of all stations are identical. It is believed thatin the (deterministic) line balancing phase the system characteristics are set up suchthat there will be no station that is much worse than all the others (with respect to theisolated throughput). However, in industry this is not always the case. Due totechnical constraints, many real-life systems have stations with non-identical meanprocessing timeseven if automatic machines are used to process a single producttype. Differences in the mean processing times may also emerge as a result of asimultaneous buffer and workload allocation, as discussed in section 3.</p><p>Figure 1 depicts the isolated station throughputs for four real-life linear flowproduction systems. The detailed data are given in the appendix. Observe that thedata exhibit a certain amount of imbalance, which makes many algorithms for theevaluation of stochastic flow production systems inapplicable or, at best, imprecise.</p><p>From the practical data presented, it is clear that an analytical flow line modelmust be able to cover unequal processing times. In addition, unequal failure andrepair characteristics must also be covered by such a model. In what follows, modelsand algorithms available for the approximate performance analysis of these types offlow production systems are tested with regard to their applicability in real-lifeplanning environments. From a practical point of view, the algorithms must beable to analyse systems with up to 50 or 100 stations, a requirement that excludesexact algorithms.</p><p>2.1. Deterministic processing timesIn a flow production system that comprises exclusively automatic stations work-</p><p>ing on a single part type, the processing times are usually deterministic but vary fromstation to station. This is often due to the fact that it is not possible to find combina-tions of subprocesses that sum to exactly the same processing time at all stations.Flow production systems of this kind can be analysed with the Accelerated Dallery-David-Xie (ADDX) algorithm proposed by Burman (1995), which is based on thedecomposition method developed by Gershwin (1987) (see also Gershwin 1994) andwhich is an extension of the DDX-algorithm of Dallery et al. (1988). With respect tothe optimization algorithm discussed in section 3 it is noteworthy that the buffer sizesare modelled as continuous variables.</p><p>With the help of a large numerical experiment based on hypothetical system data,Burman (1995) showed that his algorithm was very accurate. In the following, thisalgorithm is applied to an invented system and several real-life systems.</p><p>Invented system 1Consider a system with invented data (ten stations with identical buffer sizes;</p><p>identical deterministic processing times s 1; identical failure processes at all sta-tions: failure rate f 0:007, repair rate r 0:095 (isolated throughput = 0.931)).Table 1 compares the system throughput found with the help of a SIMAN simula-tion model (Xsimulated) and with the ADDX algorithm (Xestimated).</p><p>151Optimization of flow production systems</p><p>SECOND PROOFS C.K.M. i:/T&amp;F UK/Tprs/Tprs41-1/Prs-2135.3d Int. Journal of Production Research (PRS) Paper 102135 Keyword</p><p>Dow</p><p>nloa</p><p>ded </p><p>by [</p><p>Uni</p><p>vers</p><p>ity o</p><p>f Il</p><p>linoi</p><p>s C</p><p>hica</p><p>go] </p><p>at 1</p><p>4:34</p><p> 08 </p><p>Dec</p><p>embe</p><p>r 20</p><p>14 </p></li><li><p>152 H. Tempelmeier</p><p>SECOND PROOFS C.K.M. i:/T&amp;F UK/Tprs/Tprs41-1/Prs-2135.3d Int. Journal of Production Research (PRS) Paper 102135 Keyword</p><p>Figure</p><p>1.</p><p>Isolatedstationthroughputs</p><p>ofreal-life</p><p>system</p><p>s.</p><p>Dow</p><p>nloa</p><p>ded </p><p>by [</p><p>Uni</p><p>vers</p><p>ity o</p><p>f Il</p><p>linoi</p><p>s C</p><p>hica</p><p>go] </p><p>at 1</p><p>4:34</p><p> 08 </p><p>Dec</p><p>embe</p><p>r 20</p><p>14 </p></li><li><p>Real-life system ANext, we consider the data of the real-life system A shown in table 18 in the</p><p>appendix. Table 2 presents four scenarios with different buffer allocations: (1) zerobuffers; (2) allocation of 285 buffers provided by the industrial planners; (3) 285buffers allocated optimally with the help of the algorithm discussed in section 3;(4) target throughput of 1.8 with 485 buffers allocated optimally.</p><p>In all cases considered, the simulated throughput is very well approximated. Theexperiment further demonstrates that distributing the given total number of 285buffers optimally among the stations would result in a slightly higher throughput,which is shown both by the estimated as well as the simulated values. Moreover, ifanother 200 buffers were introduced into the system, the throughput could increaseby several percent.</p><p>Real-life system BIn system A the availabilities of the different stations ranged between 96.7% and</p><p>99.4%. In order to examine the quality of the approximation for stations with loweravailability, consider system B with the data shown in table 19 in the appendix,which comprises stations with availabilities as low as 83%. Some of the stationshave availabilites of 100%. The isolated throughput of the worst station is onlyabout 50% of the is...</p></li></ul>

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