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This article was downloaded by: [Nipissing University]On: 16 October 2014, At: 16:49Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
International Journal of ProductionResearchPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/tprs20
Comparative performance analysis of aflexible manufacturing system (FMS): areview-period-based controlF. T. S. Chan a , R. Bhagwat b & S. Wadhwa ca Department of Industrial and Manufacturing SystemsEngineering , University of Hong Kong , Pokfulam Road, HongKong, P. R. Chinab Department of Mechanical Engineering , J. N. V. University ,Jodhpur, Indiac Department of Mechanical Engineering , Indian Institute ofTechnology , New Delhi, IndiaPublished online: 16 Nov 2007.
To cite this article: F. T. S. Chan , R. Bhagwat & S. Wadhwa (2008) Comparative performanceanalysis of a flexible manufacturing system (FMS): a review-period-based control, InternationalJournal of Production Research, 46:1, 1-24, DOI: 10.1080/00207540500521188
To link to this article: http://dx.doi.org/10.1080/00207540500521188
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International Journal of Production Research,Vol. 46, No. 1, 1 January 2008, 1–24
Comparative performance analysis of a flexible manufacturing
system (FMS): a review-period-based control
F. T. S. CHAN*y, R. BHAGWATz and S. WADHWAx
yDepartment of Industrial and Manufacturing Systems Engineering, University of Hong Kong,
Pokfulam Road, Hong Kong, P. R. China
zDepartment of Mechanical Engineering, J. N. V. University, Jodhpur, India
xDepartment of Mechanical Engineering, Indian Institute of Technology, New Delhi, India
(Revision received October 2005)
The flexibility of a flexible manufacturing system (FMS) has provided it withthe capability to become one of the most suitable manufacturing systems in thepresent manufacturing environment of customized and an increasing varietyof products with shorter life cycles. Significant research has been made onflexibility from different points of views. The paper focuses on the studyof flexibility in FMS from the view of a decision-and-information system.In modelling flexibility and other physical and operational parameters of anFMS, researchers have mostly assumed a decision-and-information system has thecapability of real-time control. The literature reports qualitatively that real-timecontrol may be difficult to achieve and justify economically. The paper presents acomparative study of an FMS operating under real-time control, review-period-based control and reactive control. It also focuses on the comparative perfor-mances of the key parameters such as routing flexibility and control strategies of anFMS operating under these different modes of a decision-and-information system.It contributes an approach using simulation under Taguchi’s method to study thevarious factors contributing to FMS performance and identifies the criticalparameters for improving performance. The result shows that review-period-basedcontrol can be effectively implemented in an FMS with a lower flexibility level.Smaller review-period size can perform comparable with real-time control. Thedecision-maker must ensure the FMS’s capability of having real-time control,otherwise it may result in a reactive control that may considerably deteriorate theperformance. The results under Taguchi’s method indicate that the routingflexibility and control strategy should have maximum relative percentage contri-butions in the performance of an FMS, while the decision-and-information systemshould have the minimum. Increasing the relative percentage contribution of adecision-and-information systemmay deteriorate the performance of a given FMS.
Keywords: Flexible manufacturing system; Review period; Routing flexibilitylevels; Control strategy; Taguchi’s method
1. Introduction
Flexible manufacturing systems (FMSs) are characterized by their ability to integratevarious entities and for their flexibility. An information system is the means to
*Corresponding author. Email: [email protected]
International Journal of Production Research
ISSN 0020–7543 print/ISSN 1366–588X online � 2008 Taylor & Francis
http://www.tandf.co.uk/journals
DOI: 10.1080/00207540500521188
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interface and integrate the entities of manufacturing systems, the mode ofsynchronizing the various entities and the method of coordinating them in orderto achieve the objectives (Weber and Moodie 1989). Flexibility is the meansto become more effective in the present manufacturing environment by increasingvariety. The FMS is considered one of the important strategic systems whencompeting in the present manufacturing environment. In recent years, researchershave focused on the flexibility of FMSs from different points of view and therehas been a considerable interest in integration. However, few studies haveconsidered both the aspects of an FMS simultaneously, i.e. flexibility and integra-tion (from a decision-and-information system view), particularly for the shop-floor.
Researchers have advocated and considered the real-time control capabilities,particularly at the shop-floor control of an FMS/computer-integrated manufactur-ing (CIM) system (for shop-floor control both FMS and CIM can be consideredas being the same; Browne et al. 1988). To have real-time control capability,the monitoring functions and control decision-making should be carried out ina negligible time. FMSs are considered to have a high level of integration throughthe information system and a high level of decision automation for real-timecontrol. The literature also suggests that a higher level of decision automationand information integration may be difficult to achieve and justify economically.It is important to know whether the real-time control capability for an FMS isnecessary to harness the advantages, particularly when it is capital-intensive, or someother mode of control can perform comparably. It may be fruitful to determinethe right kind of resources and technology to support the strategy and, hence,reach the objectives. It requires a careful balancing of the multiple viewpoint andcriteria in developing and managing the entire manufacturing system. In short,it requires a system perspective (Singh 1996). The present paper focuses onthe decision-and-information system view, flexibility view and control strategyview of FMSs.
The possible decision-and-information control views of an FMS can be identi-fied as real-time control, review-period (RP)-based control, reactive control andproactive control. The decision-maker can study a given FMS under variousmodes of control to identify a suitable control system with consideration offlexibility levels present and operating control parameters, which can be justifiedfrom the operational performance view and economically. It may be beneficialto analyse carefully the advantages and limitations associated with each controlmode of an FMS. With FMSs that do not have real-time capability, onealternative can be to look towards RP-based policy for shop-floor control.An important aspect for the decision-maker is to identify the time horizon forits periodicity. The RP-based policy may not be appreciated in the present ageof information technology but may be more commercially viable than real-timecontrol. This paper contributes an approach to study the various parametersof a given FMS for their behaviour, the relationship with other parametersand contributions in FMS performance using simulation under Taguchi’smethod. The approach also identifies the parameters with strategic contributionsin FMS performance and their operational level settings that can improveperformance. The literature related to Taguchi’s method and flexibility is easilyavailable, so not much emphasis is given to them here, except with respect tokey aspects.
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The paper is organized as follows. Section 2 presents the background andmotivation of the present study. Section 3 presents the descriptions of variousmodes of a decision-and-information system of an FMS. Section 4 underlinesthe methodology of the study. Experimentation design is presented in section 5.The results and discussion are organized in section 6. Finally, conclusions arepresented in section 7.
2. Background and motivation
A control decision process takes the system’s information in the form of variable,constraints, decision-space, objectives, etc., as input. Therefore, in an integrationaspect of a system, the decision system and information system can be consideredas an integral part of each other. If one system is referred (say a decision system)automatically, the other system (information system) will become a part of it.The information and communication system of an FMS is considered as its heart,which integrates the island of automation and provides inputs to control decisions.It is assumed the FMS control scheme has the ability to make decisions in real-time based on real-time local and global information about the system. Thecontrol of FMSs is highly contingent upon the availability of information aboutthe system’s local and global status. In fact, there is a symbiotic and intimaterelationship between the shop-floor control system and the information manage-ment system (Veeramani et al. 1993). According to Patankar and Adiga (1995),the information required by each activity available on a timely basis refers to inte-gration in CIM. Jorysz and Varnadat (1990) describe information as the lifebloodof CIM systems, the glue that ties together the various system functions andcomponents, and as such careful attention must be paid to its modelling.
Even for a fully automated and computerized system, some time under certainsituations it may be difficult to achieve real-time capability. According to Montazemiand Miltenburg (1991), in CIMs the delays may take place in communicationnetwork, the database may seek time in retrieving the information or a com-puter may be overloaded. Technical developments in the last couple of decadeshave made more information available than at any other time (Ben-Arieh andPollatscheck 2002) and organization should be prevented from overloadinginformation flow. Identifying and managing the desired quantity of informationis of great interest to manufacturing organizations. Similarly, according toVeeramani et al. (1993), if the information system is unable to cope with thishigh frequency of updates, the machine controller will not have access to real-time status information on the system. The number of transactions of informationthat needs to be processed will be increased exponentially with increases in resources.This can affect the degree to which the manufacturing system can be controlledin real-time. Thus, in our opinion, if it is not possible to access the real-timeinformation in fully computerized systems, the other modes for status informationmonitoring may be considered as alternatives. Pispa and Eriksson (2003) con-clude that the distinction between the cost of information technology investmentsand the potential benefits by implementing and using new information technologyapplication should be distinguished at a conceptual level. It should be understoodthat the mere existence of an information technology application does not realize
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the benefits. Recommending simulation as tool for advance analysis, they arguethat a modelling approach should involve a vision of the alternative solutionsas possible. According to Qiu et al. (2003), the long development cycle of shop-floor control systems, the cost and lack of integration capability are identifiedas challenging obstacles in deploying e-manufacturing. Similarly, according toPark et al. (2001), the design and operation of the FMS involve intricate andinterconnected decisions that result in maximum benefit to the system. However,design and operational decisions have been made separately. These should bemade simultaneously to achieve global optimization in the development of anFMS. They modelled the design and operating parameters of an FMS to identifya suitable combination of these parameters under multiple objectives usingcompromise programming, but they did not consider the information system andflexibility levels for control of an FMS. Yurdakul (2004) evaluated the optimumdesign of a CIM system considering the cost aspect of various physical resourcessuch as computer-aided design (CAD), computer-aided engineering (CAE),computer-aided manufacturing (CAM), DNC M/cs, CMM. However, they didnot consider the information system requirements or operational control aspectsof it. According to McAfee (2002), despite rapidly growing and large invest-ments in information technology, the literature has reported that empiricalresearch often has failed to identify tangible benefits associated with this investment,either in productivity or in other operational performance. The above researcher’sview reflects that it may look to be useful to invest in information technology,but it may be difficult to achieve the expected benefit from it.
2.1 Real-time control: a limitation
The fully computerized control systems require that all information must bespecified explicitly in computers. Further, sufficient intelligence needs to be builtinto the system to infer this information for control. The manufacturing organiza-tions have very few comprehensive tools available to help in the design of aninformation system (Harding et al. 1999) and if errors are made in the developmentof an information system, they can be so costly that the organization does notsurvive to attempt a second redesign. If a proposed design can be tested andevaluated before it is implemented, costly mistakes can be avoided. Harding et al.further proposed that an information system modelling approach should help inthe stepped building of a progressive design, starting from a very simple andpartial model. The RP mode or other mode of a decision-and-information systemcan be a building block in progressive design for real-time control. According toQiu et al. (2003), a lack of formal methods for providing a systematic approachto information integration in manufacturing system leads to an ill-developedinformation system. They further argued that in most manufacturing systems,operations (e.g. material movement, loading and unloading) are mainly performedeither semi-automatically or manually. Owing to a lack of complete, accurate andreal-time shop-floor data, a control system should give instructions accordingly.
According to Montazemi and Miltenburg (1991), in CIMs the delays maytake place in communication networks, i.e. the information transfer can taketime to process. It depends on the magnitude of information and the nature andcapacity of the communication medium; this may lead to non-real-time control.
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While discussing hybrid multi-agent system architecture for enterprise integrationusing a computer network, Nahm and Ishikawa (2005) describe that the discretereal-world is dynamic because of a diverse, frequently changing situation, andthere will be no obligation for a person or computerized agent to remain witha network for a certain period. They proposed a hybrid multi-agent architecturefor enterprise that updates internal and external states regularly. Here it isimportant to describe the limiting values of time interval for updates. Accordingto Veeramani et al. (1993), the representation and manipulation of manufacturing-related information in a computer is not a trivial task. This raises the levelof complexity of information management. This complexity stems from the hetero-geneous nature of manufacturing information. The complexity of informationmanagement in CIM is heightened by the varying frequency with which informationneeds to be updated. Information in a manufacturing system changes with time,some more frequently than others. It is important for resources to have the requiredcompetency to achieve the objective. While describing an information technology-based approach to model competencies, Harzallah and Vernadat (2002) arguethat there should be clear differentiation between the acquired and requiredcompetencies of the resources.
The above discussion shows that researchers have stressed the real-timeavailability of information and decision-making and their importance in produc-tion control, but at the same time some researchers also acknowledged thedifficulty in having real-time control. Non-real-time control has not receivedmuch attention from researchers, some of them have mentioned it qualitatively(Veeramani et al. 1993). In the present authors’ opinion, a control system modeof an FMS should be modelled when modelling flexibility, whether it is real- ornon-real-time.
The concept of a RP is not new and has been used by researchers in differentperspectives. Montazemi and Miltenburg (1991) describe the updating of statisticalinformation related to decision activities after a fixed interval of time (RP)for analysis in the simulation system called Dynamic Analysis of InformationSystems Environment (DAISE). Slomp and Zijm (1993) note that in an FMS,the assignment of jobs among the various production units and any off-lineactivity (if present) are executed periodically (RP) in most practical situations.In context of a GRAI modelling approach for enterprise modelling, Wainwrightand Ridgway (1994) describe the RP as a time interval after which the decision-maker compares the present state with that of the end-state or goal andadjusts the actions accordingly. Church and Uzsoy (1992) use the RP policy forrescheduling the jobs and they compare it with event-driven policy. Kim and Kim(1994) use the RP for selecting the suitable scheduling rule in simulation-basedon-line scheduling in an FMS. They further describe that the control systemperiodically monitors the shop-floor and checks system performance and,if required, it will select a new scheduling rule. Similarly, Chan and Chan (2001)propose a pre-emptive approach for dynamic scheduling for an FMS thatevaluates the performance measure based on three objectives at every check point(after a time interval) and accordingly selects a scheduling rule to improve theperformance measures. They further conclude that such dynamic scheduling mayperform better than on-line scheduling and too high a frequency of updating (almostreal-time) or too slow a frequency of updating of a scheduling strategy may not
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prove to be beneficial. Matsui et al. (2000) studied the control of the job shopunder periodic versus dynamic type for accepting or rejecting jobs. Similarly,Shafaei and Brunn (1999) studied the performance of scheduling rules in jobshop when rescheduling is done under a rolling time horizon (RP). They concludethat it is a good alternative when real-time information and decision-makingare not available. However, they have not compared the real-time and RPperformances at different levels of flexibility and sizes of the Khouja and Kumar(2002) use period-based forecasting and a production target to evaluate theinformation technology investments for making exact forecasting and volumeflexibility. Information technology investment for shop control will be beneficialonly when there is flexibility in the system. The information system investmentshould be in accordance with the level of flexibility present in the system (Wadhwaand Bhagwat 1998).
The RP policy is generally described by the previous researchers in the contextof either evaluating a system’s performance for monitoring or for implementingthe control decision/activity, but not related to a study of flexibility and controlstrategy performance and updating of a system’s status information. It maybe interesting to know how RP policy mode performs in comparison with real-timecontrol, and how the performance changes with change in the operating environ-ment. It may be interesting to know how control strategies and flexibility performunder RP and what is the impact of the size of RP?
The RP policy may not require a high level of computerization and decisionautomation as required in on-line continuous real-time data capturing. Hence,it may be cost-effective. The designer may reach a trade-off between the advan-tages of an on-line real-time system and cost-effectiveness of the RP. The systemdesigner/controller would like to invest only to the required levels in these keyfactors, such as decision-and-information system and flexibility level. It may behelpful to develop a modelling framework taking a simulator as a platformto research the impact of such key factors on FMS performance. It is essential thatthe models act as a demonstration platform for practitioners as well as researcherson the usefulness of non-real-time control.
An industrial motivation can be expressed as a question: whether an expensiveon-line system with real-time control is more desirable in view of its capabilitiesor any RP-based monitoring can be more justifiable in a given scenario? Theimportant research issues are to study the role of flexibility with its levels andcontrol strategy in an FMS operating under RP-based monitoring. It is alsoimportant to study the nature of performance behaviour of a given FMS underthe RP mode. The system designer needs some guidelines to use the RP inpractice as a stepped investment in decision-and-information system towards real-time control. This paper attempts to address some of these issues. Computersimulation has been used as a tool to facilitate the modelling of various operatingconditions.
This paper contributes an approach to study the performance of a givenFMS with respect to flexibility levels and control strategy under different modesof control. The approach can also help the decision-maker identify the opti-mum setting of various control parameters of a given FMS. The approachalso identifies the critical parameters that have major contributions in FMSperformance improvement or deterioration.
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3. Modes of an FMS control
In the present study, RP mode of an FMS control has been compared withreal-time control. However, under a real-time control environment, if a systemcannot achieve real-time capability (due to any reasons), then it will result ina reactive control environment. Therefore, the reactive control mode of an FMShas been also briefly compared with the above two modes.
In RP (RP)-based monitoring, the status information is updated after afixed interval of time, i.e. at the start of each RP, and this information is usedfor all the decision-making activities taking place during the RP. Considerfigure 1(a). The operator/decision-maker has access to the information, whichis updated according to RP policy. This RP-based information is used by thedecision-maker for all the control activities during RP. As shown, consider anactivity at time tc1 requires decision-making (say sequencing a decision). Theoperator/decision-maker will use the status information that has been updatedat the start of the RP at time trp(n). Similarly, for any other decision-makingactivities during this RP (say at time tc2), the operation/decision-maker will usethe same status information that has been updated at time trp(n), i.e. updated atthe start of the RP. Thus, same status information is used for all control decisiontaking place during the RP.
In real-time control, the status information is collected and the decisionis made and implemented in real-time without any time delay. As shown infigure 1(b), consider an activity taking place at time tc. The decision based onreal-time information is implemented at time tc itself without any time delay.While in reactive control, the activity has to wait for a time interval (reactiontime) until a decision is implemented. Consider figure 1(c), the activity takesplace at time tc. In reactive control, the activity will be waiting for a time �t(reaction time) until a decision is implemented. The time �t depends on the level of adecision-and-information system automation level present in the system.
4. Methodology
According to Shewchuk and Moodie (2000), flexibility is seen as a principalmechanism for survival in the new manufacturing environment. The goal of the
trp(n) tc1 tc2 trp(n+1)
(a) (b) (c)
tc tc
∆t
RP
Figure 1. Modes of a decision-and-information system for control of an FMS:(a) review-period control, (b) real-time control and (c) reactive control.
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strategy is now strategic flexibility. An FMS is characterized by its ability tohave flexibility. Gerwin (1986) argues that the system facing uncertainty generallyuses flexibility as an adoptive response, and that since there are several kinds ofuncertainty, there should be several kinds of corresponding flexibility. Sarker et al.(1994) propose a classification for the types of manufacturing flexibility: routing,machine, process, expansion, job, design, material handling, set-up time andvolume. Similarly, researchers have presented classifications for the types of flexi-bility from different angles (Browne et al. 1984, Rachamadugu and Stecke 1994).Browne et al. present a classification for the types of flexibility with eight types,of which machine flexibility and routing flexibility (RF) have been consideredto be important. These two flexibilities provide the way for other types ofmanufacturing-related flexibility. The other types of flexibility can be derivedin an FMS when machine flexibility and RF are present. Consider if an FMShas a capability of having machine flexibility for a given part family or a givenproduct range, then of these two flexibilities, the RF level only remains as oneof the controlling parameters of an FMS. The decision-maker can route the partsaccording to the RF level present in the system. The RF levels have been modelledin the present study.
The performance of an FMS depends on its physical and operating character-istics. The physical characteristics refer to process time, loading/unloading time,numbers of pallets and fixtures, tool slots, etc., that cannot be changed once asystem is installed. Operating characteristics refers to control strategies used (ona basis of the current status of system) to operate a physical system in the bestpossible way to achieve the objective. The decision-and-information system facil-itates the decision-maker by providing the required status information for operat-ing decisions such as sequencing decisions (part selection), dispatching decisions(machine selection) depending on the level of RF present in the system and,if required, transportation vehicle selection and tool selection on a machine.The transportation time and tool selection time on a machine basically affect thecompletion time of a job. These can be modelled in processing times of jobsaccordingly to avoid the complex modelling.
It is expedient to use simulation as a modelling tool. A single hypotheticalmanufacturing system model that can capture the logic of the different levels ofRF and control strategies has been configured. According to Koo and Jang (2002),FMS is complex in nature due to large the number of variables involved. Themodelling of an FMS may become complex and difficult to study if all the factorsare considered simultaneously. Further, some factors may have a minor impacton the performance of an FMS with respect to the objective of study and suchparameters can be avoided or normalized to be included in other parameters tomake modelling simple. Thus, it is felt that the defined system may focus on a fewkey factors and should not be too complex to study. In a sense, interest wason a typical size and complexity of the system that has been widely researched.To have more insight about the simulation results, these will be further analysedunder Taguchi’s method.
Taguchi’s method will be used to study the impact and also to outlinean approach that can help system designers/decision-maker to have insightabout the relative importance and performance contributions of FMS factors.This approach provides a convenient and efficient way to study the factors and
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their interactions simultaneously. In Taguchi’s method, the results can be studiedto estimate the relative contributions of individual factors and their interactions,to establish the best or optimum condition for a product or a process, and toestimate the response under the optimum conditions. In the present study, it isknown that a real-time control will provide the optimum condition to operatean FMS. Therefore, Taguchi’s method has been used to study the contributionsof individual factors and their interactions in the performance of a given FMSoperating under RP mode. The factors related to operational control of a givenFMS have been considered for study: (1) routing flexibility (RF) level, (2) RP timeinterval (RP), (3) sequencing rule (SR) and (4) dispatching rule (DR) for a givenFMS and interactions between them, each at two levels.
Taguchi’s experimental design paradigm is based upon the technique ofmatrix experiments (Phadke 1989) called Orthogonal Arrays (OA). OA provides anopportunity to study the effects of several process parameters and their interactionssimultaneously. The interaction refers to the influence of change in operational levelsof one factor on the performance of other factor(s). Taguchi’s method determinesthe relationships for interacting columns in OA. These standard relationshipsof interacting columns of OA are provided in standard tables called triangular tablesof interactions and they contain information about the interactions of the variouscolumns of OA. The process of experimental design under Taguchi’s methodincludes selecting the suitable OA and assigning the factors and their interactions tothe appropriate columns. The selection of OA depends on the number of factors andtheir interactions to be studied and on the levels of factors. The selected OA shouldhave columns equal to (or more than) the number of factors, and their interactionsis to be studied. The extra columns in OA can be left unallotted. After allocation ofOA columns to the factors and interactions, the conditions for the individualexperiment, i.e. the level setting of each factor to conduct the experiments, aredetermined according to the rows of OA. If an OA has 16 rows, then each row of theOAwill define an experimentation condition to conduct 16 experiments. The results ofthis single set of experiments under Taguchi’s method are then analysed by usinganalysis of variance (ANOVA). The level settings of the factors can then be changedin a next set of experiments to study the other levels. The relative percentagecontribution of each factor and interactions between them can be quantitativelydetermined by using the ANOVA (Bagchi 1993).
The completeness of Taguchi’s method lies in the identification of the keyfactors and their levels that can influence performance and considering them forstudy. According to Logothetis and Wynn (1989), if all the major performancecontributing factors are not considered under Taguchi’s method, then the studymay not provide a clear representation. Logothetis and Wynn further suggestedthat to identify all the major contributing factors considered in Taguchi’s method,the percentage contribution of pooled errors in the ANOVA analysis should notbe more than 15–20%.
5. Experimental design
A simulation approach was used. The results of simulation experimentationwere then further analysed under Taguchi’s method. Simulation modelling was
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widely proposed by the researchers for the study of complex systems. Simulation
provides an easy platform to model the variables that are difficult to model
mathematically or which requires unrealistic assumptions. Therefore, simulation
is suitable for representing a complex system to get a feeling for the real system.
It may be especially true for FMSs with a heterogeneous and dynamic environ-
ment where on-line control strategies with respect to time are employed.In the present study, a deterministic FMS with six flexible machines with
input buffers of infinite capacity was considered. Six part types (P1–6) were
considered. The FMS is considered to have RF. The parts can be routed to
alternative processing routes in the system depending on the RF level present.
The concept of RF is explained below. The numbers of operations required for
processing each part type were taken as four to six with a minimum processing
time of an operation as 35 units of time and a maximum processing time of an
operation as 100 units of time, with an average processing time of operations as
55 units of time. Figure 2 shows the configuration of FMS studies. Table 1
shows the details of processing times assumed in deterministic simulation and the
alternative machines available at different RF levels. The inter-arrival time of each
part type has been assumed randomly such that no machine is waiting idle when
processing the first operation on any part type. The make-span for processing
a product-mix of 300 parts was modelled as a performance measure.The control strategies were modelled as a combination of a sequencing rule
and a dispatching rule. The sequencing and dispatching rules modelled were as
follows:
. Sequencing rules (SR): SPT, select the part with the shortest processing
time on the machine; MBPT, select the part for processing next that has
the maximum balance processing time left.
B1
M1
B2
M2
B3
M3
B4
M4
B5
M5
B6
M6
Machines: M1, M2.M3, M4, M5 andM6
Buffers: B1, B2,B3, B4, B5, and B6
Parts:P1 – 4 operationsP2 – 4 operationsP3 – 5 operationsP4 – 5 operationsP5 – 6 operationsP6 – 6 operations
Figure 2. Configuration of the sample flexible manufacturing system studied.
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Table
1.
Alternativemachines
available
atdifferentlevelsofflexibilitywithprocessingtimes.
Part
type
Operation
number
Routingflexibility
(RF¼0)
Routingflexibility
(RF¼1)
Routingflexibility
(RF¼2)
Routingflexibility
(RF¼3)
Routingflexibility
(RF¼4)
P1
O1
M1(40)#
M1,M2(40)
M1,M2,M4(40)
M1,M2,M4,M5(40)
M1,M2,M4,M5,M1(40)
O2
M3(50)
M3,M5(50)
M3,M5,M2(50)
M3,M5,M2,M1(50)
M3,M5,M2,M1,M4(50)
O3
M4(60)
M4,M1(60)
M4,M1,M5(60)
M4,M1,M5,M2(60)
M4,M1,M5,M2,M6(60)
O4
M6(70)
M6,M3(70)
M6,M3,M1(70)
M6,M3,M1,M2(70)
M6,M3,M1,M2,M5(70)
P2
O1
M4(40)
M4,M3(40)
M4,M3,M5(40)
M4,M3,M5,M6(40)
M4,M3,M5,M6,M1(40)
O2
M2(55)
M2,M1(55)
M2,M1,M3(55)
M2,M1,M3,M4(55)
M2,M1,M3,M4,M5(55)
O3
M6(54)
M6,M4(54)
M6,M4,M2(54)
M6,M4,M2,M1(54)
M6,M4,M2,M1,M3(54)
O4
M5(95)
M5,M2(95)
M5,M2,M4(95)
M5,M2,M4,M3(95)
M5,M2,M4,M3,M6(95)
P3
O1
M5(60)
M5,M6(60)
M5,M6,M2(60)
M5,M6,M2,M1(60)
M5,M6,M2,M1,M4(60)
O2
M1(45)
M1,M4(45)
M1,M4,M6(45)
M1,M4,M6,M5(45)
M1,M4,M6,M5,M2(45)
O3
M3(48)
M3,M5(48)
M3,M5,M4(48)
M3,M5,M4,M6(48)
M3,M5,M4,M6,M1(48)
O4
M2(65)
M2,M1(65)
M2,M1,M6(65)
M2,M1,M6,M5(65)
M2,M1,M6,M5,M4(65)
O5
M4(75)
M4,M6(75)
M4,M6,M1(75)
M4,M6,M1,M2(75)
M4,M6,M1,M2,M3(75)
P4
O1
M2(40)
M2,M1(40)
M2,M1,M3(40)
M2,M1,M3,M4(40)
M2,M1,M3,M4,M5(40)
O2
M5(50)
M5,M3(50)
M5,M3,M4(50)
M5,M3,M4,M6(50)
M5,M3,M4,M6,M1(50)
O3
M6(50)
M6,M2(50)
M6,M2,M1(50)
M6,M2,M1,M3(50)
M6,M2,M1,M3,M6(50)
O4
M3(45)
M3,M6(45)
M3,M6,M5(45)
M3,M6,M5,M4(45)
M3,M6,M5,M4,M1(45)
O5
M1(85)
M1,M5(85)
M1,M5,M2(85)
M1,M5,M2,M4(85)
M1,M5,M2,M4,M6(85)
P5
O1
M6(40)
M6,M5(40)
M6,M5,M1(40)
M6,M5,M1,M3(40)
M6,M5,M1,M3,M2(40)
O2
M4(45)
M4,M6(45)
M4,M6,M5(45)
M4,M6,M5,M2(45)
M4,M6,M5,M2,M3(45)
O3
M2(45)
M2,M3(45)
M2,M3,M6(45)
M2,M3,M6,M4(45)
M2,M3,M6,M4,M5(45)
O4
M5(40)
M5,M4(40)
M5,M4,M3(40)
M5,M4,M3,M1(40)
M5,M4,M3,M1,M2(40)
O5
M1(55)
M1,M2(55)
M1,M2,M3(55)
M1,M2,M3,M6(55)
M1,M2,M3,M6,M5(55)
O6
M3(100)
M3,M1(100)
M3,M1,M6(100)
M3,M1,M6,M5(100)
M3,M1,M6,M5,M4(100)
P6
O1
M3(35)
M3,M4(35)
M3,M4,M6(35)
M3,M4,M6,M2(35)
M3,M4,M6,M2,M5(35)
O2
M5(45)
M5,M2(45)
M5,M2,M1(45)
M5,M2,M1,M3(45)
M5,M2,M1,M3,M6(45)
O3
M4(55)
M4,M6(55)
M4,M6,M5(55)
M4,M6,M5,M3(55)
M4,M6,M5,M3,M2(55)
O4
M1(50)
M1,M5(50)
M1,M5,M2(50)
M1,M5,M2,M6(50)
M1,M5,M2,M6,M3(50)
O5
M6(52)
M6,M3(52)
M6,M3,M4(52)
M6,M3,M4,M1(52)
M6,M3,M4,M1,M2(52)
O6
M2(75)
M2,M4(75)
M2,M4,M5(75)
M2,M4,M5,M3(75)
M2,M4,M5,M3,M1(75)
#Alternativemachines
are
available
attheroutingflexibilitylevel
(processingtimeonavailable
machines).
Comparative performance analysis of an FMS 11
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. Dispatching rules (DR): MINQ, select a machine for processing the nextoperation on the part that has a minimum queue (number of parts) atthe input buffer of a machine; MWTQ, select a machine for the nextoperation that has a minimum waiting time in the queue, i.e. the minimumof sum of processing times of all the parts waiting in an input buffer ofthe machine.
According to Shnits et al. (2004), recent 100 research studies (1988–2001) of com-plex manufacturing systems such as FMS and job shop have considered SPTas a most popular sequencing rule. In the present paper, the objectives are tostudy the comparative performance FMS under RP at different flexibility levelsand control strategies and not to find an optimum performing sequencing rule.Therefore, these two rules were selected to study their performance.
The system is considered to have RF. The levels of RF were varied within theexisting machines. The parts can be processed on one of the available alternativemachines. The number of alternative machines available depends on the level ofRF set in the system (table 1). The order of operations remains the same at allRF levels. The RF concept (similar to Chan 2001) can be described as follows:
. RF¼ 0: there is exactly one machine available for an operation on a givenpart, i.e. there are zero alternatives.
. RF¼ 1: implies there are two possible machines for processing the sameoperation, i.e. there is exactly one more alternative machine (other than themachine available at RF¼ 0) for any operation on any part.
. RF¼ 2: implies there are three possible machines for processing the sameoperation, i.e. there are exactly two more machines available for processingthe same operation (other than the machine available at RF¼ 0).
. RF¼ 3 and 4: imply three and four more alternative machines are available,respectively, for any part-operation.
Each machine has associated sequencing decision and dispatching decisionpoints. The sequencing decision point takes decisions to select a part for nextprocessing from the input buffer of the machine according to sequencing ruleenforced. The decision point takes decisions to select a machine from the avail-able alternative machines for processing a next operation on the outgoing partaccording to the dispatching rule enforced. The number of alternative machinesavailable depends on the level of RF present in the system. The RF levels werevaried as 0 (no RF), 1, 2, 3 and 4.
To study the effect of RP, the RP size was varied in steps from RP¼ 0(real-time control) to 65 units of time. For each step of RP size, the above-mentioned four levels of RF were varied. The RP size and RF levels were alsovaried for possible combinations of scheduling rules considered for controlstrategy. Accordingly, simulation results were obtained for different combinationsof RF level, RP size, sequencing rule and dispatching rule. To study furthersimulation results under Taguchi’s method, we considered the variation of factorsat two levels at a time. The two levels under Taguchi’s method provide for alinear study of the assumed factors with a good approximation. For a non-linear study, three or higher levels of factors can be assumed under Taguchi’smethod. The advantages with the two levels are that the number of experiments to be
12 F. T. S. Chan et al.
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conducted is less and the study of the interactions of the factors is also nottoo complicated as it may be in three or higher levels. The other advantage of usingtwo levels is that it helps in studying the influence of individual factors by varyingonly the upper level (or the lower level) of that factor, and keeping the levels ofall other factors constant. In three and higher levels, this variation may be difficultto study. For example, to study the impact of RF level, the lower level of RF canbe kept constant as RF¼ 0 in all sets of experiments, while the upper level of RFcan be varied in different sets of experiments as RF¼ 1, 2, 3 and 4. For two-levelfactors, OA L16 has been considered when studying four factors and six interac-tions between them. OA L16 has been considered to avoid the allocation ofinteracting columns to the factors. For example, column 3 of OA L16 captures theinteraction between the factors allocated to columns 1 and 2; therefore, column 3has been avoided from allocation to any factor so that: (1) the impact of inter-action can be studied and (2) the impacts of the factor and interaction do notget cumulate. Further, it may be not possible to get an OA size exactly equal tothe number of factors and interactions to be studied, so the selected OA shouldhave a number of columns equal to or more than the number of factors andinteractions to be studied. The extra columns in OA can be left unallotted withoutany harm.
To study simulation results under Taguchi’s method, four time intervals ofRP were considered in comparison with real-time:
. Ten units of time (RP¼ 10).
. Twenty units of time (RP¼ 20).
. Thirty-five units of time (RP¼ 35).
. Fifty units of time (RP¼ 50).
For real-time control, the RP time interval will be zero (i.e. RP¼ 0). The levelcombinations of RP and real-time considered for Taguchi’s method experimentswere:
. RP¼ 0 and 10.
. RP¼ 0 and 20.
. RP¼ 0 and 35.
. RP¼ 0 and 50.
These four level combinations of RP were used one-by-one for two levelscombination of RF: 0 and 1, and 0 and 4.
Table 2 shows the levels combinations of factors considered under Taguchi’smethod experimentation. Table 3 shows different experimental conditions alongthe rows of the table under OA L16. Each row indicates the combination of factorlevels for a single experiment. In table 3, the RF levels combination were 0 and 1.In table 3, the columns 1, 2, 4 and 11 of orthogonal array L16 were allocated tofour factors: RF, SR, DR and RP interval, respectively. Columns 3, 5, 6, 9, 10and 15 were allocated to the interactions between the above four factors. Columns 7,8 and 12–14 were left unallotted because columns 7, 8, 13 and 14 capture theinteractions with column 12, which was also unallotted. The last four columnsof table 3 show the make-span results of the simulation experiments carried outunder Taguchi’s method for different levels of RP. Similarly, table 4 shows theexperimentation combinations and simulation results for RF levels 0 and 4.
Comparative performance analysis of an FMS 13
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5.1 Assumptions
One objective of the study was a comparative study of the impact of RP on the
performance of a given FMS with respect to RF levels and control strategies.
As cited above, an FMS consists of numerous physical and operating factors
that can influence performance. Some factors may have a minor effect on the
performance of an FMS. The modelling of such parameters may make modelling
complex and hide the actual impacts of key parameters. The focus of the study
has been to fish-out the actual impact of RP on the performance of an FMS
shop-floor from the view of operational control. Thus, key factors related to
operational control of an FMS, which are in the control of a decision-maker,
has been focused. Some factors not in control of a decision-maker or the factors
that can have non-uniform impacts on the performance of an FMS under different
situations have been considered as ideal. It has been assumed that machines
never breakdown and raw material is always available. These factors are not in
the control of the decision-maker and also may have a non-uniform effect on FMS
performance under different situations. The decision-maker only has access to
information about the non-availability of raw material and machine breakdowns,
and he takes decisions accordingly by changing the RF level. In the present study,
the variation in RF level was considered. Further, the impacts of these factors
may cumulate with the impact of RP to restrict the actual image of FMS perfor-
mance under RP. The make-span was considered as the performance measure,
hence due dates are not considered. Pre-emption of a part’s operation is not
allowed (i.e. operations that begin processing are completed without interruption).
Each machine can process only one operation at a time. Set-up times and
transportation times are included in the processing times.
6. Results and discussion
This section is organized in two parts. First, the results of simple simulation
experiments are presented, which will provide an understanding about the
decision-and-information system and the performance behaviour of a given FMS
Table 2. Factors level combinations considered for Taguchi’s method experimentation.
Factors Assumed two levels of the factors
Sequencing rule (SR) 1: SPT2: MBPT
Dispatching rule (DR) 1: MINQ2: MWTQ
Review-period size (RP)(upper level has been varied for four valuesin different sets of experiments)
1: 0 (zero)2: 10, 20, 35 and 50
Routing flexibility (RF) (upper level has beenvaried for two values in different sets of experiments)
1: 0 (zero)2: 1 and 4
Interactions between the above factors
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Table
3.
L16OA
foranexperim
entationsetwithroutingflexibilitylevels,RF¼0–1,andreview-periodslevels,RP¼0–10,0–20,0–35and0–50.
Thelast
threecolumnsindicate
themake-spanresultsofsimulationexperim
ents
under
theTaguchi’smethodattheabove-mentioned
levelsof
review-period.
12
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11
12
13
14
15
Results
Facts
(make-span)
Experim
ent
number
RF
SR
RF�
SR
DR
RF�
DR
SR�
DR
––
SR�
RP
RF�
RP
RP
––
–DR�
RP
RP¼
0–10
RP¼
0–20
RP¼
0–35
RP¼
0–50
1RF¼0
SPT
1MIN
Q1
1–
–1
1RP¼0
––
–1
16057
16057
16057
16057
2RF¼0
SPT
1MIN
Q1
1–
–2
2RP¼
10/20/35/50
––
–2
15918
15937
15818
16113
3RF¼0
SPT
1MWTQ
22
––
11
RP¼0
––
–2
16057
16057
16057
16057
4RF¼0
SPT
1MWTQ
22
––
22
RP¼
10/20/35/50
––
–1
15918
15937
15818
16113
5RF¼0
MBPT
2MIN
Q1
2–
–1
2RP¼
10/20/35/50
––
–2
16037
16198
16247
16279
6RF¼0
MBPT
2MIN
Q1
2–
–2
1RP¼0
––
–1
16286
16286
16286
16286
7RF¼0
MBPT
2MWTQ
21
––
12
RP¼
10/20/35/50
––
–1
16037
16198
16247
16279
8RF¼0
MBPT
2MWTQ
21
––
21
RP¼0
––
–2
16286
16286
16286
16286
9RF¼1
SPT
2MIN
Q2
1–
–2
1RP¼
10/20/35/50
––
–2
15400
15543
15655
16117
10
RF¼1
SPT
2MIN
Q2
1–
–1
2RP¼0
––
–1
15516
15516
15516
15516
11
RF¼1
SPT
2MWTQ
12
––
21
RP¼
10/20/35/50
––
–1
15903
16350
16400
16443
12
RF¼
1SPT
2MWTQ
12
––
12
RP¼
0–
––
215683
15683
15683
15683
13
RF¼
1MBPT
1MIN
Q2
2–
–2
2RP¼0
––
–1
15698
15698
15698
15698
14
RF¼1
MBPT
1MIN
Q2
2–
–1
1RP¼
10/20/35/50
––
–2
16047
15922
15988
16100
15
RF¼1
MBPT
1MWTQ
11
––
22
RP¼0
––
–2
15924
15924
15924
15924
16
RF¼1
MBPT
1MWTQ
11
––
11
RP¼
10/20/35/50
––
–1
16130
16200
16130
16480
Comparative performance analysis of an FMS 15
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Table4.
L16OA
forexperim
entationsetwithroutingflexibilitylevels,RF¼0–4,andreview-periodslevels,RP¼0–10,0–20,0–35and0–50.Thelast
fourcolumnsindicate
themake-spanresultsofsimulationexperim
ents
under
theTaguchi’smethodattheabove-mentioned
levelsofreview-period.
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11
12
13
14
15
Results
Facts
(make-span)
Experim
ent
number
RF
SR
RF�
SR
DR
RF�
DR
SR�
DR
––
SR�
RP
RF�
RP
RP
––
–DR�
RP
RP¼
0–10
RP¼
0–20
RP¼
0–35
RP¼
0–50
1RF¼0
SPT
1MIN
Q1
1–
–1
1RP¼0
––
–1
16057
16057
16057
16057
2RF¼0
SPT
1MIN
Q1
1–
–2
2RP¼
10/20/35/50
––
–2
15918
15937
15818
16113
3RF¼0
SPT
1MWTQ
22
––
11
RP¼0
––
–2
16057
16057
16057
16057
4RF¼0
SPT
1MWTQ
22
––
22
RP¼
10/20/35/50
––
–1
15918
15937
15818
16113
5RF¼0
MBPT
2MIN
Q1
2–
–1
2RP¼
10/20/35/50
––
–2
16037
16198
16247
16279
6RF¼0
MBPT
2MIN
Q1
2–
–2
1RP¼0
––
–1
16286
16286
16286
16286
7RF¼0
MBPT
2MWTQ
21
––
12
RP¼
10/20/35/50
––
–1
16037
16198
16247
16279
8RF¼0
MBPT
2MWTQ
21
––
21
RP¼0
––
–2
16286
16286
16286
16286
9RF¼4
SPT
2MIN
Q2
1–
–2
1RP¼
10/20/35/50
––
–2
15124
15639
16693
17160
10
RF¼4
SPT
2MIN
Q2
1–
–1
2RP¼0
––
–1
14384
14384
14384
14384
11
RF¼4
SPT
2MWTQ
12
––
21
RP¼
10/20/35/50
––
–1
15762
16813
16991
17523
12
RF¼4
SPT
2MWTQ
12
––
12
RP¼0
––
–2
14642
14642
14642
14642
13
RF¼4
MBPT
1MIN
Q2
2–
–2
2RP¼0
––
–1
15351
15351
15531
15351
14
RF¼4
MBPT
1MIN
Q2
2–
–1
1RP¼
10/20/35/50
––
–2
15666
16109
16770
17644
15
RF¼4
MBPT
1MWTQ
11
––
22
RP¼0
––
–2
15556
15556
15556
15556
16
RF¼4
MBPT
1MWTQ
11
––
11
RP¼
10/20/35/50
––
–1
16121
16724
16923
17821
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under RP control. Then simulation results are discussed under Taguchi’s methodto provide a quantitative insight on the results.
6.1 Study of simulation results
Figure 3 shows the impact of different RP sizes on the make-span performance undervarious levels of RF and having control strategy as a combination of SPT sequencingrule and MINQ dispatching rule. For a given level of RF and a given controlstrategy, the performance of an FMS depends upon the decision-and-informationsystem (RP size). It can be seen from figure 3 that the increase in RF level isbeneficial when RP size is small or it is real-time. It can be further noted thatmake-span performance with a small RP may be occasionally better than real-time.It can be explained as some of the low priority parts that do not get a chance forearly processing due to real-time control and create bottlenecks afterwards may getan early chance of processing due to RP-based control and to avoid bottlenecks. Thissuggests there may be some look-ahead control strategies that can perform better inRP control than a real-time control, particularly when RP size is small and the RFlevel is low. This clearly indicates that a decision-maker should be very careful andclear about the needs of real-time control when the RF level is low. The investmentsin real-time control may not yield expected benefits matching to performanceimprovement. The benefits of RF decrease as the size of RP increases. When RP(RP) size is less than 20 units of time, there is an advantage of increasing the RF level
14000
14500
15000
15500
16000
16500
17000
17500
18000
18500
19000
RF=0 RF=1 RF=2 RF=3 RF=4
Routing flexibility
Mak
espa
n
RP=0 (Real-Time) RP=4 RP=10RP=20 RP=35 RP=50RP=65 RP=80
FCFS, RF=4
Performance is counterproductive in this region
FCFS, RF=0
Flexibility performance zone
Perfromace improvement due toinformation system and control strategy
Reactive controlreation time = 2 units
Reai-time control
Figure 3. Make-span performance for various levels of routing flexibility at differentreview-period size with shortest processing time (SPT) sequencing and a minimum queue(MINQ) dispatching rule.
Comparative performance analysis of an FMS 17
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up to 4. When RP size is 20 units of time, there is an advantage of increasing the RFonly up to 2. Any further increase in RF level at RP¼ 20 for a given FMS maybecome counterproductive with respect to performance at RF¼ 2, but may beproductive with respect to RF¼ 1 or 0 (i.e. performance may be better than theperformance at RF¼ 1 or 0, but not better than the performance at RF¼ 2).Similarly, when RP is approximately 35 units of time, the increase in RF is beneficialonly up to RF¼ 1. This indicates that for a given FMS, performance may decreasewith an increase in RP size. Further, it can be seen in figure 3 that for all RP sizes, therate performance deterioration increases with increasing RF levels. Performancedeterioration is more at RF¼ 2 than at 1, at 3 than at 2, and at 4 than at 3.Therefore, for a given RP size, performance deterioration may be more at higherlevels of RF as compared with lower levels of RF.
For a given FMS, the increase in RF under RP mode may be always beneficialwhen RP size is small (less than 20 units of time). When RP is more than 20 unitsof time, an increase in RF may be advantageous up to a certain level with reducedbenefits; thereafter, any further increase in RF may become counterproductive.The decision-maker can reach a trade-off between the expected improvementin performance through an increase in RF level and corresponding investments inthe decision-and-information system (i.e. RP size) required. Alternatively, for a givenlevel of RF, a control strategy and cost-effective decision-and-information systemcan be identified.
Considering another view of figure 3, two lines have been marked as First-Come-First-Served (FCFS), RF¼ 0, and 4. The line FCFS, RF¼ 0, represents themake-span performance of given FMS with sequencing rule as FCFS and RF level,RF¼ 0 (i.e. no RF in the system). As no RF is present, no dispatching ruleis required. When the FCFS sequencing rule is used and a system does not haveany RF, no information system is required and no control decisions are required.The operator/machine will process the parts in their incoming sequence. This canbe referred as minimum/worst conditions for the performance of a given FMS.The performance of the system, at any point of time, should not deteriorate belowthis performance. The FCFS, RF¼ 0 line, in figure 3 represents this condition. Themake-span performance should be always below this line. If make-span performancefalls above this line due to any reason(s) (i.e. due to use of a worse control strategyor a type of a decision-and-information system, or both), then the decision-makershould analyse the control strategy enforced and the decision-and-informationsystem to improve the performance or at least switch to the condition of no RFand a FCFS sequencing rule. The make-span performance region above this linecan be termed as a counterproductive performance zone of the given FMS.
The line FCFS, RF¼ 4, in figure 3 represents the make-span performancewith RF level 4, sequencing rule as FCFS and dispatching rule as MINQ. Whenthe system has flexibility, certain information for control decisions (and hencedecision-and-information system) is required to evaluate the alternatives availableby virtue of flexibility. It has been considered that a non-real-time, slow andcost-effective information system with an RP size of 50 units of time (approximatelyequal to the average processing time of jobs) is present for dispatching decisions.The sequencing rule is FCFS, hence no decisions and information is required forsequencing decisions. This condition can be considered as a representation of theperformance of a given FMS due to the available RF only, and without any role
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of a decision-and-information system. The line FCFS, RF¼ 4, represents this
performance. Therefore, any make-span performance falling between FCFS,
RF¼ 0 line, and FCFS, RF¼ 4 line (in figure 3), will have a performance con-
tribution only of the available RF, but no positive contributions of a decision-
and-information system or superior control strategy. The make-span performance
zone between FCFS, RF¼ 0 line, and FCFS, RF¼ 4 line, can be termed as
a ‘flexibility performance zone’. On the other hand, if the make-span performance
falls between these two lines in the presence of a decision-and-information system
and/or using superior control strategy, then it can be said that the decision-
and-information system and/or control strategy will have a negative impact on
the deterioration of the performance of a given system and will reduce the
benefits of RF. A better control strategy and/or decision-and-information system
can improve the performance. The decision-maker can identify a superior
control strategy and/or decision-and-information system to harness the benefits
of available flexibility.Further, make-span performance below FCFS, RF¼ 4 line, represents the con-
dition where RF, control strategy and the decision-and-information system have
a combined positive contribution in improving the performance of a given FMS.
This performance improvement is due to synchronization between all the operating
parameters of an FMS. An FMS is always expected to have performance below
the FCFS, RF¼ 4 line.The above analysis provides a generic approach to a decision-maker for studying
and analysing a given FMS and identifying its counterproductive zone, flexibility
zone and synchronized productive zone with respect to the decision-and-information
system and control strategy. The approach also provides an insight to the decision-
maker for taking the suitable corrective steps needed when the performance of
a system falls above, between and below these lines.Consider a situation in which a given FMS is assumed to have a real-time
capability and a decision-maker operates it in real-time control environment. Now,
due to any reason, if this real-time capability is not achieved than the control of
an FMS will result in a reactive control. Figure 3 also shows the performance
of a given FMS under reactive control in which reaction time is 2 units of time.
The performance deterioration is much faster and higher in reactive control, even
with a very small reaction time of 2 units. One can infer that a decision-maker
should be twice as sure about the real-time capability of a given FMS before
implementing a real-time control environment; otherwise it may result in a reactive
control, which may have far more deteriorating impacts on FMS performance
as compared with the expected improvements through real-time control.The above discussion indicates that RP size has an influence on RF and the
control strategy. It suggests that the RF, control strategies and RP size (a decision-
and-information system’s automation level) should not be viewed individually
as it may lead to erroneous decisions. The combined review of these should
be done. These factors are further analysed under Taguchi’s method to identify
the contribution of each factor in the system’s performance when their levels are
changed. This helps the decision-maker know the relative percentage contribution
of each factor at different levels. The decision-maker would like to have a close eye
on the critical factors to improve the performance.
Comparative performance analysis of an FMS 19
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6.2 Analysis under Taguchi’s method
Tables 5 and 6 show the ANOVA results of experiments conducted under Taguchi’s
method with RF levels of 0 and 1, and 0 and 4, respectively. They indicate the
values of ‘F ’ and ‘%P’ (relative percentage contributions) of different factors and
there are interactions studied. The significant effects at a 5% significance level
are indicated in bold. All the major effects of parameters are significant except for
a few interactions. The percentage errors are also below 15%, which indicate that
all the major contributing factors have been considered in the study for a given
objective. Table 5 shows that the relative percentage contributions of RF and
sequencing rule decreases with an increase in RP size while the relative contributions
of RP and its interaction with RF increases. When RP is less than 35 units of time,
the maximum%P is for RF, then the sequencing rule, and then for the interaction
between RF and RP. This indicates that the benefits of having RF are present
when the RP size is less than 35 units of time. Further, as a contribution of the
sequencing rule is also present, the performance of a given FMS can be managed
by a superior sequencing rule. The presence of an interaction between RF and RP
indicates that the performance of RF is influenced by RP size. As the contribution
of RF decreases with an increase in the contribution of this interaction, it implies
that the interaction between RF and RP has a negative impact on the performance
of RF. A decision-maker would like to keep this interaction as small as possible
to extract the advantages of RF. When the RP time interval is increased to RP¼ 50,
the relative contributions of RF and the sequencing rule are decreased to almost
negligible, while relative contributions of RP and its interaction with RF are
increased to maximum. The objective of RP size is to improve the contributions
of control strategy and RF for improving the performance of a given FMS and
not to reduce the contributions of RF and the control strategy. The size of RP
Table 5. Results of ANOVA under Taguchi’s method for four sets of experiments withdifferent review-period levels and routing flexibility level, RF¼ 0 and 1.
Factors/
Review-periodlevels: RP¼ 0
and 10 RP¼ 0 and 20 RP¼ 0 and 35 RP¼ 0 and 50
interactions F %P F %P F %P F %P
RF 384.76 35.29 265.94 26.33 2074.80 19.13 178.32 12.80SR 290.16 26.59 157.59 15.56 2029.50 18.71 119.06 8.52
DR 70.01 6.35 129.25 12.75 1024.00 9.44 94.58 6.76
RP – – 35.81 3.46 396.01 3.64 457.50 32.96
RF� SR 26.386 2.33 6.36 0.53 430.56 3.96 9.43 0.61RF�DR 70.01 6.35 129.25 12.75 1024.00 9.44 94.58 6.76
RF�RP 150.42 13.74 153.37 15.14 2275.29 20.98 386.31 27.82
SR�DR 9.52 0.78 13.07 1.20 184.96 1.70 – –SR�RP 3.89 0.27 – – – – 21.91 1.51
DR�RP 2.72 0.16 28.33 2.72 152.52 1.40 7.67 0.48Error 15.731 8.13 17.25 9.56 249.25 11.59 2.937 1.78
–, Pooled with error (significant contributions at the 5% level are indicated in bold).
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that can decrease the contributions of RF and the control strategy may deterioratethe performance of a given FMS. At this point, the decision-maker can reacha trade-off between the loss in performance of a given FMS by increasing RP sizeand gains through reduced investment in the decision-and-information system.
Table 6 shows that when the RF level is increased to RF¼ 4, the relativecontribution of the RF has also increased when RP size is small, i.e. RP¼ 10.The relative percentage contribution of RF is increased when the RF level isincreased from 1 to 4 with RP¼ 10, while the relative contribution of sequencingis decreased. This implies that the RF has more dominance on the performance ofa given FMS as compared with a sequencing rule. An increase in RF level maybe a better option for improving performance as compared with changing thesequencing rules studied. Further, as the contribution of the RF is increased,there may be an advantage of increasing RF at this RP size of 10. The relativecontribution of interaction between RF and the sequencing rule is increased, whichshows that a superior control strategy may help in getting the benefits of RF.
When the RP size is increased to 35 and 50, the relative contribution of RFhas almost vanished. This indicates that there may be no advantage of havingan RF level, RF¼ 4, at this size of RP. On comparing tables 5 and 6, the lowerRF levels may be beneficial when RP size is higher, while the same is not possibleat higher RF levels. The size of RP should be such that the major relativecontributions of RF and the control strategy are always present. It also showsthat the increase in RF level increases its interactions with the sequencing ruleand RP. This means that the performance of RF at higher levels is more depen-dent on the control strategy and decision-and-information system. It may beimportant that a decision-maker should have a proper decision-and-informationsystem capable of delivering outputs within the specific time limits and a suitablecontrol strategy at higher levels of RF.
Table 6. Results of ANOVA under Taguchi’s method for four sets of experiments withdifferent review-period levels and routing flexibility level, RF¼ 0 and 4.
Factors/
Review-periodlevels: RP¼ 0
and 10 RP¼ 0 and 20 RP¼ 0 and 35 RP¼ 0 and 50
interactions F %P F %P F %P F %P
RF 644.21 46.92 37.30 13.18 15750.25 1.70 62.96 0.16SR 217.18 15.77 28.06 9.82 71378.00 7.70 2011.00 5.20
DR 43.47 3.10 13.54 4.55 5801.36 0.63 169.67 0.44
RP 69.25 4.98 65.05 23.25 329476.00 35.56 17938.62 46.41
RF� SR 78.12 5.63 4.38 1.23 2288.02 0.25 591.69 1.53RF�DR 43.47 3.10 13.54 4.55 5801.36 0.63 169.67 0.44
RF�RP 221.95 16.20 88.82 31.88 444444.44 47.97 17263.25 44.67
SR�DR – – – – 272.25 0.03 9.63 0.02
SR�RP 25.85 1.81 5.50 1.64 15047.11 1.62 253.10 0.65DR�RP 7.12 0.45 4.69 1.34 – – – –Error 3.82 2.02 2.73 8.56 7242.58 3.91 35.25 0.48
–, Pooled with error (significant contribution at the 5% level are indicated in bold).
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Taguchi’s method can help a decision-maker identify the RP size that doesnot adversely affect the relative contributions of RF and the control strategy.The decision-maker can also identify a suitable level of flexibility and a controlstrategy for a given RP size, i.e. for a give decision-and-information system.
7. Conclusions
This paper attempted to outline a generic approach for decision-makers/practitionersto study a given FMS for its decision-and-information system with respect toRF levels present in the system and operating control strategies. The proposedapproach provides an opportunity to the practitioner to have a combined insighton the operating parameters of an FMS. The decision-and-information systemof an FMS has been mostly assumed to be in real-time and it has not attractedmuch attention when analysing flexibilities. This paper identifies and analyses theimpact of a decision-and-information system in combination with physical andoperating factors of a given FMS.
When a given FMS has a low RF level, then it may not be necessary to havethe cost-intensive, information-overloaded and highly automated decision support
system for real-time control. Even at higher flexibility levels, an RP-based control
with a smaller RP size and superior control strategy can be beneficially implemented.
The results indicate that the performance of an FMS is highly dependent on
the flexibility level present, RP size (decision-and-information system automation
level) and control strategy enforced. As the flexibility of a system increases, its
interactions with the decision-and-information system and control strategy also
increases, i.e. the impacts of these are more critical to an understanding/study
at higher RF levels. The proposed approach can help a decision-maker identify
a suitable decision-and-information system and a control strategy to have the
advantages of higher RF levels. An integrated analysis of these parameters of
an FMS, i.e. flexibility levels, control strategies and decision-and-information
system, should be performed to obtain the real performance of a given FMS.
The approach can help a decision-maker reach a trade-off between the cost of
real-time control and RP size so that the relative percentage contributions of
flexibility, control strategy and their interactions are present to improve the
performance of a given FMS. If the relative percentage contributions of RF and
control strategy vanish and the RP (decision-and-information system) becomes the
major relative contributor in the FMS performance, then the performance of a
system may deteriorate. This may help the decision-maker identify a cost-effective,
non-real-time, decision support-oriented, RP-based control of an FMS.This study has focused on the comparative study of control strategies. It may
be interesting to identify a look-ahead control strategy that can perform betterunder RP-based control of an FMS. In future studies, attempt can be made withstochastic variable and different layouts for more generalizations of the results.It may be also fruitful to study the impact of parameters that are not in controlof a decision-maker such as machine breakdown under RP control.
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