A study on the lot production management in a thin-film-transistor liquid-crystal display fab

Journal of Manufacturing Systems 40 (2016) 9–25

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Journal of Manufacturing Systems journal homepage: www.elsevier.com/locate/jmansys

Technical Paper

A study on the lot production management in a thin-film-transistor liquid-crystal display fab Ying-Chin Ho a,∗ , Jian-Wei Lin a , Hao-Cheng Liu a , Yuehwern Yih b a b

Institute of Industrial Management, National Central University, Chung-Li 32001, Taiwan School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA

a r t i c l e

i n f o

Article history: Received 25 September 2015 Received in revised form 5 February 2016 Accepted 15 March 2016 Keywords: AGV Lot production management Lot selection Photo bay selection

a b s t r a c t Because of their higher processing priority, hot lots often interrupt the production of regular lots in a Thin-Film-Transistor Liquid-Crystal Display (TFT-LCD) fab. As a result, how to manage the production of hot lots so that their production demand can be met and their negative effects on regular lots minimized is a very important issue. In this paper, we identity three problems – the lot selection of interbay AGVs, the lot selection of intrabay machines, and the photo bay selection of lots – that can affect the production of hot lots and regular lots and propose methods for them. A fuzzy-based dynamic bidding (FBDB) method is proposed for the first two problems. The bidding functions in this FBDB method consider several attributes of the current system to obtain the true values of bids. An earliest possible time (EPT) method that also considers several attributes of the current system is proposed for the third problem. These two methods are compared with methods used by a Taiwanese TFT-LCD fab through computer simulations. The effects of hot lot ratios on the performance of these methods are also analyzed. Six performance measures are adopted to measure the throughput and tardiness performance of all lots, regular lots and hot lots. The simulation results show applying the FBDB and EPT methods to the three problems studied here can result in very good results in all performance measures. © 2016 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

1. Introduction In a Thin-Film-Transistor Liquid-Crystal Display (TFT-LCD) fab, some lots have higher processing priority than regular lots. These lots are known as hot lots or priority lots. They are commonly seen in semiconductor and TFT-LCD fabs and appear for different reasons. For example, they can be lots for developing new processes, pilot lots for new products, sample lots, and lots for urgent orders. Hot lots often incur product changes at machines. A product change does not incur extra cost to machines in non-photo bays since the respective manufacturing recipe can be downloaded automatically. However, a product change will incur extra cost for machines in photo bays due to photomask change on steppers. A product change on a stepper requires at least one inspection. For example, in a Taiwanese 5.5 generation TFT-LCD plant (the case company of this study), a product change requires a photomask change, which in turn requires at least 30 min for the installation and inspection of the specific photomask. If the photomask fails inspection, a cleaning procedure is performed. Subsequently, another inspection will

∗ Corresponding author. Tel.: +886 34204305; fax: +886 34258197. E-mail address: [email protected] (Y.-C. Ho).

be needed. In total, this process takes 25 min. Also, the greater the frequency of the photomask change, the greater the amount of particles generated. As a result, the possibility of failing the inspection will increase and more cleaning procedures will be needed. It is apparent photomask changes not only interrupt the production of regular lots, but also pollute the environment and incur non-valueadded cleaning and inspection time. Much precious production time can be wasted due to photomask changes. Despite the disadvantages associated with them, hot lots cannot be avoided. As a result, how to manage the production of hot lots so that their demands can be met and their negative effects on the production of regular lots can be minimized has become an important issue. After observing the case company’s production, we identity three problems that can affect the production of hot lots and regular lots in a TFT-LCD fab. These problems are the lot selection of interbay Automated Guided Vehicles (AGVs), the lot selection of intrabay machines, and the photo bay selection of lots. Studying these problems and developing methods that can outperform the methods used by the case company in several important production performance measures (for all lots, hot lots and regular lots) are the purpose of this study. The remainder of this paper is organized as follows. A review of previous studies that are relevant to this study is presented in Section 2. In Section 3, we introduce the problem

http://dx.doi.org/10.1016/j.jmsy.2016.03.002 0278-6125/© 2016 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

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environment and define the three problems studied here. The proposed fuzzy-based dynamic bidding (FBDB) method for the first two problems and the proposed earliest possible time (EPT) method for the third problem are presented in Section 4. The methods used by the case company (i.e., the TFT-LCD fab this study is modeled after) are presented in Section 5. In Section 6, the proposed methods are compared with methods used by the case company through computer simulations and the simulation results are discussed. Finally, we summarize and conclude this study in Section 7. Future research possibilities are also presented in this section. 2. Literature review In this section, we review previous studies that are relevant to ours. There are three main processes – array, cell, and module – in TFT-LCD production. In this study, we focus on the production in the array process. The TFT-LCD array process is similar to the production process of semiconductors. Its layout is also similar to that of a semiconductor fab. Unsurprisingly, they both have hot lot problems. Because of these similarities, previous studies on the semiconductor production will also be reviewed. Furthermore, since fuzzy-based bidding approaches are used to solve two of the three problems studied here, previous studies with similar or related solution procedures will also be reviewed. Jang et al. [1] presented a look-ahead policy for dispatching AGVs and determining part routing in semiconductor and LCD production bays using information on the future state of systems. Their computer simulation results showed their proposed policy reduced AGV requirements and flow time of parts. Lee and Lee [2] suggested three types of control policies (i.e., push, push-pull and pull) for re-entrant processes that are commonly seen in semiconductor and TFT-LCD fabs. Their computer experiments showed the pull policy had stable throughput performance, while the push policy outperformed the other two policies in both throughput and cycle time performance. Yang et al. [3] proposed a lean-pull strategy that combines buffers with CONWIP (CONstant work-in-process) and applied it in a TFT-LCD manufacturing plant. The buffer sizes and CONWIP levels are the decision variables solved by simulation optimization. Park et al. [4] developed a simulation-based Daily Planning and Scheduling (DPS) system for managing TFTLCD production. They showed the DPS system performed well in real manufacturing settings. Yang and Lu [5] proposed a hybrid dynamic pre-emptive and competitive neural-network approach to solve a multi-objective dispatching problem in a TFT-LCD manufacturing plant. Three performance criteria – cycle time, slack time and throughput – are considered. Shin and Kang [6] developed a scheduling method for the parallel machine scheduling problem with rework probabilities, sequence-dependent setup times and due dates in the module process of TFT-LCD manufacturing. Dabbas and Fowler [7] proposed a dispatching approach that combined four dispatching criteria (i.e., critical ratio, throughput, flow control and line balance) into one rule and tried to simulatneously optimize several objectives. Dabbas et al. [8] validated the approach proposed by Dabbas and Fowler [7] using two fab models with different complexity levels. Min and Yih [9] indicated that no single dispatching strategy can outperform others in all circumstances. They proposed a scheduler that was able to select dispatching rules according to the situation of a semiconductor wafer fabrication system. Tyan et al. [10] presented an Integrated Tool and Vehicle (ITV) dispatching strategy that considered multiple performance measures in a fully automated fab environment. Their simulation results showed the ITV dispatching strategy was superior to a static dispatching rule in on-time delivery. Lin et al. [11] proposed a hybrid push/pull dispatching rule for a semiconductor fab. Their results showed the proposed rule performed well in reducing the WIP and cycle time. Huang et al. [12] proposed a two-phase algorithm for

solving the lot dispatching and vehicle dispatching in a 300 mm wafer fab. The first phase is the offline optimization phase, while the second phase is the online dispatching phase. Kim et al. [13] considers the vehicle dispatching problem in large-scale OHT (Overhead Hoist Transport) systems of semiconductor fabrication lines. They proposed a Hungarian algorithm based OHT reassignment that attempts to take advantage of simultaneous vehicle reassignment based on the up-to-date system status. Li et al. [14] proposed an adaptive dispatching rule (ADR) with adjustable weighting parameters that take into account real-time running state information in a wafer fab. Their simulation results show that ADR with changing parameters outperforms the conventional dispatching rule and ADR with constant parameters. Chung [15] developed an analytical method to estimate the arrival times of transportation jobs performed by automated material handling systems (AMHSs) in LCD fab operations. The estimated transportation time information was used to improve the performance of automated material handling systems. Montoya-Torres [16] provided a review of important literature on various aspects of factory design, such as facility layout, automated material handling systems (AMHS) design and AMHS operational issues for wafer fabs. Managing hot lots is an important issue in TFT-LCD and semiconductor manufacturing, since the presence of hot lots can affect the cycle time and throughput of regular lots. Ehteshami et al. [17] studied the effect of hot lots on the cycle time of regular lots in a semiconductor fab. Their simulation results showed as the proportion of hot lots in the work-in-process increased, the average cycle time and the corresponding standard deviation of regular lots increased as well. Narahari and Khan [18] proposed an efficient analytical model for re-entrant lines. They used an approximate analysis methodology to predict the performance of a semiconductor manufacturing line in the presence of hot lots. The results of their study showed hot lots could affect the performance of regular lots. DeJong and Wu [19] pointed out it was not easy to quantify the effects of hot lots in semiconductor production systems as many factors were involved. They developed a model to investigate the behaviors of hot lots and quantified their effects on the semiconductor production system. Schmidt [20] showed how substantial cycle time benefits for super-hot and hot lots could be achieved with a single wafer toolset and smaller lot sizes. Crist and Uzsoy [21] explored the impacts of several different policies for allocating resources to production and engineering lots on the shop floor. They used a simulation model of a scaled-down wafer fabrication facility to examine the effects of different prioritization policies on the trade-off between the cycle times of engineering and production lots. Wang and Chen [22] proposed a heuristic preemptive dispatching rule to ensure that the production of higher-priority lots in a 300 mm wafer fab will not be hindered by its automated material handling systems. In this paragraph, previous studies that have adopted fuzzy-based, bidding-based or multi-agent-based approaches are reviewed. Lin and Solberg [23] presented a generic bidding-based framework for controlling the work flow in computer controlled manufacturing systems. Kim et al. [24] proposed a new dispatching rule that was based on the fuzzy multi-criteria decision-making method. Their proposed rule utilized relevant information such as elapsed time of move request, incoming and outgoing buffer status and AGV traveling distance. Kim et al. [25] proposed a bidding-based framework for scheduling and shop floor control in computer-controlled manufacturing systems in which every agent acts like an independent profit maker. Hwang and Kim [26] proposed an AGV dispatching algorithm that was based on a bidding concept. Their dispatching algorithm utilized the work-in-process information in incoming and outgoing buffers of a machine center and the travel time information of an AGV through bidding functions. Lee et al. [27] proposed a fuzzy adaptive scheduling method

Y.-C. Ho et al. / Journal of Manufacturing Systems 40 (2016) 9–25

and an automated knowledge acquisition method to acquire knowledge and continuously update it. Tan and Tang [28] developed a fuzzy decision-making system for dispatching AGVs in a flexible manufacturing environment. Berman and Edan [29] developed a control methodology for the decentralized autonomous AGV control in a computer-integrated manufacturing environment. Kim et al. [30] proposed a dispatching method using a bidding concept which looked into the future for an efficient assignment of delivery tasks to vehicles. Ho and Liao [31] proposed a fuzzy bidding-based distributed control strategy for the vehicle dispatching and control problem in a manufacturing system with multiple-load AGVs, Just-In-Time (JIT) production, and alternative routings. Wong et al. [32] developed an agent-based approach for the dynamic integration of process planning and scheduling functions. Two Multi-Agent Systems (MAS) architectures were also evaluated. Li et al. [33] proposed a multi-agent architecture of an agile manufacturing system and a hybrid strategy for shop floor scheduling by combining a fuzzy programming with a fuzzy contract net protocol. Erola et al. [34] proposed a multi-agent based scheduling approach for automated guided vehicles and machines within a manufacturing system. Negotiation/bidding mechanisms are used in their multiagent based approach. Wu et al. [35] proposed a fuzzy logical-based and hybrid dispatching method for the interbay material handling system in a 300 mm wafer fab. In their proposed method, various system parameters are considered simultaneously and the weight coefficients of system parameters are adjusted adaptively by Takagi-Sugeno fuzzy logic-based method. Lu and Liu [36] proposed a dynamic dispatching strategy for a flexible manufacturing system based on fuzzy logic. They identified variables that could affect the system performance and established fuzzy membership functions and fuzzy inference rules based on the simulation data.

3. Problem environment and problem definition The problem environment is modeled after the case company, which is a 5.5 generation TFT-LCD fab in Taiwan. A typical layout of the array process in a TFT-LCD fab is shown in Fig. 1. The following describes the problem environment.

• There are several bays in the TFT-LCD fab. Each bay has an input stocker for incoming lots and an output stocker for outgoing lots. • Machines in the same bay are identical. • AGVs are used for both interbay and intrabay transportation. All AGVs are single-load vehicles. The guide paths for them are all unidirectional. • Interbay AGVs are the bottleneck in the interbay material handling system. In other words, lots at the output stockers often have to wait for their turn to have the service of interbay AGVs. • The bottleneck within a bay is caused by intrabay machines, not intrabay AGVs. This is because the processing time on machines is much greater than the pickup/delivery time of intrabay AGVs. This implies a lot does not have to wait for a long time to have an intrabay AGV dispatched to serve it. • Photo bays are busier than non-photo bays. Lots often have to wait for the service of photo bays. With the problem environment described above, three problems that can affect the production of hot lots and regular lots in a TFT-LCD fab are identified. These three problems are defined and explained as follows. • The lot selection of interbay AGVs This problem occurs when an interbay AGV has just completed its current task and needs to decide which lot (among all the lots that are waiting at output stockers) it should serve next. Since interbay AGVs are the bottleneck in the interbay material handling system, determining which lot has the highest priority to be served by interbay AGVs is an important decision. • The lot selection of intrabay machines. This problem occurs when a machine has just completed its current job (i.e., a lot) and needs to decide which lot (among all the lots that are currently waiting at the input stocker of the bay that the machine belongs to) it should process next. Since machines are the bottleneck in each bay, determining which lot has the highest priority to be served by machines is critical. • The photo bay selection of lots This problem occurs when a lot, L, has just completed its process at a non-photo bay and its next process will be performed

Intrabay guidepath direction (unidirectional)

Intrabay AGV (single-load) Machine

Machine

Machine

Machine

Machine

Machine

Machine

Machine

Interbay AGV Machine (single-load)

Machine

Machine

Machine

Interbay guidepath direction (unidirectional)

11

Machine

Machine

Machine

Machine

Machine

Machine

Machine

Output stocker

Input stocker

Output stocker

Input stocker

Output stocker

Input stocker

Output stocker

Input stocker

Output stocker

Input stocker

Output stocker

Input stocker

Output stocker

Input stocker

Output stocker

Input stocker

Machine Machine Machine Machine Machine Machine Machine

Machine

Machine

Machine

Machine

Machine

Machine

Machine

Machine

Machine

Machine

Machine

Machine

Machine

Machine

Machine

Machine

Machine

Machine

Wet 3

Machine

Wet 3

Machine

Machine

Machine

Machine

Machine

Machine

Machine

Fig. 1. A typical layout of a TFT-LCD manufacturing system.

A bay Intrabay guidepath loop Interbay guidepath loop

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Y.-C. Ho et al. / Journal of Manufacturing Systems 40 (2016) 9–25

An interbay AGV, V, has just completed a task.

Are there lots waiting for vehicle service at output stockers?

no

The interbay AGV will wait until there is at least one lot waiting for vehicle service at output stockers.

yes Use the interbay AGV bidding function to determine the bid value of every bidder, i.e. every lot that is waiting for vehicle service at the output stockers.

The interbay AGV selects the lot with the greatest bid value as the lot it will serve next. Let L* stand for the selected lot.

Dispatch the interbay AGV to pick up and deliver L*. Fig. 2. The flow chart of an interbay AGV bidding session.

in a photo bay. Photo bays are often busier than non-photo bays in a TFT-LCD fab. Thus, if there are more than one photo bay in the system, the decision of selecting the photo bay that L should visit next can affect not only the production of L, but also the production of other lots. 4. The proposed methods Since none of the studies reviewed in Section 2 have the same environmental assumptions and problems as this study has, their methods and approaches cannot be used. New methods are developed specifically for the aforementioned problems. These newly developed methods are presented as follows. 4.1. The proposed FBDB method for the lot selection of interbay AGVs The proposed method for the lot selection of interbay AGVs is an FBDB approach which considers several attributes of the current system. Fig. 2 shows the flow chart of the FBDB method. As shown, when an interbay AGV, V, has just completed a task and is

ready for a new task, it will check whether there are lots waiting for vehicle service at output stockers. If the answer is yes, an interbay AGV bidding session will begin right away. If the answer is no, the interbay AGV will wait until there is at least one lot waiting for vehicle service at output stockers to start the interbay AGV bidding session. At the beginning of a bidding session, an interbay AGV bidding function is proposed to determine the bid value of every bidder – every lot that is waiting for the interbay AGV service at the output stockers. The proposed interbay AGV bidding function considers several attributes of the current system. The weights of these attributes are adjusted by a Fuzzy-Based Dynamic Weights Adjustment (FBDWA) scheme proposed by us. As shown in Fig. 2, after the bid value of every lot has been determined, the lot, L* , with the greatest bid value will be selected by the interbay AGV as the lot it will serve next. After that, the interbay AGV bidding session will end and the interbay AGV, V, will be dispatched to perform the pickup and delivery task of L* . Fig. 3 shows the roles of the interbay AGV bidding function and the FBDWA scheme in an interbay AGV bidding session. Table 1 lists the nine attributes considered in the proposed interbay AGV bidding function. The interbay AGV, V, uses the proposed interbay AGV

An interbay AGV Bidding Session Begins Lots waiting for the service of interbay AGVs at output stockers. Lot Lot

Lot Lot

Lot Lot

Lot Lot

Lot Lot

Lot Lot

Lot Lot

Lot Lot

An Interbay AGV Bidding Session Ends Fuzzy-Based Dynamic Weights Adjustment (FBDWA) Scheme

The Interbay AGV Bidding Function

Lot Lot

The lot with the greatest bid value wins the service of the interbay AGV.

Fig. 3. The roles of the interbay AGV bidding function and the FBDWA scheme in an interbay AGV bidding session.

Y.-C. Ho et al. / Journal of Manufacturing Systems 40 (2016) 9–25 Table 1 The attributes that are considered in the interbay AGV bidding function. Attribute

Definition

Value

Si WTi

The sack time of lot i. The amount of time that lot i has been waiting at the output stocker of its current bay. The remaining capacity of the input stocker at lot i’s destination bay. The remaining capacity of the output stocker at lot i’s destination bay. The remaining capacity of the input stocker at lot i’s current bay. The remaining capacity of the output stocker at lot i’s current bay. The distance that the AGV, V, needs to traverse to pick up lot i. The number of machines (i.e., steppers) with the right photomask needed by lot i in lot i’s destination bay. The priority value of lot i. Three different priority values (i.e. 0, 1.9 and 2) are assigned to regular, hot and super hot lots, respectively.

Non-negative & real Non-negative & real

DIRCi DORCi

CIRCi CORCi Di MWNPi

Pi

Non-negative & integer Non-negative & integer

Non-negative & integer Non-negative & integer Non-negative & real Non-negative & integer

0, 1.9, and 2

bidding function to determine the bid value of a lot i (where i ∈ LS and LS = a set that contains all the lots waiting for vehicle service at output stockers). Eq. (1) gives the proposed interbay AGV bidding function. In Table 1, we refer to the bay that lot i is currently at as the current bay of lot i, and the bay the lot i is visiting next as the destination bay of lot i. Please also refer to Table 1 for the description of each attribute. ABV (i) = WL

Smax − Si WTi − WTmin DIRCi + WL + WSTK Smax − Smin WT max − WTmin DICi

+ WSTK

DORCi CIRCi CORCi ILL − Di + WSTK + WSTK + WD DOCi CICi COCi ILL

MWNPi + WMWNP + WH Pi MWNPmax

(1)

where, ABV(i) = the AGV bid value of lot i, WL = the weight for the slack time and waiting time attributes of lot i, WSTK = the weight for the capacity-related attributes of input and output stockers in lot i’s current bay and destination bay, WD = the weight for the interbay AGV’s travel distance,

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WMWNP = the weight for the number of machines (i.e., steppers) with the right photomask needed by lot i in lot i’s destination bay, WH = the weight for the priority value of lot i, ILL = the total length of the inter-bay loop, i.e. the maximum distance that an AGV need to traverse to pick up a lot, LS = the set of lots currently in the output stockers, Si = the slack time of lot i, Smax = max{Si |i  LS}, i.e. the maximum slack time of all lots in LS, Smin = min{Si |i  LS}, i.e. the minimum slack time of all lots in LS, WTi = The amount of time that lot i has been waiting at the output stocker of its current bay, WTmax = max{WTi |i  LS}, i.e. the maximum waiting time of all lots in LS, WTmin = min{WTi |i  LS}, i.e. the minimum waiting time of all lots in LS, DICi = the full capacity of the input stocker at lot i’s destination bay, DIRCi = the remaining capacity of the input stocker at lot i’s destination bay, DOCi = the full capacity of the output stocker at lot i’s destination bay, DORCi = the remaining capacity of the output stocker at lot i’s destination bay, CICi = the full capacity of the input stocker at lot i’s current bay, CIRCi = the remaining capacity of the input stocker at lot i’s current bay, COCi = the full capacity of the output stocker at lot i’s current bay, CORCi = the remaining capacity of the output stocker at lot i’s current bay, PLS = the set of lots that are currently waiting at output stockers and have photo bays as their next destination bays, MWNPi = the number of machines with the right photomask needed by lot i in lot i’s destination bay, MWNPmax = max{MWNPi |i  PLS}, and Pi = the priority value of lot i. There are five weights (i.e., WL , WSTK , WD , WMWNP , WH ) in the proposed interbay AGV bidding function. They are determined with the proposed FBDWA scheme. This scheme uses several attributes of the current system as fuzzy variables to dynamically determine the values of these weights. There are ten fuzzy variables considered by the scheme. Table 2 describes these fuzzy variables. To develop the appropriate membership functions of these fuzzy variables and their respective weights for the problems they are solving, human designers may have to come up with different membership functions, test their performance through preliminary simulation experiments and select the one with the best performance. Figs. 4–8 show the membership functions of

Table 2 The fuzzy variables considered in each weight. Weight WL

WSTK

WD

WMWNP

WH

Fuzzy variables

Meaning

Definition

ALAT

Average lateness of lots

AFT

Average flow time of lots

ALAT = the average lateness of a number of lots that are completed most recently. AFT = the average flow time of a number of lots that are completed most recently. This number is specified by the human designer.

ORIS

The average occupancy ratio of all input stockers

ORIS =

OROS

The average occupancy ratio of all output stockers

OROS =

the total number of lots in output stockers the capacity sum of output stockers

UTDR

The unloaded travel distance ratio of interbay AGVs

UTDR =

the total unloaded travel distance of interbay AGVs the total travel distance of interbay AGVs

BTR

The busy time ratio of interbay AGVs

BTR =

the busy time sum of interbay AGVs the operating time sum of interbay AGVs

PCR

The photomask change ratio

PCR =

the total number of photomask changes made at steppers the total number of lots processed by steppers

LVR

The lot variety ratio in input stockers

LVR =

the number of lot types currently in input stockers the total number of lot types in the system

NHL WIP

The total number of hot lots and super hot lots The number of work-in-process lots

NHL = the number of hot lots + the number of super hot lots WIP = the number of lots that are currently being processes in the system

the total number of lots in input stockers the capacity sum of input stockers

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Y.-C. Ho et al. / Journal of Manufacturing Systems 40 (2016) 9–25

AFT

ALAT ZE

NE

PO

MI

LG

3500

7000

SM 1

1

-200

1800

0

200

0

WL MN

VS

SM

MI

LG

VL

MA

1

0.1

0.25

0.55

0.40

0.70

0.85

1.0

Fig. 4. The membership functions of ALAT, AFT, and WL .

ORIS

OROS SM

ZE

ZE

SM

LG

0.1

0.25

0.4

LG 1

1

0

0.1

0.25

0

0.4

WSTK MN

VS

SM

MI

LG

VL

MA

1

0.1

0.25

0.40

0.55

0.70

0.85

1.0

Fig. 5. The membership functions of ORIS, OROS and WSTK .

BTR

UTDR SM

ZE

LG

1

ZE

SM

LG

0.5

0.6

0.7

1

0

0.05

0.1

0.3

0

WD MN

VS

SM

MI

LG

VL

MA

1

0.1

0.25

0.40

0.55

0.70

Fig. 6. The membership functions of UTDR, BTR and WD .

0.85

1.0

Y.-C. Ho et al. / Journal of Manufacturing Systems 40 (2016) 9–25

PCR

15

LVR SM

ZE

LG

ZE

SM

LG

0.5

0.8

1.0

1

1

0

0.4

0.7

0

1.0

WMWNP MN

VS

SM

MI

LG

VL

MA

1

0.1

0.25

0.40

0.55

0.70

0.85

1.0

Fig. 7. The membership function of PCR, LVR and WMWNP .

NHL

WIP SM

ZE

LG

1

ZE

SM

LG

144

148

1

0

1

3

0

5

140

WH MN

VS

SM

MI

LG

VL

MA

1

0.1

0.25

0.40

0.55

0.70

0.85

1.0

Fig. 8. The membership functions of NHL, WIP, and WH .

these fuzzy variables and their respective weights for the problem tested in the simulation experiments in Section 6. Table 3 summarizes the notations appearing in these figures. As shown in Table 4, 45 inference rules were developed. 4.2. The proposed FBDB method for the lot selection of intrabay machines For this problem, the same FBDB approach proposed above (but with a different bidding function) is adopted. The bidding function here is an intrabay machine bidding function. Fig. 9 shows the flow chart of the FBDB method in an intrabay machine bidding session. As shown, when an intrabay machine M has completed processing a lot L, an intrabay AGV is dispatched to pick up L and deliver it to the output stocker. Let B stands for the bay that M belongs to. Table 3 The meaning of those symbols appearing in Figs. 4–8. Symbol

Meaning

Symbol

Meaning

NE ZE PO SM MI

Negative Zero Positive Small Medium

LG MN VS VL MA

Large Minimum Very Small Very Large Maximum

The intrabay machine M then checks if there are lots waiting at the input stocker of bay B. If the answer is yes, then the intrabay machine bidding session will begin for the machine M. If the answer is no, the intrabay machine M will wait until there is at least one lot needing its service at bay B’s input stocker to start its intrabay machine bidding session. In the intrabay machine bidding session, the intrabay machine M uses the intrabay machine bidding function (see Eq. (2)) to determine the bid value of every bidder – every lot waiting for the service of M at the input stocker of bay B. The lot, L* , with the greatest bid value is then selected as the lot to be processed next by M. After that, an intrabay AGV is dispatched to pick up L* at the input stocker and deliver it to the intrabay machine M. As shown in Eq. (2), several attributes of the current system are considered in the intrabay machine bidding function. The same FBDWA scheme presented above is used here to determine the weights of the attributes considered in the intrabay machine bidding function. Since many notation definitions in Eq. (2) have already been presented, only the new one is shown below. Fig. 10 shows the roles of the intrabay machine bidding function and the FBDWA scheme in an intrabay machine bidding session. MBV (i) = WL

Smax − Si WTi − WTmin + WL + WMWNP PCi + WH Pi Smax − Smin WT max − WTmin (2)

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Y.-C. Ho et al. / Journal of Manufacturing Systems 40 (2016) 9–25 Table 4 Inference rules.

AFT SM MI LG NE MN VS SM ALAT ZE MI LG VL PO LG VL VL (a) Inference rules for WL

OROS ZE SM LG ZE MN SM LG ORIS SM SM MI LG LG MI LG VL (b) Inference rules for WSTK

BTR ZE SM LG ZE MN SM LG UTDR SM VS MI VL LG SM LG VL (c) Inference rules for WD

LVR

PCR

WIP

ZE

SM

LG

ZE

MN

SM

LG

SM

VS

MI

VL

LG

MI

VL

MA

NHL

(d) Inference rules for WMWNP

where, PCi = a photomask change indicator, PCi = 1 if the bay that lot i is currently in is a photo bay and a photomask change on steppers is required for lot i, PCi = 0 if the bay that lot i is currently in is not a photo bay or a photomask change on steppers is not required for lot i. 4.3. The proposed EPT method for the photo bay selection of lots As explained above, this problem occurs when a lot, L, has just completed its process at a non-photo bay and its next process will be performed in a photo bay. Photo bays are often busier than nonphoto bays in a TFT-LCD fab. If there are more than one photo bay in the system, the decision of selecting the photo bay that L should visit next is an important decision, as it can greatly affect not only L’s

ZE

SM

LG

ZE

VS

SM

LG

SM

MI

LG

VL

LG

VL

VL

MA

(e) Inference rules for WH production but also the production of other lots. For this problem, we propose a method that also considers several attributes (e.g., the availability of photomasks, the waiting time for photomasks and the waiting time for machines) of the current system. Fig. 11 shows the flow chart of the proposed method. We refer to the proposed method as EPT method since it is designed to select photo bays that can process lots at the earliest possible time. As shown, the decision of selecting a photo bay for a lot, L, occurs when it has arrived at the output stocker of the bay in which its previous process has just been completed and its next process will be performed in a photo bay. At this point (see step 2 of flow chart in Fig. 11), we identify those photo bays containing machines that can perform the next process of L and collect them into a set BS. Let H stand for the photomask needed by L for its next process. We then check to see if there are any copies of photomask H left in the storage place for photomasks (see step 3). If the answer is no, it means all copies of photomask H have been installed on the

An intrabay machine, M, in a bay, B, has just completed processing a lot and is currently free.

Dispatch an intrabay AGV to pick up L and deliver it to the output stocker of bay B.

Are there lots waiting for the service of M at the input stocker of bay B?

no

The intrabay machine, M, will wait until there is at least one lot waiting for its service at the input stocker of bay B.

yes Use the intrabay machine bidding function to determine the bid value of every bidder, i.e., every lot that is waiting for the service of M at the input stocker of bay B.

The intrabay machine, M, selects the lot with the greatest bid value as the lot it will serve next. Let L* stand for the selected lot.

Dispatch an intrabay AGV in bay B to pick up L* from the input stocker and delivers it to the intrabay machine M. Fig. 9. The flow chart of an intrabay machine bidding session.

Y.-C. Ho et al. / Journal of Manufacturing Systems 40 (2016) 9–25

An Intrabay Machine Bidding Session Begins Lots waiting for Lot Pool machine M’s service at the input stocker. Lot Lot

Lot Lot

Lot Lot

Lot Lot

Lot Lot

Lot Lot

Lot Lot

Lot Lot

17

An Intrabay Machine Bidding Session Ends Fuzzy-Based Dynamic Weights Adjustment (FBDWA) Scheme

Lot Lot

The lot with the greatest bid value wins the service of the machine M.

The Intrabay Machine Bidding Function

Fig. 10. The roles of the intrabay machine bidding function and the FBDWA scheme in an intrabay machine bidding session.

machines in the photo bays of BS (see step 4). In this case, from BS, we identify those photo bays containing machines that are loaded with photomask H and place them in BS (see step 5). After that, Eq. (3) is used to calculate the ETC(K, H) of every photo bay, K, in BS (see step 6). As shown in Eq. (3), ETC(K, H) (where K ∈ BS ) is the expected time needed by the machines (that are in photo bay K and currently loaded with photomask H) to complete all the lots in LS(K, H). LS(K, H) is a set containing lots that are in photo bay K (including those lots in K’s input stocker) and also need photomask H. After that, from BS , we select the photo bay with the smallest ETC value as the photo bay that L should visit next (see step 7). In other words, we are selecting a photo bay that can process L at the earliest possible time.



ETC(K, H) =

Pt

t ∈ LS(K,H)

NM(K, H)

(3)

where, ETC(K, H) = the expected time needed by the machines (that are in photo bay K and currently loaded with photomask H) to complete all the lots in LS(K, H), Pt = the processing time needed by a machine loaded with photomask H to process lot t. If lot t is currently being processed by a machine with loaded photomask H, then Pt is the remaining processing time of lot t on that machine, LS(K, H) = a set containing lots that are in photo bay K (including those lots in K’s input stocker) and also need photomask H, NM(K, H) = the number of machines that are in photo bay K and also installed with photomask H. As shown in Fig. 11, if the answer for the question in step 3 is yes, we further check if we can find any machines (in the bays of BS) that are installed with photomask H (see step 8). If the answer is no, it means no copies of photomask H are currently installed on machines in the photo bays of BS (see step 9). In this case, we use Eq. (4) to calculate the ERT(K) of every photo bay, K, in BS (see step 10). As shown in Eq. (4), ERT(K) (where K ∈ BS) is the expected time needed by the machines (that are in photo bay K) to complete all the lots that are currently in LS (K). LS (K) is a set containing lots that are in photo bay K (including those lots in K’s input stocker) but do not need photomask H. After that, at step 11, from BS, we select the photo bay with the smallest ERT value as the photo bay that L should visit next. The reason for this decision is similar to the

one in step 7 (i.e., we are selecting a photo bay that can process L at the earliest possible time).

 ERT (K) =

Pt

t ∈ LS  (K)

NM(K)

+ EPCT

(4)

where,

ERT(K) = the expected time needed by the machines (that are in photo bay K) to complete all the lots that are currently in LS (K), Pt = the processing time needed by a machine to process lot t. If lot t is currently being processed by a machine in photo bay K, then Pt is the remaining processing time of lot t on that machine, LS (K) = a set which contains lots that are in photo bay K (including those lots in K’s input stocker, but not those in output stocker) and do not need photomask H, NM(K) = the number of machines that are in photo bay K, EPCT = the expected time to perform a photomask change so that photomask H can be installed on a machine.

On the other hand, if the answer for the question in step 8 is yes, it means some copies of photomask H have been installed on the machines in the bays in BS, and some are in the storage place for photomasks. For this case, from BS, we first identify the photo bays containing machines that are installed with photomask H. Let BS stand for the set of these identified photo bays. After that, for every photo bay K in BS , we use Eq. (3) to calculate its ETC(K,H) (see step 13). At step 14, from BS , we select the photo bay with the smallest ETC value. Let BT* stand for the photo bay. At step 15, from BS, we first identify the set of photo bays containing machines that do not have photomask H installed on them. Let BS stand for this set of photo bays. We then use Eq. (4) to calculate the ERT(K) of every photo bay K in BS . After that, we select the photo bay with the smallest ERT value from BS (see step 16). Let BR* stand for the photo bay. At step 17, we compare ETC(BT* , H) with ERT(BR* ). If ETC(BT* , H) > ERT(BR* ), BR* should be chosen as the next photo bay that L should visit; otherwise BT* should be chosen. The logic of steps 17, 18 and 19 is similar to that in steps 7 and 11. In other words, we are selecting a photo bay that can process L at the earliest possible time.

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Y.-C. Ho et al. / Journal of Manufacturing Systems 40 (2016) 9–25

Step 1

• • •

A lot, L, has just completed its process in a bay and has arrived at the bay’s output stocker. The next process of L will be in a photo bay. Let H stand for the photomask needed by L for its next process in the photo bay.

Step 2

• •

Collect the set of photo bays containing machines that can perform the next process of L. Let BS stand for this set of photo bays. Step 4

Step 3

Can we find any copies of photomask H in the storage place for photomasks?

no

yes

Step 5

•

Step 9

No copies of photomask H are currently installed on machines in the photo bays of BS.

no

Step 8

Can we find any machines (in the photo bays of BS) that are currently installed with photomask H?

Step 10

Use Eq. (4) to calculate the ERT of every photo bay in BS.

Step 15

•

• •

From BS, identify those photo bays containing machines that are not installed with photomask H. Let BS''' stand for the set of photo bays identified above. Calculate the ERT of every photo bay in BS'''.

•

Step 7

• •

From BS', select the photo bay with the smallest ETC as the photo bay that L should visit next.

From BS, identify those photo bays containing machines that are installed with photomask H. Let BS'' stand for the set of photo bays identified above. Calculate the ETC of every photo bay in BS''.

Step 14

• •

From BS'', identify the photo bay with the smallest ETC. Let BT* stand for the photo bay and ETC(BT*, H) stand for the ETC of BT*. Step 18

Step 16

•

Use Eq. (3) to calculate the ETC of every photo bay in BS'.

Step 13

•

From BS, identify those photo bays containing machines installed with photomask H. Let BS' stand for the set of photo bays identified above.

Step 6

Some copies of photomask H have been installed on the machines in the bays in BS, and some are in the storage place for photomasks

Step 11

From BS, select the photo bay with the smallest ERT as the photo bay that L should visit next.

•

yes

Step 12

All copies of photomask H are currently installed on the machines in photo bays of BS.

From BS''', identify the photo bay with the smallest ERT. Let BR* stand for the photo bay and ERT(BR*) stand for the ERT of BR*.

Step 17

Is ETC(BT*, H) > ERT(BR*) ? Step 19

yes

BR* is the photo bay that L should visit next.

no

BT* is the photo bay that L should visit next.

Fig. 11. The flow chart of the proposed EPT method for the photo bay selection of lots.

5. The methods used by the case company In this section, we introduce the methods used by the case company for the three problems studied here. The methods used by the case company for the first two problems are both First-Come FirstServed (FCFS) methods. For the third problem, the case company has tried two different methods. One is based on the number of lots in the input stocker of a bay, while the other is based on the number of machines loaded with the required photomask in a bay. In Section 6, simulation experiments will be conducted to compare these methods with the proposed methods. The following describes the methods used by the case company.

• The FCFS-based method used by the case company for the lot selection of interbay AGVs Fig. 12 shows the flow chart of the method adopted by the case company for the lot selection of interbay AGVs. As shown, the method considers hot lots first. If there are hot lots in LS (which is a set containing lots that are waiting for the interbay AGV service at output stockers), an FCFS rule is adopted to select a hot lot that has been waiting for the longest time in the output stockers as the lot that the interbay AGV should serve next. On the other hand, if there are no hot lots in LS, an FCFS rule is used to select a regular lot that has been waiting for the longest time in the output stockers as the lot that the interbay AGV will serve next.

Y.-C. Ho et al. / Journal of Manufacturing Systems 40 (2016) 9–25

19

An interbay AGV, V, has just completed a task.

Are there lots waiting for vehicle service at output stockers?

no

The interbay AGV, V, will wait until there is at least one lot waiting for vehicle service at output stockers.

yes Let LS stand for this set of lots that are waiting for vehicle service at output stockers.

• •

Use the FCFS rule to select a regular lot from LS. Let L* stand for the selected regular lot.

no

Are there any hot lots in LS? yes Let HLS stands for the set of hot lots in LS.

•

Dispatch the interbay AGV, V, to pick up and deliver L*.

•

Use the FCFS rule to select a hot lot from HLS. Let L* stand for the selected hot lot.

Fig. 12. The flow chart of the FCFS-based method adopted by the case company for the lot selection of interbay AGVs.

• The FCFS-based method used by the case company for the lot selection of intrabay machines Fig. 13 shows the flow chart of the method used by the case company for the lot selection of intrabay machines. The method also considers hot lots first. As shown in Fig. 13, if there are hot lots in LS (which is a set containing lots that are waiting for the service of machine M at the input stocker of bay B), an FCFS rule is adopted to select a hot lot that has been waiting for the longest time at the input stocker of bay B as the lot that machine M should serve next. On the other hand, if there are no hot lots in LS, an FCFS rule is used to select a regular lot that has been waiting for the

longest time at the input stocker of bay B as the lot that machine M should serve next. • The SNL (Smallest Number of Lots) method – the first method used by the case company for the photo bay selection of lots Fig. 14 shows the flow chart of the first method adopted by the case company for the photo bay selection of lots. As shown, the lot, L, will select the photo bay that has the smallest number of lots currently waiting at the photo bay’s input stocker. • The LNMP (Largest Number of Machines with right Photomasks) method – the second method used by the case company for the photo bay selection of lots

An intrabay machine, M, in a bay, B, has just completed processing a lot and is currently free.

Dispatch an intrabay AGV to pick up L and deliver it to the output stocker of bay B.

Are there lots waiting for the service of M at the input stocker of bay B?

no

The intrabay machine, M, will wait until there is at least one lot waiting for its service at the input stocker of bay B.

yes Let LS stand for this set of lots.

• •

Use the FCFS rule to select a regular lot from LS. Let L* stand for the selected regular lot.

no

Are there any hot lots in LS? yes Let HLS stand for the set of hot lots in LS.

Dispatch an intrabay AGV in bay B to pick up L* from the input stocker and delivers it to the intrabay machine M.

• •

Use the FCFS rule to select a hot lot from HLS. Let L* stand for the selected hot lot.

Fig. 13. The flow chart of the FCFS-based method adopted by the case company for the lot selection of intrabay machines.

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Y.-C. Ho et al. / Journal of Manufacturing Systems 40 (2016) 9–25

• •

A lot, L, has just completed its process in a bay and is at the bay’s output stocker now. The next process of L will be in a photo bay.

Let BS stand for the set of photo bays containing machines that can perform the next process of L.

From BS, select the photo bay that has the smallest number of lots at its input stocker as the photo bay that L should visit next.

Fig. 14. The flow chart of the SNL method.

•

•

A lot, L, has just completed its process in a bay and is at the bay’s output stocker now. The next process of L will be in a photo bay.

Let BS stand for the set of photo bays containing machines that can perform the next process of L.

For each photo bay, B, in BS, calculate its number of machines installed with the photomasks needed by L’s next process.

From BS, select the photo bay that has the greatest number of machines installed with the photomasks needed by L’s next process as the photo bay that L should visit next.

Fig. 15. The flow chart of the LNMP method.

Bay 1 Thin-Film Bay

Bay 2 Thin-Film Bay CVD 7

CVD 6

CVD 8

CVD 5

PVD 3

CVD 9

CVD 4

PVD 2

CVD 10

CVD 3

PVD 1

CVD 11

CVD 2

CVD 12

CVD 1

PVD 5 PVD 4 PVD 6 PVD 7 PVD 8 PVD 9

Bay 3 Photo Bay Photo 3

Photo 8 Photo 2 Photo 9 Photo 1 Photo Photo 10 10

Input Stocker

Output Stocker

Input Stocker

Output Stocker

Input Stocker

Interbay guide path direction

Input Stocker

Output Stocker

Input Stocker

Output Stocker

Input Stocker

Output Stocker

Dry 1

Dry 8

Dry 10

Dry 15

Dry 2

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Output Station

Dry 13

Dry-Etching Bay Bay 8

Dry 4

Dry-Etching Bay Bay 7

Input Stocker

Input Stocker

Output Stocker

Photo 15 Photo 14

Wet 5

Dry 6 Dry 5

Output Stocker

Photo 11

Photo 12

Wet 3

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Intrabay guide path direction

Wet 1

Wet 2 Dry 11

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Photo 5

Output Stocker

Dry 16

Photo 7

Photo 4

Input Station

Dry 9

Bay 4 Photo Bay

Wet 3

Wet 4

Photo 13

Photo Bay Bay 5

Wet-Etching Bay Bay 6

Fig. 16. The layout of the TFT-LCD fab in the simulation experiments.

Fig. 15 shows the flow chart of the second method used by the case company for the photo bay selection of lots. As shown, the lot, L, will select the photo bay that has the greatest number of machines installed with the photomasks needed by L’s next process. 6. Simulation experiments In order to understand whether the proposed methods can outperform those methods used by the case company, simulation experiments were conducted. The TFT-LCD fab in the simulation experiments is modeled after the case company, without any scaling down of the problem. That is, the number of bays, the number of machines in each bay and the number of lot types are identical to those of the case company, thus the problem environment of the simulation experiments is almost identical to that of the case

company. However, for confidentiality reasons, important data and parameters are masked. Fig. 16 shows the layout of the TFT-LCD fab. As shown in Fig. 16, the input station of the system is located in Bay 1, while the output station of the system is located in Bay 6. In the simulation experiments, there are ten lot types made in one production period. These lots are similar in their operation sequences, but different in their operation time. Table 5 lists the mix ratios of these lots. In the experiments, seven different hot lot ratios were tested. The purpose is to see the performance of the proposed methods and the methods used by the case company under different hot Table 5 The mix ratios of different lot types. Lot type

1

2

3

4

5

6

7

8

9

10

Mix ratio

0.25

0.25

0.15

0.15

0.05

0.05

0.04

0.03

0.02

0.01

Y.-C. Ho et al. / Journal of Manufacturing Systems 40 (2016) 9–25 Table 6 The factors and their levels in the experiments. Factor

Level

21

Table 8 TAL statistics for lot selection methods and their t-test results.

Hot lot ratio

Lot selection method

Photo bay selection method

Lot selection method

Mean

Standard deviation

1% 3% 5% 7%

FBDB FCFS

EPT SNL LNMP

FBDB FCFS

4675.0667 4452.6333

34.12952 18.39931

* **

lot ratios. Table 6 summarizes the factors and their levels in the experiments. There are 24 (4·2·3) combinations of these factors. Simulation experiments are conducted for each combination. The number of replications in each simulation is 20. The warm-up time is 5 days and the simulation time is 125 days. The number of replications and the warm-up time were determined using the method described in Law and Kelton [37]. The simulation software package is Arena [38]. Finally, values for six performance measures, namely the throughput of all lots (TAL), the throughput of regular lots (TRL), the throughput of hot lots (THL), the mean tardiness of all lots (MTAL), the mean tardiness of regular lots (MTRL), and the mean tardiness of hot lots (MTHL), were collected from the experiments and analyzed afterwards. In the following subsections, we present the performance of our proposed methods and the methods used by the case company in the aforementioned performance measures. The performance of different hot lot ratios and their effects on the performance of the methods studied here are also analyzed. 6.1. The performance of hot lot ratios

89.809

0.000**

Significance at the 10 percent level. Significant at 5 percent or below.

Table 9 TRL statistics for lot selection methods and their t-test results. Lot selection method

Mean

Standard deviation

FBDB FCFS

4485.8167 4271.5958

129.58327 106.82351

* **

t value

p value

83.28

0.000**

Significance at the 10 percent level. Significant at 5 percent or below.

Table 10 THL statistics for lot selection methods and their t-test results. Lot selection method

Mean

Standard deviation

FBDB FCFS

189.2500 181.0375

105.12895 101.17433

* **

t value

p value

24.344

0.000**

Significance at the 10 percent level. Significant at 5 percent or below.

Lot selection method

Mean (min)

FBDB FCFS

3.2849 143.1881

* **

Standard deviation

TAL Performance 7% (4546.375) 5% (4559.7) 3% (4566.5917) 1% (4582.7333) MTAL Performance 1% (56.9275) 3% (67.7258) 5% (77.378) 7% (90.9147)

t value

p value

3.81707 −75.56 30.11143

0.000**

Significance at the 10 percent level. Significant at 5 percent or below.

3%, 5%, and 7% in MTHL performance, the differences between them are very small. From the above discussion on MTHL and MTRL performance, one can easily understand why hot lot ratios 1%, 3%, 5%, and 7% are significantly different (at ˛ = 0.05) from each other in MTAL performance. 6.2. The performance of lot selection methods Tables 8–10 show the TAL, TRL and THL statistics of two lot selection methods (i.e., FBDB and FCFS) and their t-test results. As shown, FBDB is significantly better than FCFS in TAL, TRL and THL performance. Tables 11 and 12 also show that FBDB is significantly better than FCFS in MTAL and MTRL performance. Furthermore, as

Table 7 The Duncan test results for TAL, TRL, THL, MTAL, MTRL and MTHL performance in different hot lot ratios.

Hot Lot Ratio

p value

Table 11 MTAL statistics for lot selection methods and their t-test results.

Table 7 shows the Duncan test results for TAL, TRL, THL, MTAL, MTRL and MTHL performance of different hot lot ratios. As shown, the hot lot ratio has a positive effect on THL. This result is reasonable since the increase of the hot lot ratio means there will be more hot lots in the system. As a result, there will be more hot lots completed by the system (i.e., THL will increase). On the other hand, a greater hot lot ratio also means a smaller regular lot ratio which leads to a smaller TRL. This also explains why the hot lot ratio’s effect on TRL is negative. As shown in Table 7, the effect of hot lot ratios on TAL is negative. This is because as the hot lot ratio increases, the increase of THL cannot compensate the decrease of TRL. As a result, TAL decreases as the hot lot ratio increases. Table 7 also shows that the effect of hot lot ratios on MTRL is positive, meaning the greater the hot lot ratio, the greater the MTRL. Hot lot ratios 1%, 3%, 5%, and 7% are significantly different (at ˛ = 0.05) from each other in MTRL performance. In addition, MTRL increases rapidly as the hot lot ratio increases from 1% to 7%. On the other hand, hot lot ratios 3%, 5%, and 7% are not significantly different (at ˛ = 0.05) from each other in MTHL performance. Furthermore, although the hot lot ratio 1% is significantly worse (at ˛ = 0.05) than hot lot ratios

Hot Lot Ratio

t value

TRL Performance 7% (4225.875) 5% (4327.2917) 3% (4424.775) 1% (4536.8833) MTRL Performance 1% (57.4526) 3% (69.7743) 5% (81.396) 7% (97.5641)

Note: Hot lot ratios connected symbolically are not significant at an ˛ of 0.05.

THL Performance 1% (45.85) 3% (141.8167) 5% (232.4083) 7% (320.5) MTHL Performance 5% (2.9694) 7% (3.4629) 3% (3.7988) 1% (5.3948)

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Y.-C. Ho et al. / Journal of Manufacturing Systems 40 (2016) 9–25

Table 12 MTRL statistics for lot selection methods and their t-test results.

*

Lot selection method

Mean (min)

Standard deviation

FBDB FCFS

3.3292 149.7643

4.04258 34.15531

t value

p value

−69.717

0.000**

Significance at the 10 percent level. ** Significant at 5 percent or below.

Table 13 MTHL statistics for lot selection methods and their t-test results.

*

Lot selection method

Mean (min)

Standard deviation

FBDB FCFS

3.4834 4.3295

2.03485 11.72083

t value

p value

−1.121

0.263**

Significance at the 10 percent level. ** Significant at 5 percent or below.

shown in Table 13, FBDB is better than FCFS in MTHL performance, however the difference between them is not significant (at ˛ = 0.05). We also investigate the performance of FBDB and FCFS when they are paired with different hot lot ratios and different photo bay selection methods. Table 14 shows the Duncan test results for TAL, TRL, THL, MTAL, MTRL and MTHL performance in all possible pairs of lot selection methods and hot lot ratios. As one can see, FBDB outperforms FCFS in TAL, TRL, THL, MTAL and MTRL performance when they are paired with the same hot lot ratio. For example, FBDB-1% is better than FCFS-1% in TAL, TRL, THL, MTAL and MTRL performance. Furthermore, the difference between them is significant (at ˛ = 0.05) in almost all cases. The only exception is the comparison between the THL performance of FBDB-1% and FCFS-1%, in which FBDB-1% is better (but not significantly better) than FCFS-1%. From Table 14, one can also see almost all pairs of lot selection methods and hot lot ratios are connected symbolically in MTHL performance. This result indicates FBDB and FCFS are not very different in their MTHL performance under different hot lot ratios. Table 15 shows the Duncan test results for TAL, TRL, THL, MTAL, MTRL and MTHL performance in all possible pairs of lot selection

Table 14 The Duncan test results for TAL, TRL THL, MTAL, MTRL and MTHL performance in all pairs of lot selection methods and hot lot ratios.

TAL Performance

Pairs of Lot Selection Method and Hot Lot Ratio

FCFS-7% (4445.15) FCFS-3% (4453.4833) FCFS-5% (4453.7667) FCFS-1% (4458.1333) FBDB-7% (4647.6) FBDB-5% (4665.6333) FBDB-3% (4679.7) FBDB-1% (4707.3333) MATL Performance

Pairs of Lot Selection Method and Hot Lot Ratio

FBDB-1% (0.8185) FBDB-3% (1.9481) FBDB-5% (3.5428) FBDB-7% (6.8302) FCFS-1% (113.0365) FCFS-3% (133.5035) FCFS-5% (151.2132) FCFS-7% (174.9992)

TRL Performance FCFS-7% (4131.4) FCFS-5% (4226.0167) FCFS-3% (4315.55) FBDB-7% (4320.35) FCFS-1% (4413.4167) FBDB-5% (4428.5667) FBDB-3% (4534) FBDB-1% (4660.35)

THL Performance FCFS-1% (44.7167) FBDB-1% (46.9833) FCFS-3% (137.9333) FBDB-3% (145.7) FCFS-5% (227.75) FBDB-5% (237.0667) FCFS-7% (313.75) FBDB-7% (327.25)

MTRL Performance FBDB-1% (0.8185) FBDB-3% (1.9481) FBDB-5% (3.5428) FBDB-7% (6.8302) FCFS-1% (113.0365) FCFS-3% (133.5035) FCFS-5% (151.2132) FCFS-7% (174.9992)

MTHL Performance FCFS-5% (2.5756) FBDB-3% (3.0811) FCFS-7% (3.1459) FBDB-5% (3.3632) FBDB-1% (3.7096) FBDB-7% (3.7798) FCFS-3% (4.5165) FCFS-1% (7.08)

Note: Pairs of lot selection methods and hot lot ratios connected symbolically are not significant at an ˛ of 0.05. Table 15 The Duncan test results for TAL, TRL THL, MTAL, MTRL and MTHL performance in all pairs of lot selection methods and photo bay selection methods.

TAL Performance Pairs of Lot Selection Method and Photo Bay Selection Method

FCFS-EPT (4439.8875) FCFS-LNMP (4451.7375) FCFS-SNL (4466.275) FBDB-LNMP (4647.35) FBDB-SNL (4679.175) FBDB-EPT (4698.675) MATL Performance

Pairs of Lot Selection Method and Photo Bay Selection Method

FBDB-EPT (1.6999) FBDB-SNL (1.8908) FBDB-LNMP (6.264) FCFS-SNL (125.7007) FCFS-LNMP (140.5784) FCFS-EPT (163.2852)

TRL Performance FCFS-EPT (4259.2625) FCFS-LNMP (4270.8) FCFS-SNL (4284.725) FBDB-LNMP (4459.0375) FBDB-SNL (4489.7875) FBDB-EPT (4508.625) MTRL Performance FBDB-EPT (1.6752) FBDB-SNL (1.8313) FBDB-LNMP (6.4813) FCFS-SNL (131.5128) FCFS-LNMP (146.9329) FCFS-EPT (170.8471)

THL Performance FCFS-EPT (180.625) FCFS-LNMP (180.9375) FCFS-SNL (181.55) FBDB-LNMP (188.3125) FBDB-SNL (189.3875) FBDB-EPT (190.05) MTHL Performance FCFS-EPT (2.0302) FCFS-SNL (2.7186) FBDB-EPT (2.9361) FBDB-LNMP (3.5022) FBDB-SNL (4.012) FCFS-LNMP (8.2398)

Note: Pairs of lot selection methods and photo bay selection methods connected symbolically are not significant at an ˛ of 0.05.

Y.-C. Ho et al. / Journal of Manufacturing Systems 40 (2016) 9–25

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Table 16 The Duncan test results for TAL, TRL, THL, MTAL, MTRL and MTHL performance in different photo bay selection methods.

TRL Performance

TAL Performance Photo Bay Selection Method

LNMP (4549.5438) EPT (4569.2813) SNL (4572.725)

LNMP (4364.9188) EPT (4383.9438) SNL (4387.2563)

MATL Performance Photo Bay Selection Method

THL Performance LNMP (184.625) EPT (185.3375) SNL (185.4688)

MTRL Performance

SNL (63.7958) LNMP (73.4212) EPT (82.4925)

MTHL Performance

SNL (66.672) LNMP (76.7071) EPT (86.2611)

EPT (2.4831) SNL (3.3653) LNMP (5.871)

Note: Photo bay selection methods connected symbolically are not significant at an ˛ of 0.05.

methods and photo bay selection methods. As shown, FBDB is significantly better (at ˛ = 0.05) than FCFS in TAL, TRL, THL, MTAL and MTRL performance when they are paired with the same photo bay selection method. In other words, FBDB-EPT, FBDB-SNL and FBDBLNMP are significantly better (at ˛ = 0.05) than FCFS-EPT, FCFS-SNL and FCFS-LNMP, respectively, in TAL, TRL, THL, MTAL and MTRL performance. Table 15 also indicates almost all pairs of lot selection methods and photo bay selection methods are connected symbolically in MTHL performance. This result shows FBDB and FCFS are

not very different in their MTHL performance under different photo bay selection methods. 6.3. The performance of photo bay selection methods Table 16 shows the Duncan test results for TAL, TRL, THL, MTAL, MTRL and MTHL performance in different photo bay selection methods. As shown, the rankings of photo bay selection methods in TAL, TRL and THL performance are identical. These performance

Table 17 The Duncan test results for TAL, TRL, THL, MTAL, MTRL and MTHL performance in photo bay selection methods under different hot lot ratios.

TAL Performance Hot Lot Ratio Photo Bay Selection Method

1% LNMP (4560.425) SNL (4591.775) EPT (4596)

3%

5%

LNMP (4549.725) EPT (4574.55) SNL (4575.5)

LNMP (4549.425) EPT (4561.45) SNL (4568.225)

7% LNMP (4538.6) EPT (4545.125) SNL (4555.4)

TRL Performance Hot Lot Ratio Photo Bay Selection Method

1% LNMP (4514.75) SNL (4545.85) EPT (4550.05)

5%

3% LNMP (4408.6) EPT (4432.45) SNL (4433.275)

LNMP (4317.6) EPT (4328.725) SNL (4335.55)

7% LNMP (4218.725) EPT (4224.55) SNL (4234.35)

THL Performance Hot Lot Ratio Photo Bay Selection Method

1% LNMP (45.675) SNL (45.925) EPT (45.95)

3% LNMP (141.125) EPT (142.1) SNL (142.225)

5% LNMP (231.825) SNL (232.675) EPT (232.725)

7% LNMP (319.875) EPT (320.575) SNL (321.05)

MTAL Performance Hot Lot Ratio Photo Bay Selection Method

1% SNL (49.2178) LNMP (56.3274) EPT (65.2373)

3% SNL (58.4068) LNMP (68.5746) EPT (76.196)

5% SNL (68.3789) LNMP (75.3632) EPT (88.392)

7% SNL (79.1795) LNMP (93.4197) EPT (100.1449)

MTRL Performance Hot Lot Ratio Photo Bay Selection Method

1% SNL (49.6788) LNMP (56.8075) EPT (65.8716)

3% SNL (60.1872) LNMP (70.5676) EPT (78.568)

5% SNL (71.9006) LNMP (79.2425) EPT (93.0451)

7% SNL (84.9216) LNMP (100.2108) EPT (107.5598)

MTHL Performance Hot Lot Ratio Photo Bay Selection Method

1% EPT (2.7869) SNL (3.7907) LNMP (9.6067)

3% EPT (2.1785) SNL (2.9465) LNMP (6.2714)

5% EPT (2.2923) SNL (3.1564) LNMP (3.4596)

Note: Photo bay selection methods connected symbolically are not significant at an ˛ of 0.05.

7% EPT (2.6748) SNL (3.5675) LNMP (4.1462)

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Y.-C. Ho et al. / Journal of Manufacturing Systems 40 (2016) 9–25

rankings show SNL has the best TAL, TRL and TAL performance. Although EPT does not have the best TAL, TRL and TAL performance, it is not significantly different (at ˛ = 0.05) from SNL in TRL and THL performance. In addition, EPT and SNL are very close in TAL performance. We are surprised to see EPT has the poorest MATL and MTRL performance. To investigate what caused EPT to perform last in MATL and MTRL and second in TAL, TRL and THL, we look into the performance of all pairs of lot selection methods and photo bay selection methods (see Table 15). From Table 15, we see EPT can achieve optimal TAL, TRL, THL, MTAL and MTRL performance and has a very good MTHL performance when it is paired with FBDB. However, if EPT is paired with FCFS, its TAL, TRL, THL, MTAL and MTRL performance will be the poorest. This finding can explain the performance results for EPT in Table 16. It also indicates the EPT method cannot reach maximum efficiency unless it is paired with the right lot selection method, i.e., the proposed FBDB method. The performance of photo bay selection methods under different hot lot ratios is also studied here. Table 17 shows the Duncan test results for TAL, TRL, THL, MTAL, MTRL and MTHL performance in photo bay selection methods under different hot lot ratios. As shown, the TAL performance rankings of EPT, SNL and LNMP under 3%, 5% and 7% hot lot ratios are identical, with SNL being first, EPT second, and LNMP last. Although their TAL performance ranking under the 1% hot lot ratio (i.e., EPT first, SNL second and LNMP last) is not completely identical to those under the 3%, 5% and 7% hot lot ratios, they are in fact similar. This is because there is no significant difference between the TAL performance using the EPT or SNL methods. These observations allow us to conclude that hot lot ratios do not significantly affect the relative TAL performance of EPT, SNL and LNMP. The same conclusions can also be made on TRL and THL performance of EPT, SNL and LNMP, since observations similar to those seen in TAL performance can be also be found. Finally, we also find the MTAL, MTRL and MTHL performance rankings of EPT, SNL and LNMP are not affected by hot lot ratios. This observation allows us to conclude that hot lot ratios cannot significantly affect the relative MTAL, MTRL and MTHL performance of EPT, SNL and LNMP.

7. Summary, conclusions and future research possibilities In this study, we examined the production management of hot lots and regular lots in a TFT-LCD fab. Because of their higher processing priorities, hot lots often interrupt the production of regular lots. As a result, how to manage the production of hot lots so that their production demand can be met and their negative effects on regular lots can be minimized is a very important issue. We identified three problems (i.e., the lot selection of interbay AGVs, the lot selection of intrabay machines, and the photo bay selection of lots) that can affect the production of hot lots and regular lots and proposed an FBDB method for the first and second problems and an EPT method for the third problem. The FBDB method is a fuzzy-based dynamic bidding method that considers several attributes of the current system in the bidding functions for the first and second problems. The EPT method also considers several attributes of the current system to select photo bays that can process lots at the earliest possible time. The proposed methods are compared with methods used by a TFT-LCD fab in Taiwan through computer simulations. Different hot lot ratios are also considered in computer simulations. Furthermore, six performance measures are used: TAL, TRL, THL, MTAL, MTRL and MTHL. The simulation results indicate that the proposed FBDB method is significantly (at ˛ = 0.05) better than the FCFS method in TAL, TRL, THL, MTAL and MTRL performance. The simulation results also show that the proposed FBDB method is better than the FCFS

method in MTHL performance, but the difference between them is not significant (at ˛ = 0.05). These results allow us to conclude that the proposed FBDB method is indeed a better method than the FCFS method in every performance measure. The simulation results also show the proposed EPT method can result in the best TAL, TRL, THL, MTAL and MTRL performance and a very good MTHL performance when it is paired with the proposed FBDB method. The proposed EPT method cannot realize its benefits if it is coupled with the FCFS method. In other words, applying the proposed FBDB and EPT methods together is the best combination of methods in solving the three problems studied here. Finally, we would like to propose three future research possibilities for this study. The first future research possibility is to adopt a two-way bidding strategy, in which interbay AGVs (or intrabay machines) and lots can evaluate each other. The bidding procedure in the proposed FBDB method is a one-way bidding strategy in which lots bid for interbay AGVs (or intrabay machines), but not vice versa. One problem with one-way bidding is the bidding result may not be beneficial to both sides. With a two-way bidding strategy, determining which lot and which interbay AGV (or intrabay machine) should be matched together is determined by the values they bid for each other – that is bidding results are based on the benefits of both sides. Furthermore, a negotiation scheme can also be developed and incorporated into the strategy. It is likely this two-way bidding strategy with a negotiation scheme can outperform the one-way bidding strategy adopted here. The second future research possibility is to include a lookahead strategy in the proposed method. The production activities in a real-life TFT-LCD fab are very dynamic. For example, hot lots may arrive unexpectedly and machines can break down at any time. The look-ahead strategy can provide us a near-future status of the system. This information can then be used to assist us in selecting the best decisions for the three problems studied here. For example, one can adjust the weights of the bidding functions in the FBDB method to reflect the true values of bids by considering not only the system’s current status (as in the proposed FBDB method), but also its near-future status. Finally, since developing methods that can outperform those used by case company was the purpose of this study, we only compared our proposed methods with those used by the case company. We did not compare the proposed methods with methods proposed by other researchers. It is suggested that in a future study the proposed methods be compared with methods found in literature. Since the methods proposed by other researchers were not developed for problems that are identical to ours, it is very likely that they cannot be directly applied to the problems studied here. Some efforts may have to be taken (e.g., developing appropriate decision flow charts for them) in order to make them work for the problems studied here and to have a fair comparison between them and the proposed methods.

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