Proceedings of the 2002 IEEE International Conference on Robotics & Automation Washington, DC • May 2002
Dynamic OHT Allocation and Dispatching in Large-Scaled 300mm AMHS Management Da-Yin Liao, Member, IEEE, Hsien-Sheng Fu
Abstract--This paper presents a two-phased approach for effective dynamic OHT allocation and dispatching in large.scaled 300ram AMHS management. The OHT dispatching problem is first explored with the help of simulation models of each OHT loop. The best OHT dispatching policy that efficiently controls OHT transports is then selected among several OHT dispatching rules. For each OHT loop, its required number of OHT vehicles can be calculated from the simulation results on the given service requirements to this loop. However, as the total number of OHT vehicles in the fab is limited, the allocation of OHT vehicles for this loop is determined by considering the total requirements on OHT services from all the other loops. Our objective of OHT allocation and dispatching has two folds: (1) to meet the transport requirements of throughput; and (2) to minimize the carrier delivery times. Numerical results based on realistic data from a local 300ram mass production lab demonstrate that this twophased approach performs well both in minimizing the averages and variances of carrier delivery times as well as in achieving the target requirements. Index Terms--300ram AMHS management, OHT allocation and dispatching, discrete-event simulation.
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Figure 1. Configuration of An OHT Loop (Top View)
I. INTRODUCTION and makes possible the automatic tool-to-tool delivery within a 300mm fab. However, it is still very difficult and challenging to optimize and manage OHT operations in such a large-scaled automatic material handling system (AMHS). Figure 3 shows the layout configuration of a large-scaled OHT system. In a large-scaled AMHS, there are hundreds of OHT vehicles running in dozens of OHT loops. Assume that all the OHT vehicles are capable of travelling among all the OHT loops. Although the average (or static) loading on each OHT loop has been optimized in the stage of fab layout design, the transport requirements of different OHT loops are usually various and changing from time to time. For an OHT loop, its transport requirements are dynamic due to the varying distribution of WIP and the fluctuating processing capacity of equipment within the loop. It is therefore needed an effective OHT control and management methodology to cope with the dynamic changes on the requirement of OHT services. A great deal of attention has been paid to the automation of material handling systems in both 300mm interbay and intrabay systems [2], [5], [7]-[10]. Most of them present the design concept, especially on the effective integration of 300mm fab layout and AMHS. Cardarelli et al develop a simulation tool for design and management optimization of automated interDa-Yin Liao is with Departmentof InformationManagement,NationalChi- bay material handling and storage systems for large wafer fabs Nan University,Pull, Nantou545, Taiwan. (e-mail:
[email protected]) [3]. Generalized probability density functions fitted on the obHsien-Sheng Fu is with Fab12 Manufacturing Department, servation on the monthly input-relative probability in a wafer Taiwan Semiconductor Manufacturing Co., Hsinchu 300, Taiwan. fab is used as the scenarios to evaluate the dynamics of interbay (e-mail:
[email protected]) N order to achieve high productivity and eliminate the possible ergonomic hurts due to the heavy weight of 300mm wafers and carriers, a cost-effective 300mm semiconductor lab demands highly automated material transfer operations [ 1], [8]. It has been basic and default in contemporary 200mm fab design to the implementation of interbay material handling and transport for WIP (wafer in process) management. In addition, in 300mm fab operations, the capability of automatic carder delivery within an intrabay is also considered as a must. Among the proposed intrabay transport solutions, the OHT (Overhead Hoist Transport) solution has become a promising technology to realize transportation automation in an intrabay loop, especially for the operation model (like semiconductor foundry) where both automatic and manual carder transfer operations exist in a same time. Figure 1 depicts the configuration of an OHT loop and Figure 2 demonstrates the environment where both automatic and human operations exist. Recently, there have been some advanced diverging and converging mechanisms proposed and developed [5] to allow an OHT to travel from one intrabay to the other ones. This hardware breakthrough extends the capability and scope of OHT applications from one single intrabay loop to the fab-wide scale
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fab is limited, the optimal number of OHT vehicles for each loop is then determined by considering the total requirements on OHT services from all the other loops. Our objective of OHT allocation and dispatching has two folds: (1) to meet the transport requirements of throughput; and (2) to minimize the cartier delivery times. This paper is organized as follows. Section II describes the OHT allocation and dispatching problem in large-scaled 300mm AMHS management. Section III proposes the solution methodology to optimize the throughput and delivery times. Simulation studies based on realistic data from a local 300ram production lab are conducted in Section IV. In Section V, concluding remarks are made with a summary and directions for future research.
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II. OHT A L L O C A T I O N AND DISPATCHING PROBLEM ". Figure 2. Automatic and Manual Operations Environment with OHT Services (Side View)
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Consider a 300mm IC fab where there are totally ILl OHT loops and IVI OHT vehicles running on these ILl OHT loops. To simplify the description and without loss of generality, assume that all of the IVl OHT vehicles are able to and capable of travelling among all the ILl OHT loops in the fab. Define v = (vl,v2,-.-,Vlt, i) as the states of OHT vehicles in the fab, where vt is the non-negative integer number of OHT vehicles in OHT;loop 1 and l E {1, 2 , . . . , ILl}. Let v ° denotes the given initial states of OHT vehicles in the fab and v ° = (vt°, v°, ..- , V~LI). Assume that there are no addition or replacement of OHT vehicles in the fab during the time horizon, [0, T]. That is, the total number of OHT vehicles is unchanged according to the following conservation rules: I = IV]
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v ° + v ° + .... +V~Li=lVI,
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Figure 3. Example of Layout Configuration of Large-Scaled OHT Systems
material handling and storage systems. Fu and Liao [4] have developed an effective OHT dispatching policy to reduce carrier delivery times while achieving high throughput requirements in an interbay material handling system. We address, in this paper, on the OHT allocation and dispatching problem in large-scaled 300mm AMHS management. Observing from the characteristics of the OHT allocation and dispatching problem, we propose a two-phased approach to solve the problem. At first, simulation models for each OHT loop are built. Without considering the OHT service requirements, the best OHT dispatching policy that efficiently control OHT transports is then selected among several OHT dispatching rules. For a given set of service requirements to an OHT loop, the required number of OHT vehicles in the loop can be calculated from the simulation results on this loop. However, as the total number of OHT vehicles in the
where vl, v 2 , . . . , vii i are decision variables of non-negative integers and v °, v°, ..- , v~/,] are given. Furthermore, as the frequency of OHT allocation is usually in a shift or daily basis, which is much longer than the relocation time of an OHT vehicle, the time for an OHT vehicle to relocate among OHT loops can thus be neglected. The answer to the problem of which OHT vehicles in a loop to be re-located from one OHT loop to one other loop is not considered in this paper and is assumed to be neglected. A move is defined as an accomplishment of transferring a cartier from its current location (the departure) to the destination. For an OHT loop, the move (or throughput) of the loop is determined by both the number of OHT vehicles in the loop as well as the OHT dispatching policy used in the loop. Define m - (ml, m 2 , . - . , m i l I) as the moves of OHT vehicles during [0, T], where rat is the move of OHT vehicles in OHT loop 1 during [0, T] and 1 E { 1 , 2 , - . . , [L]). Assume that for each OHT loop, the OHT dispatching policy for this loop remains unchanged during [0, T]. Define p - ( P l , P 2 , " - , P i L I ) as the OHT dispatching policies used during [0, T]. where pt is the OHT dispatching policy applied in OHT 1 during T and I E (1, 2 , . . . , ILl}. There exist relationship functions, @ and
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III. SOLUTION M E T H O D O L O G Y
r , among the number of OHT vehicles, v, the moves, rn, and the OHT dispatching policies, p. That is, m = @(v, p)
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v = r ( m , p),
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where @ ---(O~, @9.,-.., @ILl)andr = ( E l , F 2 , . . . , F I L l ) . Define a transport job as a macro of transfer commands to contribute to a move. A transport job contains that (i) a request for an empty OHT is initialized for carrier transfer from the departure to the destination, (ii) an empty OHT arrives and picks up the carrier at the departure, (iii) the OHT moves the carrier from the departure to the destination, and (iv) the OHT delivers the carrier at the destination. Carrier delivery time is then defined as the time to complete a transport job. Observing from the research results of [4], for an OHT loop, its carrier delivery time depends on both the number of OHT vehicles and the OHT dispatching policy deployed in the loop. Define t -- ( t l , t 2 , . . . , t l L i ) as the carrier delivery times in the fab, where tz is the average delivery time in OHT loop 1 during T and 1 ~ {1, 2 , - . . , ILl}. The relationship function O is then defined among the carrier delivery times, the number of OHT vehicles, and the OHT dispatching policies:
t = O(v, p ) .
The OHT allocation and dispatching problem (P) defined in Section II is an integer programming problem of NPhard [6] computational complexity due to the nonlinearity of equations (3), (4) and (5). From the observations on the problem characteristics, in this section, we propose a twophased approach to solve this problem. The two-phased approach decomposes the solution methodology into two parts: at first, the best OHT dispatching policy is selected to achieve the minimum requirements and, at the second part, based on the selected OHT dispatching policy, the optimal number of OHT vehicles in each OHT loop is then determined to meet the OHT service requirements as close as possible. Figure 4 depicts the two-phased approach for OHT allocation and dispatching problem solution.
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Given the required (or target) OHT service requirements r -- (rl, r 2 , . - . , rill), where rt is the OHT service requirement in OHT loop 1 during T a n d / E {1,2,...,1L1}. Our objectives of the OHT allocation and dispatching problem are to meet the OHT service requirements as close as possible while minimizing the averages and variances of carrier delivery times. Define the objective function Z as
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(6) Figure 4. Schematic Diagram of the Two-Phase Approach
where F~ (.) and F2(.) are convex functions. F~ (.) represents the tracking capability of OHT service requirements and F2(.) the costs caused by the averages and variances of carrier delivery times, respectively. The OHT allocation and dispatching problem is then to find an optimal collection of number of OHT vehicles and OHT dispatching policy for each loop, {v, p}, to minimize the differences between the OHT service requirements and the actual resultant moves and to minimize the averages and variances of carder delivery times, Mathematically, it is defined asthe following: (P)
{vmin} Z
subject to (1)-(6). Note that equations (3), (4) and (5) in Section II are usually difficult to be exactly defined and are different by cases. Simulation studies of [4] indicate that the relationships are nonlinear among m, v, p, and t.
A. Phase 1: Selecting the Best OHT Dispatching Policy
For an OHT loop, the relationship among its dispatching policy, the number of OHT vehicles, and the resultant average carrier delivery time is defined in Equation (5). The function 19 is usually difficult to define and depends on the dispatching policy used in the loop. To resolve this difficulty, a simulation-based method is adopted to simplify the sophisticated modelling efforts for the function O. A simulation model is first built for each OHT loop. Simulation studies are then conducted based on different configurations of the number of OHT vehicles as well as several OHT dispatching policies. The best OHT dispatching policy that achieves the minimal average carrier delivery times is then selected among these simulated policies.
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B. Phase 2: Determining the Number of OHT Vehicles The best OHT dispatching policy selected in Phase 1 is then adopted as the dispatching policy for all the OHT loops. The simulation models built in Phase 1 are used again to come out the relationship between the number of OHT vehicles and moves, as well as between the number of OHT vehicles and carrier delivery times, respectively. Figure 5 demonstrates the aforementioned relationships.
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Phase 2. Determining the Number of OHT Vehicles Step 2: (Determining Target Number of OHT Vehicles) Adopt the best OHT dispatching policy in Step 1 as the OHT dispatching rule for all the loops. Conduct the simulations by considering the effects of the number of OHT vehicles in a loop and the resultant moves, as well as by considering the effects of the number of OHT vehicles versus the resultant carrier delivery times. Based on the requirements of OHT moves, determine the target number of OHT vehicles while achieving the minimal average and variances of carrier delivery times. Step 3: (Allocating OHT Vehicles) If there exists an OHT loop whose target number of OHT vehicles is more than that of being allocated, start to move the excessive OHT vehicles away from the OHT loops whose required OHT vehicles are less than or equal to the initial ones and whose resultant effect is the minimal. Step 4: (Checking for Terminating Conditions) If there are no OHT loops with less OHT vehicles than required, then STOP; else continue Step 3.
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IV. N U M E R I C A L E X P E R I M E N T S
Figure 5. Relationship Diagram among v, m, and t The required number of OHT vehicles in an OHT loop can be easily determined from the simulated results with the given OHT service requirements of moves. If there exists an OHT loop whose existing OHT vehicles are less than the required number of OHT vehicles, replenishment of OHT vehicles is needed from other OHT loops to this one. This re-location of OHT vehicles starts from moving the excessive OHT vehicles away from the OHT loops whose required OHT vehicles are less than or equal to the initial ones while the resultant effect is the minimal. The re-location of OHT vehicles continues until there are no OHT loops with less OHT vehicles than required. Note that the above approach assumes that there are no burst OHT requirements for the whole lab, i.e., the total requirements of OHT services in the fab should be less than those of the total number of OHT vehicles which have been determined in the fab layout design stage. The two-phased approach for OHT allocation and dispatching is then summarized as the following algorithm.
OHT Allocation and Dispatching (OHTAD) Algorithm Phase 1. Selecting the Best OHT Dispatching Policy Step 0: (Initialization) Build the simulation models for all the OHT loops in the fab. Step 1: (Selecting the Best OHT Dispatching Policy) Conduct the simulations with a set of OHT dispatching policies. Select one among them to be the best OHT dispatching policy which minimizes the averages and variances of carrier delivery times.
To convey our idea of the two-phased approach on OHT allocation and dispatching problems in 300mm AMHS management, numerical experiments are conducted with realistic data from a local 300mm mass production fab. There are 32 OHT loops with 250 OHT vehicles running in these loops within the fab. Although, in addition to the OHT loops, there are also OHS (Over Head Shuttle) interbay systems installed in the fab, to simply the discussion, the requirements and capacity on OHS have been excluded in the design of numerical experiments. The time horizon is set to 30 days and the time unit is of a shift (12 hours for a shift). The required moves for each OHT loop ar first calculated by considering the daily production schedules as well as the WIP profiles of the equipment within the loop. For each OHT loop, there is at least one entry/exit point as the linkage to other OHT loops for OHT vehicles to travel across. For the required moves that demand an OHT vehicle to transport from one OHT loop to the others, these moves will be divided into at least two separated moves - one is the move to the exit point of the current loop and the other starts from this point to the destination. Four OHT dispatching policies are used in the numerical studies of Phase 1. They are Nearest Job First (NJF), Longest Waiting Time (LWT) First, Farthest Job First (FJF), and Modified Nearest Job First (MNJF) policies. The definition of these four policies are described as below: Nearest Job First (NJF) Policy Given a set of transport jobs ready for and waiting to be transferred by OHTs, an empty OHT is dispatched to the job whose waiting point (departure) is the nearest to the current location of the empty OHT. For each empty OHT, NJF utilizes the intuitive idea of "first meet, first serve" on transport jobs.
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Longest Waiting Time (LWT) First Policy Given a set of transport jobs ready for and waiting to be transferred by OHTs, an empty OHT is dispatched to the job whose waiting time is the longest among those of the jobs. For each empty OHT, LWT utilizes the intuitive idea of'first create, first serve" on transport jobs.
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