Design of Plant Layouts with Queueing Effects

Saifallah BenjaafarDepartment of Mechanical Engineering

University of MinnesotaMinneapolis, MN 55455

July 10, 1997

AbstractIn this paper, we present a formulation of the plant layout problem where the objective is to

minimize work-in-process. We show that the choice of layout has a direct impact on work-in-

process accumulation, manufacturing lead time, achievable throughput rates, and required material

handling capacity. More importantly, we show that layouts generated using a queueing-based

model can be very different from those obtained using conventional layout procedures. In

particular, we present a number of surprising and counter-intuitive results. For example, we show

that reducing overall distances between departments can increase average work-in-process in the

plant. We also show that the relative desirability of a layout can be affected by non-material

handling parameters, such as department utilization levels, variability in processing times at

departments and variability in product demands.

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1. Introduction

Including queueing effects in design of plant layouts has been notoriously difficult [2, 4, 5,

12]. This difficulty is due to a lack of analytical models which are capable of explicitly capturing

the effect of layout configuration on dynamic plant behavior. As a result, most existing plant

layout design procedures attempt to simply minimize a static measure of material handling time or

cost [6, 11, 15]. This is certainly the case for the widely used quadratic assignment problem

(QAP) formulation, where the objective is to minimize the total distances traveled in moving

material from one processing department to another [6, 11, 15].

In this paper, we present a reformulation of the quadratic assignment problem, where the

objective is to minimize work-in-process (WIP). We show that the choice of layout has a direct

impact on work-in-process accumulation, manufacturing lead time, achievable throughput rates,

and required material handling capacity. More importantly, we show that layouts generated using a

queueing-based model can be very different from those obtained using the conventional QAP

formulation. In particular, we present a number of surprising and counter-intuitive results. For

example, we show that reducing overall distances between departments can increase average work-

in-process in the plant. We also show that the relative desirability of a layout can be affected by

non-material handling parameters, such as department utilization levels, variability in processing

times at departments and variability in product demands. These results are different from

conclusions reached by Fu and Kaku in a recent paper [4], where they argued that the QAP

formulation leads to a layout that also minimizes average WIP.

In order to obtain average work-in-process due to a particular layout configuration, we model

the manufacturing facility as a central server queueing network. Each processing department is

modeled as either a single or a multi-server queue with arbitrary distribution of product processing

and inter-arrival times. The material handling system operates as a central server in moving

material from one department to another. We assume that the material handling system consists of

discrete devices (e.g., forklift trucks, human operators, automated guided vehicles, etc.). The

distances traveled by the material transporters are determined by the layout configuration, product

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routings and product demands. In determining the transporter travel time distribution, we account

for both empty and full trips by the material transport devices. Detailed description of the queueing

model and our assumptions are given in section 4.

Because we impose no assumptions regarding the arrival processes of products or their

processing times, exact analytical solutions are difficult to obtain. Therefore, we rely on network

decomposition and approximation techniques to obtain approximate estimates of average work-in-

process. These approximations have been shown elsewhere to provide fairly reliable estimates of

the actual work-in-process for a wide range of parameters [3, 17, 18]. Since our objective in

layout design is to obtain a ranked ordering of different layout alternatives, approximations are

sufficient, as long as they guarantee accuracy in the ordering of these alternatives.

An alternative to analytical approximations is to use computer simulation. However,

simulation can be very computing-intensive when hundreds or thousands of layout configurations,

as it is often necessary in layout design, must be evaluated. Results from simulation are often

difficult to generalize to systems other than those being simulated. This contrasts with analytical

models where the mathematical relationships we obtain can be readily used to gain general insights

into the fundamental relationship between various parameters. Nevertheless detailed simulation is

useful whenever an accurate assessment of an individual layout is required.

The organization of the paper is as follow. In section 2, we provide a review of relevant

literature. In section 3, we briefly describe the quadratic assignment problem. In section 4, we

describe our queueing-based formulation of the layout problem and the corresponding queueing

network model. In section 5, we use our model to study the relationship between layout and

work-in-process and to compare layouts selected by the QAP to those selected by the queueing

model. We also validate our results using simulation. In section 6, we present extensions of our

model and conclusions.

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2. Literature Review

Very little of the existing literature addresses queueing issues in facility layout design. In a

recent review of over 150 papers published over the past ten years on plant layout, Meller and

Gau [14] identified only one paper on the subject. This paper is by Fu and Kaku [5] who to our

knowledge, were the first and only ones to explicitly address queueing issues in layout design.

Similarly to our study, they used average work-in-process as the layout design criterion. To obtain

average work-in-process, they developed a simple queueing network model in which they assumed

all arrival processes to be Poisson and all processing times to be exponential, including

transportation times. In modeling transportation times, they also ignored empty travel by the

material handling devices and accounted only for full trips. These assumptions allowed them to

treat the network as a Jackson queueing network - i.e., a network of independent M/M/1 and

M/M/n queues - for which a closed-form analytical expression of average work-in-process is

available. Using their model, they showed that minimizing average work-in-process is, in fact,

equivalent to minimizing average material handling cost, as used in the conventional QAP

formulation.

The limitations of the Fu and Kaku formulation are in the assumptions used. By assuming that

inter-arrival times, processing times, and material handling times are all exponentially distributed,

and by not accounting for empty travel times, they failed to capture important dynamics that arise

under less restrictive assumptions. These include, for example, effects due to the second moments

of transportation and processing times, and variability in product inter-arrival times. As we show

in this paper, when the distribution of processing, material handling, and inter-arrival times are

appropriately accounted for, the layouts obtained from the queueing model can be very different

from those obtained by the QAP formulation. In fact, we show that under certain circumstances,

increasing travel distances can reduce average work-in-process.

Other related literature include the work of Kouvelis and Kiran [10] who consider a closed

queuing network for modeling Flexible Manufacturing Systems (FMS). Their modeling

assumptions are similar to those of Fu and Kaku, except that they assume work-in-process is

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maintained constant. Therefore, they measure performance by average throughput rather than

work-in-process. Johnson and Brandeau [7, 9] and Thonemann and Brandeau [16] have

extensively used single stage queueing systems to model discrete material handling devices, such

as automated guided vehicles. However, their models do not explicitly capture differences in

layout configurations. Several other queueing and simulation models have been proposed for the

design and analysis of material handling systems. An excellent review on this subject can be found

in [8].

3. The Quadratic Assignment Problem

The quadratic assignment problem (QAP) can be formulated as follows [6, 11, 15]:

Minimize z = xikxilλ ijdkl ∑l

∑k

∑j

∑i

subject to:

xik = 1∑k = 1

K

V i (1)

xik = 1∑i = 0

M + 1

V k (2)

xik = 0, 1 V i, k (3)

where xik = 1 if department i is assigned to location k and xik = 0 otherwise, dkl is the distance

between locations k and l, and λ ij is the amount of material flow (the number of material unit

loads) between departments i and j. Constraints 1 and 2 ensure, respectively, that each department

is assigned one location and each location is assigned one department. The objective function

minimizes material handling cost by minimizing the average distance traveled by an arbitrary unit

load of material. If material transport is provided by a discrete material handling device, then the

QAP also minimizes the average distance traveled by the device when the device is full - i.e., while

carrying a load.

Although the above formulation adequately accounts for the cost/time in moving material

between departments, it has several important limitations. The objective function is a static

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measure that does not account for variability in material flows between departments. In systems

with discrete material handling devices, the objective function does not capture empty travel by

these devices. Also, by minimizing only average distances traveled, information about the higher

moments of travel distribution is ignored. This could lead, for example, to selecting a layout with

a small mean but a high variance. As we show in section 5, this would result in higher variability

in material handling times and cause longer queueing delays. More generally, by focusing only on

average material handling time, the QAP fails to capture congestion effects due to waiting for

material handling resources when the number of these resources is finite. Contention for finite

resources, coupled with variability, leads to congestion and queueing delays which directly affect

overall manufacturing lead times and work-in-process levels in the plant. In the next section, we

present a reformulation of the QAP where many of the above limitations are addressed.

4. Model Formulation

In order to illustrate the procedure for including queueing effects in layout design, we make the

following assumptions (most of these assumptions are made only for illustrative purposes and can

be relaxed as discussed in section 6).

i) The plant produces N products. Product demands are independently distributed random

variables characterized by an average demand Di and a squared coefficient of variation Ci2 for i =

1, 2, …, N . The squared coefficient of variation denotes the ratio of the squared mean over the

variance.

ii) Material handling is assured by a single discrete material handling device. Material transfer

request are serviced on a first come-first served (FCFS) basis. In the absence of any requests, the

material handling transporter remains at the location of its last delivery.

iii) The travel time between any pair of locations k and l, tkl, is assumed to be deterministic and is

given by tkl = dkl/v, where dkl is the distance between locations k and l and v is the speed of the

material handling transporter.

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iv) Products are released to the plant from a loading department and exit the plant through an

unloading (or shipping) department. Departments are indexed from i = 0 to M + 1, with the

indices i = 0 and M + 1 denoting, respectively, the loading and unloading departments.

v) The plant consists of M processing departments, with each department consisting of a single

server (e.g., a machine) with ample storage for work-in-process. Jobs in the queue are processed

in first come-first served order. The amount of material flow, λij, between a pair of departments i

and j is determined from the product routing sequence and the product demand information. The

total amount of workload at each department is given by:

λ i = λ ki∑k = 0

M

= λ ij∑j = 1

M + 1

for i = 1, 2, …m, (4)

λ0 = λM + 1 = Di∑i = 1

N

, and (5)

λ t = λ ij∑j = 1

M + 1

∑i = 0

M

, (6)

where λt is the workload for the material handling transporter.

vi) Processing times at each department are independent and identically distributed with an

expected processing time E(Si) and a squared coefficient of variation Csi2 for i = 0, 1, …, M + 1

(the processing time distribution is determined from the processing times of the individual

products).

vii) A layout configuration corresponds to a unique assignment of departments to locations. We

use the vector notation x = {xij}, where xik = 1 if department i is assigned to location k and xik = 0

otherwise, to differentiate between different layout configurations.

The plant is modeled as an open network of GI/G/1 queues, with the transporter being a central

server queue. A graphical depiction of product flow through the network is shown in Figure 1. In

order to obtain WIP-related performance, we use network decomposition and approximation

techniques (see [3] and [18] for a general review) where the performance of each department, as

well as the transporter, is approximated using the first two moments of the associated job

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λ0

Loading department Transporter

λM+1

λ t

Unloading department

department 1

Department M

Department 2

λ0λ0

λM

λ2

λ1

Figure 1 Central server queueing network model

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inter-arrival and processing times. Under a given layout, expected WIP at department i can then be

obtained as:

E(WIPi) = ρi

2(Cai2 + Csi

2)gi

2(1 - ρi) + ρi, (7)

where ρi = λ iE(Si) is the average utilization of department i, Cai2 and Csi

2 are, respectively, the

squared coefficients of variation of job inter-arrival and processing times, and

gi ≡ gi(Cai2, Csi

2, ρi) =

exp[-2(1 - ρi)(1 - Cai

2)2

3ρi(Cai2 + Csi

2)] if Cai

2 < 1

1 if Cai2 ≥ 1.

{ (8)

Similarly, expected WIP at the transporter is given by:

E(WIPt) = ρt

2(Cat2 + Cst

2)gt

2(1 - ρt) + ρt, (9)

where ρt = λtE(St) is the average utilization of the transporter, E(St) is the expected travel time per

material transfer request and Cst2 is its squared coefficient of variation. Note that ρt and ρi must be

less than one for expected work-in-process to be finite.

In order to compute expected WIP, the first and second moments of transportation time, as

well as the coefficients of variation of inter-arrival times to each department and to the transporter

must be known. As we show in the following two theorems, these parameters are directly

determined by the layout configurations.

Theorem 1: Given a layout configuration x = {xik}, the first and second moments of transporter

travel time are given by the following:

E(St) = λkrλ ij/λ t2∑

k = 0

M

trij(x)∑j = 1

M + 1

∑i = 0

M

∑r = 1

M + 1

, and

E(St2) = λkrλ ij/λ t

2∑k = 0

M

(trij(x))2∑j = 1

M + 1

∑i = 0

M

∑r = 1

M + 1

,

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where trij(x) = xrkxilxjs(dkl + dls)/v∑s

∑l

∑k

= xrkxildkl/v ∑l

∑k

+ xilxjsdls/v ∑s

∑l

and

corresponds to the travel time, under layout configuration x, from department r to department i

and then to department j.

Proof: First note that travel time from department i to department j, under layout configuration x ,

is given by:

tij(x) = xikxjldkl/v∑l = 1

K

∑k = 1

K

. (10)

Also note that in responding to a material transfer request, the transporter performs an empty trip

from its current location (the location of its last delivery), at some department r, followed by a full

trip from the origin of the current request, say department i, to the destination of the transfer

request at a specified department j. Then, in order to obtain the first two moments of the

transporter travel time, we need to characterize the probability distribution prij of an empty trip

from r to i followed by a full trip from i to j. The probability prij is given by:

prij = pkr∑k = 0

M

pij, (11)

where pij is the probability of a full trip from department i to department j which can be obtained as

pij = λ ij

λ ij∑j = 1

M + 1

∑i = 0

M. (12)

Noting that the time to perform an empty trip from department r to department i followed by a full

trip to department j is given by trij(x) = tri(x) + tij(x), the first two moments of transporter travel

time per transfer request can now be found as:

E(St) = prijtrij(x)∑j = 1

M + 1

∑i = 0

M

∑r = 1

M + 1

, and (13)

E(St2) = prij(trij(x))2∑

j = 1

M + 1

∑i = 0

M

∑r = 1

M + 1

, (14)

which, upon appropriate substitutions, lead to the desired result. #

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Theorem 2: Given layout x = {xik}, the squared coefficients of variation of job inter-arrival and

departure times at processing departments and at the transporter can be approximated by the

following:

Ca02 = (Di/ Di∑

i = 1

N

)Ci2∑

i = 1

N

,

Cai2 = πi(ρt

2Cst2 + (1 - ρt

2)Cat2) + 1 - πi , for i = 1, 2, …, M + 1, and

Cat2 =

πiρi2Csi

2∑i = 0

M

+ πi(1 - ρi2)(1 - πi)∑

i = 1

M

+ πi2(1 - ρi

2)ρt2Cst

2∑i = 1

M

+ π0(1 - ρ02)Ca0

2

1 - πi2(1 - ρi

2)( 1 - ρt2)∑

i = 1

M,

where πi = λi/λt for i = 1, 2, …, M + 1.

Proof: We use the following known approximations for characterizing, respectively, the squared

coefficients of variation in inter-arrival and departure times at a node i in an open network of

GI/G/1 queues [3]:

Cai2 =

λ jpji

λ i

(pjiCdj2 + (1 - pji))∑

j ≠ i

+ λ0γi

λ i

(γiCa02 + (1 - γi)), and (15)

Cdi2 = ρi

2Csi2 + (1 - ρi

2)Cai2, (16)

where pij is the routing probability from node i to node j (nodes include departments and the

material handling device), γi is the fraction of external arrivals that enter the network through node

i, and 1/λ0 and Ca02 are, respectively, the mean and squared coefficient of variation of the external

job inter-arrival times. In our case, γ0= 1 and γi = 0 for all others since all jobs enter the cell at the

loading department, the routing probability from departments i = 0 through M to the material

handling transporter is always one, that from the material handling transporter to departments j = 1

through M + 1 is

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ptj =

λ ij∑i = 0

M + 1

λ ij∑j = 0

M + 1

∑i = 0

M + 1 (17)

and to the loading department (j = 0) is zero. Parts exit the cell from department M + 1 (unloading

department) so that all the routing probabilities from that department are zero. Substituting these

probabilities in the above expression, we obtain

Ca02 = (Di/ Di∑

i = 1

N

)Ci2∑

i = 1

N

, (18)

Cat2 = (λ i/λ t)Cdi

2∑i = 0

M

= πiCdi2∑

i = 0

M

, and (19)

Cai2 = πiCdt

2 + 1 - πi for i = 1, 2, …, M + 1, (20)

which, along with equality (10), can be simultaneously solved for the desired results. #

The layout design problem can now be formulated as:

Minimize E(WIP) = E(WIPi)∑i = 0

M + 1

+ E(WIPt)

subject to:

xik = 1∑k = 1

K

i = 0, 2, …, M + 1 (21)

xik = 1∑i = 0

M + 1

k = 1, 2, …, K (22)

ρt < 1 (23)

xik = 0, 1 i = 0, 2, …, M + 1; k = 1, 2, …, K (24)

The above formulation shares the same constraints as the quadratic assignment problem. We

require an additional constraint, constraint 23, to ensure that a selected layout is feasible and will

not result in infinite work-in-process. The objective function is however different from that of the

QAP. In the conventional QAP, the objective function is a positive linear transformation of the

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expected transporter time. Therefore, a solution that minimizes average travel time between

departments is optimal. In the next section, we show that this is not necessarily the case when

queuing effects are accounted for and that solutions obtained by the two formulations can be very

different. We also note that by virtue of Little's law [13], minimizing epxected WIP is equivalent

to minimizing expected product flow time. Therefore, our model can readily be used to optimize

lead time performance as well.

The quadratic assignment problem is notoriously known for being NP hard. Therefore our

model is also NP hard (our objective function is a nonlinear transformation of that of the QAP).

Although it is not our intent in this paper to provide a solution algorithm, most of the existing

heuristics for the QAP can be readily applied to the our model. For example, an iterative pairwise

or multi-step exchange procedure, such as CRAFT [1], can be used to generate a solution. Note

that in this case, after each exchange, it is the expected WIP that is calculated and used to evaluate

the desirability of the exchange. Other heuristics may be used as well. For a recent review of the

quadratic assignment problem and solution procedures, the reader is referred to Pardalos and

Wolkowicz [15].

5. Model Analysis and Insights

Examining the expression of expected WIP in the objective function, it is easy to see that it has

two sources: the processing departments and the material handling transporter. In both cases, WIP

accumulation is determined by (1) the variability in the arrival process, (2) the variability in the

processing/transportation times, and (3) the utilization of the departments and the transporter.

Because the transporter provides input to all the processing departments, variability in

transportation time directly affects the variability in the arrival process to all the departments. In

turn, this variability, along with the variability of the department processing times, determines the

input variability to the transporter. Because of this close coupling, the variability of any resource

affects the work-in-process at all other resources.

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The conventional QAP model, by focusing only on average travel time, fails to account for the

important effect of variability. As we show in the following observations, average travel time can

be a poor indicator of expected WIP.

Observation 1: Layouts with the same average travel times can have different average WIP.

Proof: We use a counter-example to show that layouts with similar average travel time can have

very different average WIP levels. Consider a facility with three departments (i = 0, 1, and 2).

The facility produces a single product, which is manufactured in the fixed sequence shown in

Figure 2(a). The corresponding queuing network is shown in Figure 2(b). Other relevant data is

as follows: D1 = 1.62 parts/hour; E(Si)= 36 min for i = 0, 1, and 2, C12 = 1; and Csi

2 = 1 for i = 0,

1, and 2, and v = 10 ft/min. We consider two layout scenarios, x1 and x2. The distances

between departments are as follows, scenario 1: d01(x1) = d10(x1) = d12(x1) =d20(x1) = d21(x1)

= 100 ft ; and scenario 2: d01(x1) = d10(x1) = d20(x1) = 10 ft, d12(x1) = 190 ft, and d21(x1) =

280 ft. It can be verified that the two scenarios lead to an average travel time of E(St(x1)) =

E(St(x2)) = 17.5 and a transporter utilization ρt(x1) = ρt(x2) = 0.945. The second moments of

transportation time are, however, different: E(St2(x1)) = 325 and E(St

2(x2)) = 644.5. From the

first two moments of travel time, we obtain Cst2(x1) = 0.061224, Cat

2(x1) = 0.98841, Ca02 (x1) = 1,

Ca12 (x1) = Ca2

2 (x1) = 0.580205, Cst2 (x2) = 1.10449, Cat

2(x2) = 1.00129, Ca02 (x2) = 1, and

Ca12 (x2) = Ca2

2 (x2) = 1.046725, from which we can calculate expected WIP as follows:

E(WIP(x1)) = 99.33 and E(WIP(x2)) = 123.76. Since E(WIP(x2)) > E(WIP(x1)), although

ρt(x1) = ρt(x2), our result is proven. #

To confirm our analytical result, we simulated a detailed model of the two layout scenarios.

For each scenario, we obtained the following 95% confidence interval for the value of expected

WIP: E(WIP(x1)) = 102.14 +/- 1.67 and E(WIP(x2)) = 123.12 +/- 1.79, which certainly support

our analytical findings. For the sake of brevity, details of the simulation are omitted.

Observation 2: A smaller average travel time does not always lead to a smaller average WIP.

Proof: We use again a counter-example to prove that E(WIP) is not necessarily decreasing in

average travel time. Consider a facility identical to the one previously described. All parameters

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M0 M2M1

(a) Product flow sequence

λ0

Transporter

λ2

λ tλ0λ0 λ1

Μ1Μ0

Μ2

(b) Queuing Model

Figure 2 Product flow sequence and the corresponding queueing network model

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are the same with the exception of department processing time, where in this case E(Si) = 36.5 min

for i = 0, 1, and 2. Again, we consider two layout scenarios, x1 and x2; scenario 1: d01(x1) =

d10(x1) = d12(x1) =d20(x1) = d21(x1) = 100 ft ; and scenario 2: d01(x1) = d10(x1) = d20(x1) =

10 ft, d12(x1) = 10 ft, and d21(x1) = 170 ft. Layout x1 results in an average travel time of

E(St(x1)) = 17.5 and layout x2 in E(St(x2)) = 8.25. The resulting utilization of the transporters

is ρt(x1) = 0.945 and ρt(x2) = 0.4455. Thus, we have E(St(x1)) > E(St(x2)) and ρt(x1) >

ρt(x2). The second moments of transportation time can be calculated as E(St2|x1) = 325 and

E(St2|x2) = 198.25, from which we obtain Cst

2(x1) = 0.061224, Cat2(x1) = 0.993961, Ca0

2 (x1) = 1,

Ca12 (x1) = Ca2

2 (x1) = 0.580502, Cst2(x2) = 1.912764, Cat

2(x2) = 1.001311, Ca02 (x2) = 1, and

Ca12 (x2) = Ca2

2 (x2) = 1.091104 (note that Cst2 (x2) > Cst

2 (x1)). We can now calculate expected WIP

as follows: E(WIP(x1)) = 185.195 and E(WIP(x2)) = 210.966. Since E(WIP(x2)) >

E(WIP(x1)), although ρt(x2) < ρt(x1), our result is proven. Again, we confirmed or result using

simulation. The 95% confidence intervals for expected WIP are: E(WIP(x1)) = 182.14 +/- 6.58

and E(WIP(x2)) = 205 +/- 5.72, which certainly support our conclusion. #

The above observations highlight the important effect that variability in travel times between

departments can have on the desirability of a layout. In both observations, layouts with the smaller

travel time variance were superior, even when their average travel times were higher. In fact, in

the example of observation 2, the average travel time in layout x2 was less than half that of x1 .

These results clearly show that minimizing average travel time (or transporter utilization) is not

always desirable. In fact, reductions in average travel time if they come at the expense of

increasing travel time variance should at times be avoided. Note that the increase in travel time,

due to the higher travel time variance, does not only affect WIP accumulation at the transporter, but

also the level of WIP at the processing departments. The greater variability in travel times

translates into greater variability in the arrival process to the department which, in turn, leads to

longer queues at these departments. These results point to the need to explicitly account for travel

time variance when selecting a layout. A layout that exhibits a small variance may, indeed, be

more desirable than one with a smaller travel time average.

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Travel distances are not, however, the only factor that affects the relative desirability of a

layout. Non material handling factors such as department utilization levels, variability in

department processing times, and variability in demand levels could determine whether one layout

configuration is more desirable than another.

Observation 3: The relative desirability of a layout can be affected by non-material handling

factors.

Proof: We use a series of examples to show that varying either utilization levels or processing

time and demand variability can affect the relative desirability of a layout. We consider the same

example described in observation 2 and the same two layout scenarios x1 and x2. In Table 1, we

show the effect of varying department processing time on the performance of x1 and x2, and in

Table 2, we show the effect of varying processing and demand variability (for the sake of brevity,

only WIP values are reported). It is easy to see that the same layout can be superior under one set

of parameters and inferior under another. For example, x2 has a smaller average WIP than x1

when average processing time is 32 min and a much larger average WIP than x1 when average

processing time is 37 min. Similarly, x1 has a smaller average WIP than x2 when Ca02 = 1 and

Csi2 = 0.5 and a larger average WIP when Ca0

2 = Csi2 = 2. #

The above results show that the relative desirability of a layout is highly dependent on many

operating parameters. For example, a layout that is effective in an environment where department

utilization is high may not be appropriate if departments were lightly loaded. Similarly, a layout

that is effective when processing/arrival times are highly variable may not be appropriate if

processing/arrival times were deterministic. Generally speaking, layouts that reduce travel time

variability are more desirable when department utilization is high or variability in either inter-arrival

or processing times is low. When either department utilization is low or processing/demand

variability is high, minimizing average travel times becomes more important. However, definite

guidelines are difficult to identify because of the many interacting parameters. Therefore, an

evaluation of the queueing model will often be required to generate an accurate layout ranking.

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Table 1 The effect of utilization on the relative desirability of layouts(Ca0

2 = 1, Csi2 = 1, D = 1.62 parts/hr)

E(Si) E(WIP(x1)) E(WIP(x2))

32 min 25.76 20.55

33 min 30.55 26.18

34 min 38.44 35.51

35 min 53.99 54.02

36 min 99.33 108.20

37 min 2,588 3,088

Table 2 The effect of variability on the relative desirability of layouts(E(Si) = 35 min, D = 1.62 parts/hour)

Variabilitycoefficients

E(WIP(x1)) E(WIP(x2))

(Ca02 = 1, Csi

2 = 0.5) 38.75 47.24

(Ca02 = 1, Csi

2 = 1) 53.99 54.02

(Ca02 = 1, Csi

2 = 2) 86.41 84.47

(Ca02 = 2, Csi

2 = 2) 95.02 92.96

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A special instance when minimizing average travel time also minimizes average work-in-

process is when Cai2 = Csi

2 = Cat2 = Cst

2 = 1 for all i = 0, 1, …, M + 1. This is the case when the

processing times, inter-arrival times, and travel times are all exponentially distributed. Average

work-in-process can then be shown to be a function of only the utilization levels. Therefore, a

layout that minimizes the utilization of the transporter, ρt, also minimizes average work-in-process.

Since ρt is a linear transformation of average travel time, minimizing average travel time minimizes

ρt.

Finally, we should note that in addition to affecting work-in-process and lead time, the choice

of layout determines production capacity. The stability condition ρt < 1 puts a limit on the

maximum feasible number of unit transfer loads per unit time that can be moved by the transporter.

This limit, λmax, is given by

λmax = 1/ prij( xrkxilxjs(dkl + dls)∑s

∑l

∑k

)/v∑j = 1

M + 1

∑i = 0

M

∑r = 1

M + 1

, (25)

which is clearly a function of the transportation distances and the distribution of transportation

times. By limiting λmax , the maximum achievable output rate from the system is limited. This

means that the choice of layout could directly affect the available production capacity. Maximizing

throughput by maximizing λmax could be used as an alternative layout design criterion. In this

case, layouts would be chosen so that the available material handling capacity is maximized. Such

a design criterion could be appropriate in high volume/make-to-stock environments where

minimizing lead time or work-in-process is not critical.

The stability condition can also be used to determine the minimum required number of

transporters, nmin, for a given material handling workload, λt:

nmin = λ t prij( xrkxilxjs(dkl + dls)∑s

∑l

∑k

)/v∑j = 1

M + 1

∑i = 0

M

∑r = 1

M + 1

. (26)

Similarly, we could obtain the minimum required transporter speed, for a fixed number of

transporters, or the minimum required transfer batch size. Note that in determining these feasibility

requirements, we account for both full and empty travel by the material handling device.

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6. Discussion and Conclusion

In this paper, we presented a formulation of the plant layout problem where the objective is to

minimize work-in-process. We used the model to explore the relationship between layout

configuration and operational performance. We showed that the conventional criterion of selecting

layouts based on average material handling distances can be a poor indicator of queueing effects.

In a series of counter-intuitive observations, we showed that the conventional QAP formulation can

lead to the selection of very different layouts from those obtained using the queueing-based model.

In particular, our analysis highlighted the important effect that variance in material handling times

plays in determining layout desirability. This effect is ignored in conventional layout procedures.

We also showed that non-material handling factors, such as processing time variability or process

utilization, can directly affect layout performance.

Certain simplifying assumptions we have made in our current queueing model are easy to

relax. For example, the model can be extended to allow for multiple transporters and multiple

processing units at each department. Approximations for GI/G/n queues would simply have to be

used (see [3, 17]) for details). Similarly, the assumptions of deterministic distances between pairs

of departments and deterministic transporter speed can be eliminated. Accounting for variability in

these two parameters can be easily accommodated by appropriately modifying the second moment

of transportation time. Other assumptions are, however, more difficult to relax. This includes, for

example, the modeling of control policies other than FCFS for either routing transporters or for

sequencing products. In these cases, simulation may be the only practical approach.

Our queueing model is based on known approximations of GI/G/1 queues. While these

approximations are fairly robust, more customized approximations could be constructed for

specific applications [3]. However, we should note that the usefulness of the queueing model is

not as much in its accuracy, as it is in its ability to capture key effects in the relationship between

layout and operational performance and in its ability to lead to consistent rankings of different

layout alternatives. More importantly, it is a tool that can be used at the design stage to rapidly

evaluate a large number of alternatives, a task that may be difficult to achieve using simulation.

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The queueing model also offers an opportunity to design simultaneously the layout and the material

handling system (e.g., determining the number of transporters, transporter speed, travel paths,

etc.) and to examine the effect of both on expected WIP. The ability to evaluate layout and material

handling concurrently is indeed absent from most existing layout procedures.

Several avenues for future research are possible. Better analytical approximations should be

developed to take advantage of the special structure of the layout problem. Either analytical or

simulation models that account for different routing and dispatching policies of the material

handling system should be constructed. These models should then be used to study the effect of

different policies on layout performance. Furthermore, it would be useful to use the queueing

model to evaluate and compare the performance of different classical layout configurations, such as

product, process, and cellular layouts, under varying conditions. This may lead to identifying new

configurations that are more effective in achieving short lead times and small WIP levels. In

previous sections, we have argued that variance in travel distances is as important as the mean of

these distances. Therefore, identifying configurations that minimize both mean and variance is

important. Examples of such layouts, as shown in Figure 3, could include a Star layout, where

departments are equi-distant from each other, or a Hub-and-Spoke layout, where each hub consists

of several equi-distant departments and is serviced by a dedicated transporter.

Acknowledgement: The author's research is supported by the National Science Foundation

under grant No. DMII-9309631, the U.S. Department of Transportation under grant No.

USDOT/DTRS93-G-0017, and the University of Minnesota Graduate School.

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