Proceedings of the 2012 Winter Simulation Conference
C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A.M. Uhrmacher, eds

OPERATIONS ANALYSIS AND APPOINTMENT SCHEDULING FOR
AN OUTPATIENT CHEMOTHERAPY DEPARTMENT
Mitsuko Yokouchi

Setsuko Aoki

Department of Nursing
Graduate School of Medicine
Nagoya University
Daiko-Minami, Higashi-ku,
Nagoya, Aichi 461-8673, JAPANDepartment of Nursing
Kohnan Hospital Group
Kamokogahara, Higashinada-ku
Kobe, Hyogo 658-0064, JAPANHaiXia Sang
Run Zhao
Soemon Takakuwa

Graduate School of Economics andBusiness Administration
Nagoya University
Furo-cho, Chikusa-ku,
Nagoya, Aichi 464-8601, JAPANABSTRACTWith current increasing demands for outpatient medical services, healthcare providers have had to analyze methods for providing safety and quality care services under constrained resources. A long waitingtime has a severe impact not only on the patients’ satisfaction but also on the physical conditions of thepatients, who receive invasive treatment in ambulatory care facilities. Patients treated with outpatientchemotherapy have been rapidly increasing over the last decade in Japan. In this context, a discrete event
simulation model for exploring appointment scheduling in an outpatient chemotherapy department of ageneral hospital was developed. An efficient schedule was identified that held bed utilization to a tolerance level by restraining the excess waiting-time in a clinical setting. It is suggested that a schedulingmethod based on the infusion time be available for the outpatient chemotherapy department.
1

INTRODUCTION

Outpatient chemotherapy is a new type of outpatient medical service. The number of patients having cancer treated in an outpatient chemotherapy department (OCD) has been rapidly increasing in Japan, after
ambulatory treatment using anti-cancer infusion drugs was first given reimbursement approval under thenational medical insurance in 2002. The development of new drugs that control side effects caused by anti-cancer drugs helps patients to receive chemotherapy in outpatient departments. Moreover, ambulatory
treatment helps patients to maintain their daily lives as much as possible and to reduce the stress that occurs during admission. In addition to the merit for the patients, there is an advantage to the national insurance system with respect to reducing the medical cost, which is attributed to the admission fee. Hence,treatment in an OCD is promoted from the standpoint of both the patients and the service providers. Recently, many hospitals have had to extend their treatment capacity to accommodate increasing numbers ofpatients under the limited medical resources of Japan.

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907

Yokouchi, Aoki, Sang, Zhao, and Takakuwa
With the increasing demand for treatment in OCD, patients’ waiting-time tends to be prolonged. Along waiting-time decreases the patients’ satisfaction and imposes a further burden on the patients in addition to having painful chemotherapy. Previous research studies have revealed that effective appointment
scheduling (AS) can minimize both the patients’ waiting-times and the doctors’ idle times as a result of
regulating the flow of patients through the outpatient department (Cayirli and Veral 2003). AS in outpatient medical services has been one of the important subjects in health care administration.
Within the appointment systems in outpatient departments, there are many types of systems that aremodifications and/or combinations of two basic systems: one is the single-block appointment system andthe other involves individual appointments. Wijewickrama and Takakuwa (2006) summarized major AS
rules or systems for outpatient departments as follows: the single-block system, the individualblock/fixed-interval system, the individual-block/fixed-interval with an initial block system, the multipleblock/fixed-interval rule, the multiple-block/fixed-interval rule with an initial block rule, the variableblock/fixed-interval rule, the individual-block/variable interval rule and the variable-interval appointmentscheduling system. There are some modified rules (Wijewickrama and Takakuwa 2005, Wijewickrama
and Takakuwa 2008).
There are different types of delivery systems in outpatient care services. Conventional outpatient departments in hospitals and primary care clinics are charged with general consultation and elementarytreatment. New delivery systems called Ambulatory Service Centers (ASCs) (Bowers and Mould 2005)
provide specific medical services exclusively, such as interventional procedures for pain management,
day case surgery, endoscopic examination/treatment, radiation therapy or chemotherapy. The AS rules for
traditional outpatient departments have been developed and modified, while AS for new types of delivery
systems, including the OCDs, remain to be explored. Some studies for the ASCs examine the effect of thearrival time on performance (Dexter 1999; Huschka et al. 2007; Huschka et al. 2008; Pirolo et al. 2009;
Ogulata et al. 2009; Centeno, Dod, and Aranda 2010; Sambeek et al. 2010).
Dexter (1999) indicated that long waiting-times for patients were related to having a long consultation time in the preanesthesia evaluation clinic. He suggested that the commonly applied AS in outpatient
departments was insufficient for the anesthesia clinic, to reduce the patient waiting-times. Centeno et al.
(2010) suggested considering the type of endoscopic procedure that was required, based on an analysis of
the low throughput in an endoscopy center, which employed block scheduling. Huschka et al. (2007) applied job hedging to AS in an outpatient procedure center that consisted of a patient waiting area, pre/postprocedure rooms and operation rooms and found that the expected overtime and the patient waiting-timewere more influenced by the arrival time schedules rather than the surgery allocation and sequencing.
The arrival times in those studies were allocated based on several rules that are employed in a conventional outpatient department or operation room (OR). It is assumed that the appointment scheduling in
ASCs ought to consider the characteristics of the medical procedures and the patients’ flow in each typeof unit. There are only a few studies that address AS in an outpatient chemotherapy department.
This study focuses on the appointment scheduling of patients treated in an OCD at a medium scale
general hospital located in Kobe, Japan. The purpose of this study is to identify the best scheduling rules
based on the characteristics of the medical process in OCDs using a discrete event simulation.
2

DESCRIPTION OF THE OUTPATIENT CHEMOTHERAPY DEPARTMENT

The outpatient chemotherapy department (OCD) is a clinical unit of Kohnan Hospital in Kobe, Japan.
This hospital has a total of 400 beds in 9 wards for inpatients and a mixed-patient outpatient departmentthat includes 17 specialties. The OCD supports over 100 patient treatment visits per week. Patients withvarious types of cancer, such as breast cancer, colorectal cancer, uterine cancer, and lung cancer, aretreated in the OCD. Some patients take adjuvant chemotherapy before or after surgery; some take it forradical cure and others take it for symptom management or the prolongation of life. The OCD service isprovided from 9:00 to 17:00 on weekdays. There are a total of 4 chairs and 6 beds located in two large
contiguous areas of the facility. Three registered nurses associated with the department work at the facility.

908

Yokouchi, Aoki, Sang, Zhao, and Takakuwa
All of the patients receive chemotherapy by appointment according to their therapeutic planning. The
treatment process is shown in Figure 1. A patient goes to the outpatient department to take a blood specimen collection after a general reception. Then, he/she has a medical examination taken by his/her own
primary doctor in the department. The primary doctor checks the appropriateness of a prior prescription
based on the examination results. If the doctor approves the prescription, then the final order is given.
Two pharmacists on duty who compound infusions in the dispensary section receive the order. The pharmacist examines the prescription and begins the mixing of the drugs, provided that no concerns arise. If
the patient condition is presumed not to be equal to the planned treatment, his/her appointment is canceled.
After having the medical examination, the patient moves to the OCD facility and steps into the OCD
reception area. While the nurses of the OCD set up the equipment and prepare the patients for the therapy,
infusion drugs are carried from the dispensary section located on another floor to the OCD facility by aporter. A doctor on duty and one of the OCD nurses then start intravenous infusion, and they administeran anti-cancer drug after confirming a patient’s details, the medicine type and the dosage. After the administration of the drugs, a nurse checks the patient. If any abnormalities exist, the doctor decides whether to continue the treatment. After the treatment, a nurse examines the patient and provides instructions
for home care.
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Figure 1: Medical process in the OCD
909

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In general, a combination of medications and the administration time of the infusion are indicated inprescriptions according to a standardized protocol. For example, some patients with breast cancer are
treated using regimen X, which includes a premedication such as a steroid by a bolus infusion for 1 minute, with the 1st infusion solution to reduce the side effects by infusion dripping intra-venous (DIV) for
30 minutes, a 2nd solution blended with anti-cancer drug A by DIV for 60 minutes, a 3rd solution blended
with anti-cancer drug B by DIV for 60 minutes and the final solution to wash out drug toxicity by DIV for
30 minutes. The patients treated by this regimen spend minimally 181 minutes taking DIV in OCD beds.
Other types of regimens (sets of drugs) for breast cancer patients require more long or short times to becompleted according to the prescribed administration time. Moreover, patients with different types ofcancer take specific regimens that are suitable for each type of cancer.
3

MODEL

3.1

Performance Measures and Data Collection

This study aims to identify the best scheduling rules based on the infusion time, which has an influenceon the length of the OCD stay. A discrete event simulation technique is employed to find the optimal appointment schedule, which minimizes the patient waiting-time while maintaining the utilization of resources as high as possible under an actual therapeutic environment. Hence, three indicators are used toanalyze the performance.
1. Patients’ waiting-time in the medical process
2. Patients’ total time in the medical process
3. Utilization of beds : the average percent occupancy of the total beds available
Data were collected via interviewing nursing administrators, reviewing medical records and conducting a time-motion study. The number of patients treated in a week, the diagnosis of each patient, thechemotherapy regimens (treatment plans that specify the dosage, the schedule and the duration of treatment) for each patient, the intravenous dripping time for each infusion solution that was blended withspecific anti-cancer drugs, the percentage of cancelled treatments, and the staffing and sequence of thetreatment process were collected. Continuous time-motion observations of medical activity were performed to record the actions, location, start time and completion time for each action. The subjects of thetime-motion study were 3 OCD nurses, 2 pharmacists and a porter. The resultant data were used to extract
the process time in the OCD treatment.
The patients were divided into 9 categories according to the type of cancer because the infusion timefor each type of cancer patient was assumed to have a unique distribution. In general, some specific set of
medications called regimens are prescribed based on the type of cancer being treated. As the beds in the
OCD are taken, at least for the administration time of the infusion assigned by the prescription for each of
the patients, the length of the OCD stay is assumed to depend primarily on the administration time of the
drug infusion. The probability distributions of the infusion times were estimated by using the data of the
medical record. The patient types, the percentage of each patient type and the delay time for the infusion
DIV for each cancer type are summarized in Table 1.
Nursing activities were divided into direct services, in which the nurses provided care for the patients
directly based on the medical process, and indirect services that were conducted as background jobs, e.g.,recording, preparation for the treatment procedure, information exchange, or walking from one place toanother. Using time-motion study data, probability distributions were estimated for both types of services.
The indirect services can be detected with the frequency of occurrence for each time span (Table 2). The
direct services, listed according to the medical process and the delay time for each step, are shown in Table 3.

910

Yokouchi, Aoki, Sang, Zhao, and Takakuwa
Table 1: Patient Types and Parameters
Patient Type

Numberper Day

Proportion%

Delay Timefor DIV Infusion (Min.)

1.26
3.50
1.54
1.40
0.28
7.27
2.52
1.40
0.84
20

6.3
17.5
7.7
7.0
1.4
36.4
12.6
7.0
4.2
100

TRIA(122, 206, 541)
TRIA(91, 185, 311)
TRIA(108,174,212)
TRIA(32, 140, 242)
TRIA(182, 187, 191)
TRIA(1, 93, 290)
TRIA(122, 170, 322)
TRIA(122, 171, 212)
TRIA(30, 140, 290)

Gastric Cancer
Colorectal Cancer
Liver / Cholecyst Cancer
Pancreas Cancer
Kidney Cancer
Breast Cancer
Gynecologic Cancer
Lung Cancer
Other Cancer
Sum
TRIA = Triangular

Process Name
RecordMoving
Preparation
Informationexchange
Informationcollection
Observation
Patient care
Toilets
assistance

Table 2: Indirect Operation Process and Delay Time
Frequency
Delay Time
(Min.)T8
T9 T10 T11 T12 T13 T14
TRIA(0.12,0.86,11.03)
TRIA(0.02,0.07,3.13)
TRIA(0.02,0.62,20.47)
TRIA(0.05,0.36,4.9)
TRIA(0.02,0.46,10.47)
TRIA(0.05,0.4,3.65)
TRIA
0.18,0.27,2.43)
TRIA(0.13,0.269,1.52)

T15

T16

T17

0

1

5

3

5

2

6

4

2

3

15

62

50

46

36

38

52

63

22

35

8

17

18

15

5

6

21

20

19

27

2

2

6

7

1

3

7

2

1

3

3

11

1

11

9

8

6

6

4

7

2

11

5

7

5

3

6

7

4

2

1

2

2

3

2

0

1

2

0

1

0

0

0

1

0

1

3

1

0

0

TRIA = Triangular, T8 = Time 8, T17 = Time 17
3.2

Simulation Model

A discrete event simulation model of the OCD was created using the simulation package Arena version
13.0 (Kelton, et al. 2010). In addition to the main patient flow, indirect nursing services were included in
the model, such that the sequence of the medical process was substantially influenced by the nursing activity in the OCD. The estimated probability distributions were used in all of the processes of the patientsflow and in indirect nursing services. Primary resources for the simulation were the 10 beds, with 1 doctor

911

Yokouchi, Aoki, Sang, Zhao, and Takakuwa
implementing vascular access and managing adverse events, 3 nurses in charge of the OCD, 2 pharmacists and 1 porter.
Table 3: Direct Operation Process and Delay time with Frequency
SectionOutpatient
Department

OCD

Pharmacy

Process Name

Delay Time (Min.)

Moving time to blood test room
Blood exam
Waiting for blood test result
Moving time to chemo room
OCD receptionist
Bed assignment
Receive drug
Call for OCD Dr
Preperation for vascular access
Vascular accessPreparation for premedication
Premedication
Set infusion
Finish up treatment
Billing process
Moving time to payment
Payment
Receiving call from outpatient department
Drug mixing
Check drug
Hand over drug to pharmacist assistantMoving from Pharmacy to OCD
Hand over drug to OCD Nurse

1.5
TRIA(2,4,7)
TRIA(5,11,51)
2
8+8.94*BETA(0.997,2.17)
LOGN(0.391,0.262)
BETA(0.913,1.044)
LOGN(0.317,0.38)
TRIA(0.42,0.7,6.33)
GAMM(1.32,1.13)
LOGN(1.29,2.09)
WIEB(1.32,1.2)
LOGN(2.13,2.92)
3.43*BETA(0.67,0.577)
0.03+1.97*BETA(0.79,0.736)
3
TRIA(5915)
0.06+0.23*BETA(0.614,0.66)
GAMM(0.816,1.29)
WEIB(0.216,1.07)
0.04+0.11*BETA(0.991,0.939)
LOGN(0.169,0.141)
WEIB(0.559,1.61)

GAMM = Gamma, LOGN = Lognormal, TRIA = Triangular, WEIB = Weibull
The entities of the model were the patients categorized by the types of cancer. The model was a terminating simulation, in the sense that a finite number of patients were scheduled each day within a predetermined time in which the OCD was open.
In our preliminary experimentation, the patients were assigned to a probability of arrival and to theprobability distributions for the infusion time according to the cancer type. The experiments were conducted to investigate the outline of the bed utilization in the OCD under the four conditions described below.
1.

Increasing the number of nurses from 1 to 12 under patients’ arrivals were fixed at 40 (20 in
the AM/ 20 in the PM)
2.
Increasing the number of nurses from 3 to 7 when the patients’ arrivals were fixed at 20 (10in the AM/ 10 in the PM)
3.
Increasing the number of patients from 20 to 40 (half in the AM/ half in the PM) under an operation having 12 nurses.
912

Yokouchi, Aoki, Sang, Zhao, and Takakuwa
4.

Increasing the number of patients from 20 to 31 under an operation with 12 nurses.

The core simulation was conducted to define the optimal scheduling based on the infusion time on a
typical day. In the model, the number of arrivals and the duration of infusion for 20 specific patients were
fixed according to the data. The rules below were applied for patient allocations to the ten OCD beds inthe model, as follows:1.
2.

Listing the types of patients according to the duration of the infusion.
Sorting the list according to an ascending sequence of infusion times: {L(1), L(2),L(3),….L(n)}, where L(1) is the case in which the planned infusion time is the shortest and L(n)is the case with the longest planned infusion time.
3.
Allocating patients arriving at the OCD for 10 beds as follows: { L(1), L(2), L(3),…L(10)}4.
Allocating subsequences as follows:{L(n), L(n-1), L(n-2),… }With respect to the arrival time, we determined that the previous 10 patients arrived at the beginningof the day. The subsequent 10 arrivals occurred at times that were generated by subtracting the estimatedmean time for pre-OCD procedures (including the general reception for checking in at the OCD) from thetimes at which the infusions for previous patients were completed (Table 4). The times to complete theinfusions were determined based on the estimated distributions. This method was employed to identify
the optimum subsequent arrivals, for both reducing the patients’ waiting-time and increasing the bed utilization.
1. Using the probability distributions of the time to completion for the infusion, which was generated in the original model.
2. Varying the times of the secondary patients’ arrivals according to the probability distributions
for completing the infusion in the 5th to 95th percentile range
The design and analysis of the simulation model were adjusted and modified again by experts in industrial engineering, through many collaborative meetings. One hundred replications were simulated in
all of the cases described above. The differences among the conditions were tested for statistical significance using a t-test, with P < 0.05 considered to be statistically significant.
Table 4: Distribution of the time to complete DIV infusion
Bed

Distribution

1 NORM(67.5, 19.1)
2 NORM(104, 20.1)
3 NORM(145, 16.6)
4
NORM(169, 15)
5 NORM(186, 15.9)
6 NORM(204, 18.2)
7 NORM(230, 23.9)
8 NORM(261, 25.1)
9 NORM(283, 22.2)
10 NORM(305, 17.1)
NORM = Normal

Mean

±SD

9:07:30 19.2
9:44:00 202.2
10:25:00 16.6
10:49:00 15.0
11:06:00 15.9
11:24:00 18.3
11:50:00 24.0
12:21:00 25.2
12:43:00 22.3
13:05:00 17.2

Min

Max

5th
percentile

50th
percentile

95th
percentile

8:38:54
8:58:30
9:51:00
10:16:00
10:35:00
10:46:00
11:04:00
11:22:00
11:33:00
12:09:00

10:14:00
10:28:00
11:06:00
11:22:00
11:51:00
12:20:00
12:47:00
13:11:00
13:19:00
13:38:00

8:36:05
9:10:56
9:57:42
10:24:19
10:39:51
10:54:04
11:10:41
11:39:43
12:06:29
12:36:52

9:07:30
9:44:00
10:25:00
10:49:00
11:06:00
11:24:00
11:50:00
12:21:00
12:43:00
13:05:00

9:38:55
10:17:04
10:52:18
11:13:40
11:32:09
11:53:56
12:29:19
13:02:17
13:19:31
13:33:08913

Yokouchi, Aoki, Sang, Zhao, and Takakuwa
4

SIMULATION RESULTS

4.1

Evaluation of Bed Utilization in Preliminary Experimentation

Figure 2 shows the differences in bed utilizations that occur with increasing numbers of nurses, under thecondition that the number of patients is fixed at 40. The utilization of the beds tended to increase underoperations that involved 1 to 7 nurses. There was a significant difference only in bed utilization between
the conditions of 1 nurse and 2 nurses (t=-3.448, p=.003). The results indicate that bed utilization showeda remarkable decrease in an operation having less than 2 nurses, who would be under the stress of attending to 40 patients. The bed utilization, however, showed no significant differences among the conditionsof 3 to 7 nurses with an operation involving 20 patients (Figure 3). With respect to bed utilization, it
would be adequate to operate with 3 nurses in the facility.

Figure 2: The impact of the number of nurses on
the bed utilization when the number of patients isfixed at 40 (AM 20 / PM 20)

Figure 3: The impact of the number of nurses on
the bed utilization when the number of patients isfixed at 20

Figure 4: The impact of patient arrivals (AM/PM)on the bed utilization when the number of nurses
is fixed at 12

Figure 5: The impact of the number of patients on
the bed Utilization when the number of nurses
is fixed at 12

The impact of the number of patients on bed utilization is indicated in Figure 4. The condition inwhich 30 patients (15 in the AM/ 15 in the PM) were taken care of by 12 nurses showed a significant increase in bed utilization compared to a situation in which 20 patients were being cared for (t=-20.184,
p<.000). Figure 5 shows the differences in bed utilization when there is an increasing number of patients(from 20 to 31) under an operation involving 12 nurses. There are significant differences in bed utilization
under the condition of 20 patients compared to that of 23 patients (t=-14.972, p<.000) and 23 patients914

Yokouchi, Aoki, Sang, Zhao, and Takakuwa
compared to 25 (t=-4.610, p<.000). The capacity of treatment would be assumed to be between 25 and 30
cases in this facility, even if the nursing resources in place were more than adequate.
4.2

Impact of ASs on the Service Time and Bed Utilization

The distribution of the time for 10 patients to complete their infusions is shown in Table 4, with estimatedrepresentative values. Figure 6 shows a gradual decrease in the measure of performance, the bed utilization, the waiting-time and the total time. The mean waiting-time of all 20 patients treated in a single day
was 143.30 (min) for the 5th percentile of the distribution and 80.26 (min) for the 95th percentile. A waiting-time of over 2 hours in the treatment process would impose a severe strain on the patients from a clinical point of view. Although it is difficult to determine the latitude of the waiting-time in the treatment
processes, it should be at least under 2 hours of wasted time. Setting the appointments for 2nd round patients at the time shown in the 40th percentile would be assuming to maintain the waiting-time at under 2hours in this system. The mean utilization of 10 beds shows 0.67 under the condition of using the 5th percentile of the distribution and 0.57 when using the 95th percentile. If the appointment would be set above
the 40th percentile of the distribution, then the utilization would decrease to under 0.6. The time of the40th percentile of the distribution is limited to keeping the utilization above 0.6 in the OCD.
Figure 7 shows the changes in the proportions of the waiting-time out of the total time (WT/TT) andthe means of utilization. In a clinical setting, WT/TT is expected to have an influence on the patient satisfaction, which would be similar to the influence of the waiting-time. The WT/TT was 0.40 at the 35th
percentile and 0.34 at the 40th percentile, while it was 0.49 at the 5th percentile and 0.28 at the 95th percentile.

Figure 6: Service time and bed utilization

Figure 7: Bed utilization and WT/TT

The time for the 40th percentile of the distribution would be a convincing boundary to use for holding the WT/TT above 40%, with a tolerance for both the utilization and the waiting-time itself in this system. The last patient treated in the day checked out at 16:14, which was a moderate level among the conditions and allowed plenty of time for closing, with the condition of using the 40th percentile (Figure 8).

Figure 8: Check out time of last patient915

Yokouchi, Aoki, Sang, Zhao, and Takakuwa
Consequently, it is assumed that optimal appointments for 2nd round patients are scheduled based on
the 40th percentile of the distribution of the time for previous patients to complete their infusions. This
scheduling could maintain the utilization at 0.6 while restraining the waiting-time to under 2 hours andthe WT/TT ratio to less than 0.4.
5

CONCLUSIONS

A simulation model of an outpatient chemotherapy department at a general hospital is constructed, to explore appointment scheduling based on the properties of the treatments. The results from preliminary experimentation show that the existing system has a treatment capacity of 30 patients under an operation involving 3 nurses with respect to bed utilization. The experimentation processes concentrated on theappointments for second treatment rounds over a single day and aimed to identify optimal scheduling,which shortens the mean of the waiting-time for all of the patients treated while maintaining the same bed
utilization.
The results show that each performance measure has a tendency to decrease with later times of arrival, which are determined while accounting for the infusion time of previous patients. It was suggested
that using the 40th percentile of the distribution for scheduling lowered the waiting-time while maintaining the bed utilization within a clinically permissible range during a typical day in the OCD.
Although the clinical relevance of the acceptable range for the waiting-times in an outpatient chemotherapy clinic requires more discussion, this scheduling rule can be made available for clinical management. First, this appointment scheduling rule could reflect the distinctions in outpatient chemotherapy forwhich patients receive infusions in bed during a designated period according to a prescribed regimen. Second, the rule is easily adapted to a clinical setting by using the pre-procedure time and the total infusion
time of each type of cancer patient.
ACKNOWLEDGMENTS
The authors wish to express sincere gratitude to the staff of the Kohnan Hospital Group. This research
was supported by “Grant-in-Aid for Scientific Research (C) (23593129)” of Japan Society for the Promotion of Science (JSPS) .
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AUTHOR BIOGRAPHIESMITSUKO YOKOUCHI is an associate professor in Department of Nursing, Graduate School of Medicine at Nagoya University in Japan. She pursued a B. Sc degree in human sciences from Toyo Eiwa University and a M.Sc. degree in nursing science from Tokyo Medical and Dental University. Her Ph.D. is innursing science from Osaka University. She has been certified as a Registered Nurse in Japan. Her research interests include simulation in health care, health care management and clinical nursing. Her email address is yokouchi@met.nagoya-u.ac.jp.
SETSUKO AOKI is a general manager in Department of Nursing at Kohnan Hospital Group, which includes three general hospitals, a geriatric health services facility and a home care agency. She received aB. Sc degree in human sciences from Toyo Eiwa University and a M.Sc. degree in nursing science from
University of Hyogo. She has been certified as a Registered Nurse in Japan. She has been serving concurrently as a lecturer of management course for nursing at Japanese Nursing Association. Her research interests include simulation in health care, nursing management and clinical nursing. Her e-mail address isro_s.aoki@kohnan.or.jp.
HAI XIA SANG is a Ph.D. candidate in the graduate School of Economics and Business Administration
at Nagoya University in Japan. She received her B.Sc. and M. Sc. Degree in Economics from the Asahi
University of Japan in 2005 and 2007. Her research interests include retail store management using simulation technology. Her email is sanghaixia7861@hotmail.co.jp.
RUN ZHAO is a Ph.D. candidate at the Graduate School of Economics and Business Administration at
Nagoya University in Japan. He received his B. Sc. degree in Industrial Engineering from China Universi-

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ty of Mining And Technology in 2006, and received his M. Sc. degrees in Mechanical Engineering (Industrial Engineering) from Chongqing University in 2009. His research interests include the optimizationof manufacturing and work-in-process inventory systems using SIMAN and Simulation with ARENA,
and Green Production Mode. His email address is zhaorun1982@hotmail.com.
SOEMON TAKAKUWA is a Professor at the Graduate School of Economics and Business Administration at Nagoya University in Japan. He received his B. Sc. and M. Sc. degrees in industrial engineeringfrom the Nagoya Institute of Technology in 1975 and the Tokyo Institute of Technology in 1977, respectively. He obtained a Ph.D. in industrial engineering from Pennsylvania State University. He holds a Doctorate of Economics form Nagoya University. He holds a P.E. in Industrial Engineering. His research interests include the optimization of manufacturing and logistics systems, management information systems
and simulation analysis on these systems in the context of hospitals. He prepared the Japanese editions ofboth Introduction to simulation using SIMAN and Simulation with ARENA. He serves concurrently as
the senior staff of Department of Hospital Management Strategy and Planning at Nagoya University Hospital. His email address is takakuwa@soec.nagoya-u.ac.jp.

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