Policy Analysis with Simulation: Centralization of Blood Supply

Chain

Burcu Cansu İnanç, Niousha Karimi Dastjerd, Emre Anıl Kakillioğlu and Nilgün Fescioğlu Ünver

Department of Industrial Engineering, TOBB University of Economics and Technology, Ankara, Turkey

Keywords: Blood Supply Chain, Centralization, Simulation Modeling.

Abstract: Incessant increase of human population and environmental factors increase patients’ need for blood, so

research on this topic is a crucial necessity. A blood supply chain starts with the donation of blood and ends

with transfusion to patient, and configuring blood supply chains as a whole has become a major requirement.

In this research, blood supply chain system centralization and decentralization policies are compared for a

specific setting in Turkey. Effects of these policies on performance measures like number of expired blood

products in regional blood bank and, in each hospital and total cost of the blood supply chain system are

observed using simulation modeling. Results show that under the given conditions the best policy is main-

taining a decentralized blood supply policy which leads to a lower blood expiration rate and lower total cost.

1 INTRODUCTION

The incessant increase of the population and

environmental factors results in the appearance of

some health issues. Some of these issues are taken

care through ambulatory treatment and some others

are treated through inpatient care (admitted to the

hospital). Blood is a human fluid which delivers vital

substances such as nutrients and oxygen to the body

cells and transports metabolic waste away from same

cells. The inpatients may need blood supply from

outsources due to operations and blood loss. Blood is

composed of many different kinds of components and

patients may need one or more of these component(s)

depending the treatment. In general, blood is

collected as a whole in donation centers or in

hospitals and, after donation, blood is decomposed

into different components such as red blood cells,

platelets and fresh frozen plasma which are

perishable. Hospitals may prefer to supply their

demand by their own production, if they have

necessary infrastructure, or by regional blood banks.

In hospitals, blood inventory levels of blood products

are monitored based on a determined period and when

they reach to the determined base stock level new

replenishments from the regional blood centers are

done. Orders are delivered to the hospitals at the

beginning of the day in which the orders are made. In

cases, which demanded blood products do not match

the existing blood groups and characteristics in stock,

hospitals make effort to afford the blood product. In

such a situation, hospitals first search for the proper

blood product in the regional blood bank and nearby

hospitals, if it is not found, they try to satisfy the

demand by letting donations occur.

As the process of supplying blood at the right time is

extremely vital on patients' side, blood supply chain

network and blood center locations play an important

role in efficient demand satisfaction as well as

economic scale. There are many decisions that are

effective in the pursuit of these objectives. One of the

major decisions is centralizing the blood supply chain

or decentralizing the blood supply chain by hospital

based blood banks which affects objectives with a

huge impact.

In Turkey, according to the new law about the

national blood banking system, the blood demand

from all hospitals is satisfied from Turkish Red

Crescent Society (TRCS). None of the hospitals are

authorized to collect blood donations by themselves

anymore though it costs more to supply blood

products from TRCS. Neither the tests for patient and

product match can be done by hospitals. Centralizing

blood supply chain with TRCS has positive and

negative effects as other decisions. For instance,

judging from hospitals point supplying blood

products from TRCS costs more than producing these

products by their own production. On the other hand,

in the centralized system all donations can be

collected at a single location and production process

can be standardized.

Inanc, B., Dastjerd, N., Kakillio

glu, E. and Ünver, N.

Policy Analysis with Simulation: Centralization of Blood Supply Chain.

DOI: 10.5220/0006404100560064

In Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2017), pages 56-64

ISBN: 978-989-758-265-3

Existing literature on blood supply chain is generally

focused on different main parts of the supply chain

with different purposes and includes different levels

of detail. However, each part and each property of the

supply chain effects objectives which exist in the

literature, therefore the supply chain must be taken

into account from donation to transfusion as a whole

with sufficient details. Besides, different scenario

analyzes exist in literature but a strategic decision that

will affect every aspect of the system like

centralization has not been analyzed from a broad

perspective. Different versions of this scenario

analysis exist in literature but blood products and

blood groups were not included and system was not

modelled from donation to transfusion as a whole.

In this research, the impact of centralization and

decentralization policies on blood supply chain

network's total cost and blood wastages/expired blood

products are investigated. Points that were not

considered in previous studies in the literature are

taken into account and in this research. For this

purpose, each blood product and blood group is

included in the blood supply chain network which is

modelled from donation to transfusion as a whole.

In addition, blood supply chain structure, standards

and policies vary for different countries and locations

and in Turkey or in a country with similar

infrastructure, no similar research exists.

In order to make the analysis on a realistic testbed,

data is collected from TRCS and own blood banks of

the hospitals in Ankara, and a simulation model is

generated based on this information.

This paper is organized as follows: Section two

presents existing work in literature, Section three

defines the problem and the method. The fourth

section includes the numerical results and, in the last

section conclusion and suggestions for future work

are presented.

2 LITERATURE

Existing literature which are related to the problem

are investigated and categorized into two groups as

studies handling blood supply chain issues and

policies by means of simulation methods and studies

considering blood as a perishable good and tackling

the issues occurred in blood supply chain.

Starting from the first category, Cohen and Pierskalla

(1975) considers management strategies for a

regional blood bank. The research focuses on the

impact of centralized and decentralized control and

also various scenarios on the transfused blood,

shortages, number and percentage of the outdated

blood. In Özgen (2007), three echelon supply chain

configurations which has been defined in the new

Turkish Law of Blood and Blood products has been

considered for regional blood center in Antalya.

Katsaliaki and Brailsford (2007) is a case study of a

blood center of a hospital which is located in United

Kingdom. This research aims minimizing the wastage

and shortages in blood supply chains. In Beliën and

Forcé (2012) all the researches of blood supply chain

till 2011 are categorized based on the modeling and

solution approaches they used and the ideas for future

works are presented. In Katsaliaki (2008) blood

donation in blood bank, donated bloods' storage,

blood distribution systems, hospitals' ordering,

stocking, blood- component user's performance and

costs are analyzed based on the data gathered from

UK blood bank and hospitals where the donations

take place. Zahraee et al. (2015) aim to increase the

efficiency of the blood supply chain by applying the

dynamic simulation and Taguchi method and monitor

the resulted change on simulation software. In the

work done by Baesler et al. (2014) the focus is on

storage of blood components. In this paper, process

between blood donations till its delivery is analyzed.

In Rytilä and Spens (2006) the increasing importance

of the blood supply chains and the strategic decisions

which can be made in order to afford the most

efficient blood utilization is negotiated. Different

inventory strategies, different transportation methods,

different ranking approaches and different production

control approaches are considered. Kamp et al. (2010)

considers blood availability and management in case

of epidemic diseases such as H5N1 and H1N1 instead

of considering the optimal inventory levels. In Pegels

et al. (1977) four different scenarios/strategies are

compared. The scenarios applied to the simulation

model can be listed as utilization of frozen blood

cells, utilization of techniques for extending blood

and blood components shell lives, application of

improved donation strategies and utilization of

improved inventory strategies. Simonetti et al. (2014)

aim at investigating the impact of various blood

management policies on the availability of the blood

and its distribution in USA. The utilized model

simulates the impact of different blood management

applications which are used in blood transfusion on

the demand. In Osorio et al. (2016) simulation is used

to support the strategic and operational decisions

made in production planning. Discrete event

simulation is used to show the supply chain flows

incorporating collection, production, stocking and

distribution. This research is a case study taken place

in Colombia and is based on the real-world data taken

from the blood bank. In J. Blake et al. (2015) a

Policy Analysis with Simulation: Centralization of Blood Supply Chain

simulation study is used to assess the network

performance when distinct blood centers are

consolidated into one unique blood center located in

Calgary, Alberta. Yuzgec et al. (2013) has suggested

a simulation model of blood network operations for

determining the inventory levels and distribution plan

of specific region having a regionalized blood

banking system. Vlachos et al. (2012) has considered

a methodology to estimate the required inventory

level of umbilical cord blood units in Greece. The

paper aims at ensuring an adequate probability that a

Greek patient find a Human Leukocyte Antigen. J.

Blake and Hardy (2013) have again studied the

impact that consolidation of blood production and

distribution facilities have on customer service. Xu et

al. (2013) has suggested a three-dimensional

simulation model which simulates the deformation

and aggregation of red blood cells by coupling the

interactions between the fluid and the deformable

solid membrane of the red blood cells using

continuum mechanics. In Onggo (2014) the elements

of a hybrid simulation model are explained using a

case study of blood supply chain simulation model for

low and middle-income countries that has different

characteristics and challenges in comparison to the

typical blood supply chain in high-income countries.

Baesler et al. (2014) have used a discrete event

simulation model for analyzing and proposing

inventory policies to a regional blood center. Haijema

et al. (2009) have developed a new approach which

combines stochastic dynamic programming and

simulation to provide practical order up to rules that

are nearly optimal formal theoretical support for

blood platelets. The proposed approach has been

applied to a Dutch regional blood bank. Mustafee et

al. (2009) has described an investigation into using

conventional and distributed approaches to

simulating the supply chain of blood from the UK

Southampton NBS Center to hospitals in this area.

Blake and McTaggart (2016) used simulation for

resolving the impacts of modernization configure-

tions on blood distribution network of Canadian

Blood Services.

The product considered in this research is known to

be highly perishable and due to this characteristic, the

second category includes the literature for existing

papers on the perishable inventory theory.

Sonnendecker and Millard (1960) are the researchers

who have suggested the application of general

inventory control methods and policies to the blood

supply chain and in this way the perishable inventory

theories has been applied to blood supply chain

problems. In Kopach et al. (2008) focus is on meeting

the trade-off between different demand levels

(emergency and ad-hoc), service levels, costs and also

the classic shortage and wastage minimizing

objective function factors by revising a queuing

model with utilization of cross level techniques.

Stanger et al. (2012) offered the best application that

minimizes the wastage occurring due to expiration of

the normal blood inventory holding durations. In this

paper, main focus is on the management of red blood

cells stock management in hospitals' transfusion

laboratories. In J. T. Blake et al. (2003) the methods

which can be used in platelet suppliers' regional

inventory ordering policies are discussed. In Hardy

(2015) four simulation models are used to evaluate

the impact of a reduced red blood cell shelf life on

outdate, shortage, and emergency order rates.

Literature analysis shows that blood products and

blood groups were not included and system was not

modelled from donation to transfusion as a whole in

previous researches despite their crucial effects on

objectives.

3 METHOD

In this section the functioning blood supply chain in

Ankara, centralization scenario and decentralization

scenario is summarized and then the simulation

model established is described in detail.

3.1 Problem Summary

In Turkey, a centralized blood bank system is used

and TRCS supplies blood products to hospitals

regardless of hospitals’ capabilities. In this research

the performance of centralized and decentralized

regional blood supply chain policies are compared

and the objective is to monitor the changes occurring

in number of expired blood products of hospitals and

TRCS and total cost of the system under centralized

and decentralized scenarios.

In this research, there are two types of hospitals: Type

1 and Type 2. Hospital types are determined based on

their ability to afford their own blood when

demanded. According to the investigation of this

subject, some large and facilitated hospitals which are

referred as Type 1 hospitals own their own blood

bank infrastructure and some smaller hospitals which

are referred as Type 2 hospitals need to satisfy their

demand from the regional blood bank.

The supply chain performance is assessed under two

different policy scenarios: Central supply chain

policy and decentral supply chain policy. In central

supply chain policy, all demands of both hospital

types are satisfied from the TRCS which plays the

SIMULTECH 2017 - 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

Figure 1: Centralized blood supply chain - Step1: within TRCS from donation to transportation - Step2: within hospitals from

storage to transfusion.

role of regional blood bank. TRCS collects, tests and

processes all blood donations within the city.

Hospitals keep an inventory and this inventory is

replenished by TRCS daily. The base stock level of

each hospital is determined by TRCS. When there is

a blood product demand, the hospital inventory is

checked, if it is not available in the hospital TRCS is

checked, if there is no blood product of the requested

type in TRCS banks, other hospitals are checked.

Also, blood cross match tests are done by TRCS.

Centralized blood supply chain is generalized into

two steps in Figure 1, the first step illustrates flow

from donation to TRCS storage and the second step

illustrates flow from hospital storage to transfusion.

In the decentralized supply chain policy, Type 1

hospitals produce their own blood components but

Type 2 hospitals still order from TRCS. In decentral

system, Type 1 hospitals collect, test and process their

own blood inventory. Type 2 hospitals order their

blood from TRCS and their inventory is replenished

daily by TRCS. If the required blood product is not

available in the inventory of a Type 1 hospital,

hospital first asks other Type-1 hospitals, if blood is

not available in other hospitals they ask TRCS. If the

hospital is a Type 2, they ask TRCS when the required

blood type is not in their inventory.

Outdated blood disposal is a crucial operation both at

TRCS and at hospitals. Both decentral and central

systems need to check their inventory periodically for

determining expired products. Blood and all its

components are perishable products which have

different shelf lives. To illustrate, red blood cells can

stay alive for a 42-day period of time, frozen plasma

cells can stay alive for 2 years while platelets live for

5 to 7 days, once the facility that collected them has

completed their tests. The responsible personnel

check the blood products' expiration date and dispose

the ones which are outdated.

In blood processing centers at first each donor’s

suitability is tested, if a donor is not suitable (2%

probability) the donation is disposed; otherwise the

blood is divided into its components and sent to

appropriate storages. In addition to the division of

blood to different components, blood can be

Policy Analysis with Simulation: Centralization of Blood Supply Chain

categorized into 8 subgroups based on blood groups

which have varying frequencies from population to

population. Blood demands of patients can be

satisfied in case that the blood group and other blood

characteristics match. These demands are satisfied

from hospitals' blood stocks, so it is crucial to keep

sufficient stock in hospitals.

Preparation process varies between different blood

products. RBC demands are prepared one day prior to

transfusion but when the time comes for transfusion

the product may not be needed anymore with 60%

probability. If RBC is not used product can be

returned to the relevant storage. Plasma demands are

prepared instantly with a 20 minutes defrosting

process. PLT demands are prepared instantly and it

does not require any special process. In contrary to

the red blood cells case all other products are disposed

if they are not used when being demanded.

Typically, before transfusion, the red blood cells have

to pass the cross-match tests which determine the

blood availability for a specific patient. In this

research, cross match tests are ignored. The ratio of

the blood which is considered as waste as a result of

the cross-match test is approximately 1% - 2% which

is a negligible percentage in comparison to other

components in the system. This 1% - 2% waste will

not affect the internal circulations of the system,

number of wasted blood and total cost by a significant

amount.

The blood donation amount is highly stochastic due

to its nature. Sometimes the natural disasters impulse

the empathy of human beings and the donation ratios

grow up sharply while in some other cases considered

as normal cases these ratios fall down. In addition to

donation ratios, the demand amounts and frequencies

are also stochastic values and to the best of the

knowledge one cannot say the demand would be

exactly equal to some amount on a specific day. In

order to study this highly stochastic system a

simulation approach is preferred. The simulation

model is used to assess the number of unsatisfied

demand due to blood shortages, wastages and also the

total system cost under different scenarios and

different donation levels.

3.2 Simulation Model

Ankara, Turkey is taken as the base of our simulation

model. The data set used in this research is gathered

from different resources. The information about the

demand values and process durations are collected

from a Type 1 Hospital’s annual reports that contain

daily amount of demands for a year. This demand

data contains the data for all the possible blood

products which includes blood groups. The process

durations, TRCS prices and blood product production

costs are taken based on the information taken from

experts. Ratios of blood group demands are adopted

from TRCS's statistics. In Ankara, hospitals to meet

their demand by their own stocks, is a more expected

situation which is approved by experts. In other

words, usually there is no need for hospitals to search

blood products from different sources, stocks can

usually meet demand. In addition, in Ankara Type 1

hospitals are located close to each other which make

the transfer time between Type 1 hospitals short

enough to not affect the expiration time of a blood

product. Therefore, in the decentral case Type-1

hospitals share one depot and Type-2 hospitals share

another depot (TRCS depot). Information about the

number of donations per day is adopted from TRCSs'

reports which contain the monthly donation numbers

for a period of 6 years. The distribution of daily

donation amounts is generated through this monthly

data and expert opinion. Ratios of blood group

donations are adopted from TRCS's statistics. In

addition to the data mentioned above, the blood bank

process durations are utilized which were presented

in Baesler et al. (2014). Blood product production

process distributions are generalized with the

generated data from the simulation model which is

established with process distributions presented in

Baesler et al. (2014).

The simulation model is developed in ARENA 14.0

and solved based on the real-life data gathered from

different resources as illustrated in Section 3.1.

The simulation model of centralized blood supply

chain network is shown in Figure 2 below. The

simulation model starts with donations, and then each

donation is transferred from donation points to

regional TRCS center where biological tests and

centrifuge occurs. Because of different processing

needs each blood product is modeled with a sub-

model and the sub-model of RBC is shown in Figure

3 below. Also in the simulation model, each blood

type of each product is stored in a different storage,

and for daily replenishments and shortages products

are transferred from TRCS to hospital storages where

they are stored in the same way. When demand

occurs, it is tried to be matched if there is enough

stock at hospital’s storage. If there is not enough

product with demanded properties, other storages,

TRCS and other hospitals’ storages, are investigated.

If demand can be matched with the requested blood

product transfusion process begins, and each

transfusion process is modeled with a sub-model.

SIMULTECH 2017 - 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

Figure 2: Simulation model for centralized system.

Figure 3: Sub-model for producing RBC.

Figure 4: Sub-model for RBC transfusion.

Sub-model of RBC’s transfusion process is

shown in Figure 4.

The blood donation data gathered from TRCS only

includes the donations which are from central

Ankara. Computations show that central Ankara

donations can only satisfy %50 of the demand for

each blood component. The results of the simulation

model also confirm this fact. However in reality,

TRCS also gathers additional donations which come

from the close provinces. Only with the addition of

these donations, Ankara can satisfy its blood demand

completely.

For further analysis of the system multiple levels of

donation ratios (donation/demand) are considered

which represent the cases where the demand increase

or donations decrease. For each scenario donation

rates are gradually increased to fulfill the demand and

its effects to objectives are observed. Hourly donation

ratios (donation/demand within the same hour) are

taken as different percentages that are (100%, 87.5%,

75%, 62% and 50%). Expert opinion was obtained in

validation stage and the model showed high face

validity. The model was run for 365 days and 6

replications with a warm of period of 1000 hours.

4 NUMERICAL RESULTS

The performance measures which are assessed

include number of expired products and the total cost

Policy Analysis with Simulation: Centralization of Blood Supply Chain

of the system. In this case study, the demand rate is

kept constant while the donation ratios are changed.

The first performance measure is the blood products

that expired which may mean that the supply chain is

not good structured enough, and it is a problem that

needs to be addressed on. In the decentralized system,

there are less expired blood products than the

centralized system. In the centralized system, Type 1

hospital storages usually contain extra blood product

than needed because of the new emergency stock

legislation. Due to a high amount of stock held until

the blood product is identified by other hospitals and

transfer process is completed, the blood can expire.

Therefore, blood products may expire before being

used and centralization with high amount of base

stocks is not a good scenario for this performance

measure as shown in Figure 5.

The second performance measure is total cost. This

measure is considered for Type 1 hospitals and for the

whole system. TRCS produces blood products with

the same cost as Type 1 hospitals and sells these blood

products with the price of 3 times of its production

cost. That’s why total cost increase for Type 1

hospitals in centralized scenario is expected while

total cost of the system increases as well. The only

factor of total cost is not the blood product

production; there are disposal costs of expired blood

products which is costly because of the type of the

product. As previously shown in the decentralized

system there is less expired blood products than the

centralized system, so total cost of the system is

expected to be less in the decentralized system.

Decentralized scenario gives better results than

centralized scenario in this performance measure

which is shown in Figure 6.

For these performance criteria, the best scenario is the

decentralized one which has lower expiration rate and

lower total cost. So, it is better to have a decentralized

system for Type 1 hospitals which are capable of

supplying their own demand by their laboratories.

Figure 5: Number of expired products of central and decentral systems.

Figure 6: Total cost of central and decentral systems.

0,00

10000,00

20000,00

30000,00

40000,00

50000,00

60000,00

50% 62.5% 75% 87.5% 100%

Expired Products

Donation ratio

Central

Decentral

20000000

40000000

60000000

80000000

100000000

120000000

140000000

160000000

180000000

50% 62.5% 75% 87.5% 100%

Total Cost

Donation ratio

Central

Decentral

SIMULTECH 2017 - 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

5 CONCLUSIONS

Blood supply chain policies become more and more

important nowadays, because of the increasing

population rate, widespread epidemics, disasters and

terrorist attacks which increase the need of blood

products. Therefore, centralization policy and its

effects on system have great importance in terms on

both cost and human life.

In this research, the impact of centralization and

decentralization policies on blood supply chain

network's total cost and blood wastages/expired blood

products are investigated with simulation. Ankara,

Turkey blood supply chain system is used as the base

of the simulation model. With aim of eliminating

deficiencies in previous research each blood product

and blood group is included in the blood supply chain

network which is modelled from donation to

transfusion as a whole

Results showed that decentralized system is better for

performance criteria like, number of expired products

of hospitals and TRCS, and total cost of the

system. Actually, the decentralized scenario is a kind

of a semi-decentralization which is also preferred by

USA. In USA system hospitals, which are capable of

producing blood products from their donations supply

blood to system with Red Cross. With adoption of this

semi-decentralized blood supply chain system, type 1

hospitals’ having idle blood product production

facilities can be prevented. Results also showed that

base stock levels of the hospitals should be

determined carefully in order to the centralized

system work efficiently. The current base stock levels

lead into a greater number of expired products.

For future work, the effects of different base stock

levels on performance measures can be evaluated.

Optimal stock levels of different stock policies can be

determined with a mathematical programming model

and then the impact of centralization and

decentralization policies on blood supply chain

network can be observed with use of the simulation

model which is developed in this research.

REFERENCES

Moore, R., Lopes, J., 1999. Paper templates. In

TEMPLATE’06, 1st International Conference on

Template Production. SCITEPRESS.

Smith, J., 1998. The book, The Publishing Company.

London, 2

edition.

Baesler, F., Nemeth, M., Martínez, C., & Bastías, A.

(2014). Analysis of inventory strategies for blood

components in a regional blood center using process

simulation. Transfusion, 54(2), 323-330.

Beliën, J., & Forcé, H. (2012). Supply chain management

of blood products: A literature review. European

Journal of Operational Research, 217(1), 1-16.

Blake, J., & Hardy, M. (2013). Using simulation to evaluate

a blood supply network in the Canadian maritime

provinces. Journal of Enterprise Information

Management, 26(1/2), 119-134. doi: doi:10.1108/

17410391311289587

Blake, J., McTaggart, K., & Hardy, M. (2015). Modelling a

Blood Distribution Network in the Prairies with a

Generic Simulation Framework. INFOR: Information

Systems and Operational Research, 53(4), 194-210

Blake, J. T., Thompson, S., Smith, S., Anderson, D.,

Arellana, R., & Bernard, D. (2003). Optimizing the

platelet supply chain in Nova Scotia. Paper presented at

the Proceedings of the 29th meeting of the European

Working Group on Operational Research Applied to

Health Services (ORAHS). Prague: European Working

Group on Operational Research Applied to Health

Services.

Cohen, M., & Pierskalla, W. (1975). Management policies

for a regional blood bank. Transfusion, 15(1), 58-67.

Haijema, R., van Dijk, N., van der Wal, J., & Smit Sibinga,

C. (2009). Blood platelet production with breaks:

optimization by SDP and simulation. International

Journal of Production Economics, 121(2), 464-473.

doi:http://dx.doi.org/10.1016/j.ijpe.2006.11.026

Hardy, M. (2015). Simulation of a reduced red blood cell

shelf life.

Kamp, C., Heiden, M., Henseler, O., & Seitz, R. (2010).

Management of blood supplies during an influenza

pandemic. Transfusion, 50(1), 231-239.

Katsaliaki, K. (2008). Cost-effective practices in the blood

service sector. Health policy, 86(2), 276-287.

Katsaliaki, K., & Brailsford, S. C. (2007). Using simulation

to improve the blood supply chain. Journal of the

operational research society, 58(2), 219-227.

Kopach, R., Balcıoğlu, B., & Carter, M. (2008). Tutorial on

constructing a red blood cell inventory management

system with two demand rates. European Journal of

Operational Research, 185(3), 1051-1059.

Mustafee, N., Taylor, S. J., Katsaliaki, K., & Brailsford, S.

(2009). Facilitating the analysis of a UK national blood

service supply chain using distributed simulation.

Simulation, 85(2), 113-128.

Onggo, B. S. (2014). Elements of a hybrid simulation

model: a case study of the blood supply chain in low-

and middle-income countries. Paper presented at the

Proceedings of the 2014 Winter Simulation

Conference, Savannah, Georgia.

Osorio, A. F., Brailsford, S. C., Smith, H. K., Forero-Matiz,

S. P., & Camacho-Rodríguez, B. A. (2016). Simulation-

optimization model for production planning in the

blood supply chain. Health Care Management Science,

1-17. doi:10.1007/s10729-016-9370-6

Özgen, C. (2007). Simulation analysis of the blood supply

chain and a case study. Middle East Technical

University.

Policy Analysis with Simulation: Centralization of Blood Supply Chain

Pegels, C. C., Seagle, J., Cumming, P., Kendall, K., &

Shubsda, J. (1977). An analysis of selected blood

service policy changes. Medical care, 15(2), 147-157.

Rytilä, J. S., & Spens, K. M. (2006). Using simulation to

increase efficiency in blood supply chains.

Management Research News, 29(12), 801-819.

Simonetti, A., Forshee, R. A., Anderson, S. A., &

Walderhaug, M. (2014). A stock-and-flow simulation

model of the US blood supply. Transfusion, 54(3pt2),

828-838. doi:10.1111/trf.12392

Sonnendecker, J. P., & Millard, D. W. (1960). Industrial

Engineering Analyses of a Hospital Blood Laboratory:

Engineering Experiment Station, College of

Engineering, Ohio State University.

Stanger, S. H., Yates, N., Wilding, R., & Cotton, S. (2012).

Blood inventory management: hospital best practice.

Transfusion medicine reviews, 26(2), 153-163.

Vlachos, D., Iakovou, E., Keramydas, C., &

Anagnostopoulos, A. (2012). On the estimation of the

necessary inventory for hellenic public cord blood

banks using simulation. Operational Research, 12(1),

57-68. doi:10.1007/s12351-010-0091-1

Xu, D., Kaliviotis, E., Munjiza, A., Avital, E., Ji, C., &

Williams, J. (2013). Large scale simulation of red blood

cell aggregation in shear flows. Journal of

Biomechanics, 46(11), 1810-1817. doi:http://dx.doi.

org/10.1016/j.jbiomech.2013.05.010

Yuzgec, E., Han, Y., & Nagarur, N. (2013). A Simulation

Model for Blood Supply Chain Systems. Paper

presented at the IIE Annual Conference. Proceedings.

Zahraee, S. M., Rohani, J. M., Firouzi, A., & Shahpanah,

A. (2015). Efficiency improvement of blood supply

chain system using Taguchi method and dynamic

simulation. Procedia Manufacturing, 2, 1-5.

Blake J., McTaggart K. (2016), Using Simulation for

Strategic Blood Supply Chain Design in the Canadian

Prairies, SIMULTECH 2016.

SIMULTECH 2017 - 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications