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HIERARCHICAL FACILITYNETWORK PLANNING MODEL
FOR GLOBAL LOGISTICSNETWORK CONFIGURATIONS
FRANS HENDRY DAVID - BRIAN
INVENTORY THEORY
FALL 2013NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
THE BEST SUPPLY CHAINS AREN'T JUST FAST AND COST-EFFECTIVE.THEY ARE ALSO AGILE ANDADAPTABLE. - HAU L. LEE
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OUTLINE
Introduction
System Specification
Hierarchical Cluster Analysis
Facility Network Configuration
Model Development
Experimental Design & Data Collection
Setting Parameter Analysis of Numerical Study
Numerical Study
Conclusions
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INTRODUCTION
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Network configurations are critical issues in the area of global logistics (GLs) as theydetermine the performance of GLs operational strategies.
Elaborate via integration and classification of corresponding facilities, such as internationalhubs and depots.
Performance of GLs strategies and their functional integration should rely on elaboratenetwork configurations to accomplish the goals of GLs management.
Implementation: DHL, UPS, FedEx and TNT
Challenges for GLs network design:
1. Efficiently coordinating activities of all transnational facilities, is difficult due to thedifferent functional relationships in both the spatial and temporal domains.
GLs operational networks are typically hierarchical, containing different nodes located indifferent network layers, where each node has its own operational goals and problems.
2. Existing models that are suitable for GLs hierarchical network planning are scarce.
Most of previous literature is likely to address the issue of GLs network configurations directlyby mathematical programming, thus solving the induced facility location problems all in onephase without considering the hierarchical and geographic relationships
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PROBLEM FORMULATION
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Paper Study that were used in this paper are divided intothree sections:
Pioneering Work
Algorithm Paper
Technique Paper
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LITERATURE REVIEW (1)
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General logistic network configuration have been addressed in some pioneeringworks:
1. T. Miller, D. Wise, L. Clair, Transport network design and mode choice modeling forautomobile distribution: a case study
Determine best transport mode and rail network location strategy using a mixed integer programmingmodel
2. T.G. Crainic, Service network design in freight transportation
Used Mathematical programming approach for inter-modal service network design to seek for a set ofinterrelated decisions that ensure an optimal allocation and utilization of resources to achieve theeconomic and customer service goals of the company.
3. S. Melkote, M.S. Daskin, Capacitated facility location/network design problems
Formulated a combined facility location/network design problem using a mixed integer programmingapproach, where service capacities of facilities are considered.
4. A. Cakravastia, I.S. Toha, N. Nakamura, A two-stage model for the design of supply chainnetworks
Developed an analytical model of the supplier selection process in designing a supply chain network,where the capacity constraints associated with potential suppliers are considered in the supplierselection process
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5. V. Jayaraman, A. Ross, A simulated annealing methodology to distribution networkdesign and management.
Proposed the Production, Logistics, Outbound, Transportation (PLOT) distribution networkdesign system, which was characterized by functions of multiple distribution channelmembers and their corresponding locations
6. Z. Drezner, G.O. Wesolowsky, Network design: selection and design of links and
facility location
Introduced a novel network design problem which determines the links and facilitylocations, using several heuristic solution tools such as a descent algorithm, simulatedannealing, tabu search, and a genetic algorithm.
7. D. Ambrosino, M.G. Scutella, Distribution network design: new problems andrelated models
Solved some complex distribution network design problems, which involve facility location,warehousing, transportation and inventory decisions
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Few hierarchical network design studies have used algorithm:
8. J.R. Current, C.S. Revelle, J.L. Cohon, The hierarchical design problem
Formulated a hierarchical network design problem (HNDP) for identifying the shortestpaths among facilities in a proposed two-level hierarchical network.
9. N.G.F. Sancho, The hierarchical network design problem with multiple primary
paths Developed a dynamic programming model to find a suboptimal solution for the HNDP with
multiple primary paths. For all hierarchical network characteristic, the model still stressesthe algorithm improvement to search better optimal solution in the proposed model
10. C.C. Lin, S.H. Chen, The hierarchical network design problem for time-definiteexpress common carriers
Utilized a time-constrained hierarchical hub-and-spoke network to determine fleet size andschedules on primary and secondary routes to minimize total operating cost while meetingthe desired service level.
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Related technique paper:11. Y.W. Wan, R.K. Cheung, J. Liu, J.H. Tong, Warehouse location problems for
airfreight forwarders: a challenge created by the airport relocation
12. C.C. Lin, Y.J. Lin, D.Y. Lin, The economic effects of center-to-center directs on hub-and-spoke for air express common carriers
13. M. Wasner, G. Zapfel, An integrated multi-depot hub-location vehicle routingmodel for network planning of parcel service
14. A.A. Chaves, L. Lan, Hybrid algorithms with detection of promising areas for theprize collecting traveling salesman problem
15. J.-B. Sheu, A novel dynamic resource allocation model for demand-responsivecity logistics distribution operations
16. K.-M. Osei-Bryson, T.R. Inniss, A hybrid clustering algorithm17. A. Nagurney, D. Matsypura, Global supply chain network dynamics with
multicriteria decision-making under risk and uncertainty
18. B. Groothedde, C. Ruijgrik, L. Tavasszy, Towards collaborative, intermodal hubnetworks: a case study in the fast moving consumer goods market
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Main weakness for previous literature research: Studies regarding hierarchical GLs network configurations are rare
Limited to the scope of domestic logistics, and thus, issues of global logistics and influencingfactors characterizing uncertainties of transnational logistics activities remain unsolved
This paper presents a novel planning methodology for hierarchical GLs network
that integrates cluster analysis and integer programming to solve the GLs networkdesign problem.
In short, this study contains:
1. Hierarchical clustering to partition the demand dataset into a meaningful set of mutuallyexclusive hierarchical clusters.
2. GLs facility classification, where influencing factors associated with each type of facility are
considered.3. The integer programming methodology , to address the resulting network design issues,
where the corresponding facilities (hubs, dist. Center, depot) are hierarchically structured.
This study also considering multiple GLs channel members and related factors(e.g., customs accessibility, transnational transportation and inventory costs)
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LITERATURE REVIEW (6)
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SYSTEM SPECIFICATION
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System Component Definition
Conceptual Framework
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SYSTEM SPECIFICATION
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Three node types are defined:
Hubs
Distribution
Center
Warehouse
Depot
Based on
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SYSTEM COMPONENTDEFINITION
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Service component intensity () is composed of:
Transshipment amount ()
Storage value ()
Where= + Specifically is used to determine the type of candidate facilities
1Hub
2 < 1DistributionCenter
< 2Warehouse
1and 2are two thresholds
which can be determined by
averaging the values suggested by
GL enterprise managers in
practical application
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SYSTEM COMPONENTDEFINITION
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PHASE 1
Identifying original demand spotlocation
Hierarchy clustering the demand spotlocation
PHASE 2 & 3
Determining the number and locationof facility nodes for each demandgroup using integer programming.
Considering several factors:
1. Investment costs and risks
2. Logistic operational costs
3. Potential benefits
4. Transnational logistic restriction
5. Regional demand variations
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CONCEPTUAL FRAMEWORK
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Four assumptions used to facilitate the model formulation:
Only three facility types (hubs, distribution center, warehousedepots)
Differ in their express cargo capacities
The demand quantity associated with each given original demandspot is known.
The range of service-competence intensity associated with eachnode type in the proposed hierarchical GLs network is known.
The proposed hierarchy is composed of three layershubs,distribution centers, and warehouse depots
The facilities on a given layer are served by facilities of layer directly above.
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CONCEPTUAL FRAMEWORK
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MODEL DEVELOPMENT
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SCHEME OFPROPOSED MODEL
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DEMAND-SPOT HIERARCHICALCLUSTER ANALYSIS
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Hierarchicalcluster analysis
considers each
given object ias
a point in a
multi-
dimensionalspace
characterized
by two
attributes : the
amount of
inboundandoutbound
cargo
associated
with object i
Variable is an
important step in
hierarchical
cluster analysis,
since differences
in units and
magnitude of
variance betweenattributes
influence
computational
results of
distance metrics.
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HIERARCHICAL TREE
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FACILITY NETWORKCONFIGURATION
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Given the aforementioned assumptions, an integrated and composite multi-
objective model is formulated to obtain optimal solutions with the goals of :
Minimizing hierarchical GLs network investment cost (1)
Maximizing profit from hierarchical GLs network operations (2), and
The aggregate satisfaction rate of customer demand (3).
However, these three goals may be in conflict during the corresponding
hierarchical GLs network configuration process. A typical example is the trade-off
between minimizing hierarchical GLs network investment cost and maximizing
operational profit.
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FACILITY NETWORKCONFIGURATION
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GLs NETWORK INVESMENTCOST
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BC : Building Cost
LC : Land Cost
AIC : Asset Input Cost
RRC : Related Risk Cost
RMC : Raw Material Cost
LBC : Labor Cost
LC : Land Cost
MC : Machine Cost
EC : Equipment Cost
PR : Political Risk
NDR : Natural Disaster Risk
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PROFIT FROM HIERARCHICALGLs NETWORK OPERATIONS
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r : revenue
oc : operational cost
tdc : transportation and distribution cost
rorc : operational risk-oriented cost
r : revenue
oc : operational cost
tdc : transportation and distribution cost
err : exchange rate risk
hr : human resource
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THE AGGREGATE SATISFACTIONRATE OF CONSUMER DEMAND
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Accounts for the proportion (Z) of potential consumer demands (D) that
can be served by the logistics distribution amount (Y) planned within a
preset upper bound of delivery time (t)
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NUMERICAL STUDY
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TAIWAN CHINA USA
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EXPERIMENTAL DESIGNAND DATA COLLECTION (1)
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Based on the predetermined thresholds and proposed facility identificationrules, 3 original demand spots in Taiwan (e.g. Taipei, Taichung, and Kaohsiung)were chosen as candidates for the consideration of locating hubs.
Similarly, there are 15 and 36 original demand spots chosen as candidates forlocating hubs in China and the USA, respectively.
Accordingly, the problem scope has 3429 decision variables subject to 1068constraints.
Taiwan - 15 original demand spots
China - 116 original demand spots
USA - 260 original demand spots
using local population data
Thresholds 1and
2for facility
service-competence intensity index ()
averaging the values suggested by GL
enterprise managers were used in the
case study
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EXPERIMENTAL DESIGNAND DATA COLLECTION (2)
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First, this study collected data for local populations of these demand spotsand the corresponding gross domestic product (GDP) data from databases inTaiwan, China, and the USA.
International express delivery demand associated with each original demand spotwas then approximated using a proportion of GDP and the corresponding local
population.
The next step generated a four-tier GLs hierarchical network based ongeographical relationships
Step by step
the threemain regions
(first tier)
sub-regions(second tier)
the local area(third tier)
originaldemand spots(fourth tier)
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EXPERIMENTAL DESIGNAND DATA COLLECTION (3)
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HIERARCHICAL CLUSTERRESULT OF PROPOSED MODEL
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Estimating cost-related parameters directly from reported statistical data isdifficult due to business confidentiality and security concerns.
Therefore, interviews with key staff in express operations and logistics-related sectors of DHL were conducted to collect real data. The interviews
utilized both open-ended and closed questions regarding existingstrategies in express air delivery and logistics management, aswell as potential limitations.
A questionnaire was designed based on the need to estimate cost-relatedparameters of the model. For example, given a cost item, the correspondingsurvey respondent was asked to measure unit cost within an acceptable range.
Model parameters : (1) cost-related parameters(2) risk-related parameters
(3) boundary conditions
Estimated from:
interview survey data and
corresponding statistics.
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SETTING PARAMETERS (1)
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Corresponding parameters are classified into and associated with the
following activities:
Governmentstability
NaturalDisaster
Exchangerate
Personnelskills
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SETTING PARAMETERS (2)
Risk-related parameters estimated in this scenario aim at
the unit increments of money risks for environmental
risk cost and operational risk cost induced by the
hierarchical GLs network configuration.
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Among these risk-related parameters, Government Stability and Personnel
skills are associated with the corresponding artificial organization andbehavior; the others are influenced by natural disasters and operationalsituations in the resulting hierarchical GLs network. As mentioned, a unitincrease in risk-induced penalty refers to the monetary value of a particularpenalty caused by the unit of a given physical amount associated with aparticular activity.
Natural Disaster Risk
To estimate unit incremental risks for natural disasters, this study firstaveraged aggregate earthquake and flood damage costs of these three regionsover the last 30 years using historical data provided by central governments.
Second, aggregate damage costs caused by earthquakes and floods weremeasured using the averaged aggregate earthquake and flood damage costsmultiplied by the ratio of natural disaster frequency over the last 30 years.
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SETTING PARAMETERS (3)
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Exchange rate risk
Exchange rate risk may depend on foreign reserves, exchange law, and foreigndebt. Therefore, this study estimated the exchange-oriented risk by approximating
the corresponding comparative measures of exchange risk from BERI, which is
similar to the concept of political risk cost for the three regions.
Here, according to the proposed method, exchange risk can be expressed by the
amount of foreign debt divided by the amount of foreign reserves. In this case study,
statistics for foreign debt and foreign reserves for these three governments were used
to estimate the corresponding exchange risk.
Personnel Skill RiskSimilar to risks induced by the government stability, personnel skill risk can be
caused by either Democracy or Communism. Accordingly, was estimated usingcomparative measures of freedom developed by the Freedom House and BERI for
workers and society. Notably, parameter may vary with race; particularly, whites
currently have an advantageous position worldwide.
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SETTING PARAMETERS (4)
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SETTING PARAMETERS (5)
Parameters in the hierarchical GLs network based on cost-minimum function
Parameters in the hierarchical GLs network based on profit maximum function
Parameters in the hierarchical GLs network based on other primary parameters
Cost and risk-related parameters estimation:
ANALYSIS OF
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The numerical studies consider 2 different test scenarios:
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ANALYSIS OFNUMERICAL RESULTS (1)
1. Performance of proposed model VS Existing model
Express delivery enterprise case without coordination of threefacilities, including hubs, distribution centers, and warehouse depots.
2. Sensitivity Analysis
Used to realize what the most important parameters are in theenterprise and assist in enterprise resource planning.
All other parameters preset in Tables 24 remain the same in both scenarios
LINGO 9.0 software package is employed to search for optimal solutions
ANALYSIS OF
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First Scenario: Optimal solutions was generated using the proposedmodel, and then compared the resulting aggregate profit with existingoperational performance.
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ANALYSIS OFNUMERICAL RESULTS (2)
The existing GLs network ofDHL are primarily driven
by operational strategiesto maximize profit.
The weight (w2)associated with sub-
objective function of
profit-maximizationis setto 1
Proposed model is likely to outperform the
existing strategy
ANALYSIS OF
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Second Scenario: we conducted sensitivity analysis with respect tothe following four parameters
The combination of w1= w2= w3= 1/3 was chosen for all casesexcept for the sensitivity analysis with respect to these weights (No. 4)
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ANALYSIS OFNUMERICAL RESULTS (3)
1The threshold(1) associated
with the facilityservice-
competenceintensity index
for determinationof hub locations
2
Original demand(Dj)
3
Upper and lower
bounds of thesatisfaction rateof customer
demand ( ,
)
4
The weights
associated withthe three sub-
objectivefunctions (w1,w2, and w3)
ANALYSIS OF
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The threshold (1) Sensitivity Analysis
In this analysis, the authors tried to know the effect of a different companypolicy in terms of facility allocation threshold (1) on the number ofhubs that can be located in certain area.
Implication:
A GL enterprise is allowed to loosen the facility allocation threshold bychoosing a lower value of 1 for deciding hub locations if the enterprisehas enough capital and human resources allocated in GL networkconfigurations.
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ANALYSIS OFNUMERICAL RESULTS (4)
> = > >
> > > >
> > > >
ANALYSIS OF
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The Demand and Satisfaction rate Sensitivity Analysis
In this analysis, the authors tried to find out the effect of the change innumber of demand and customer satisfaction rate for he proposed model.
Implications:
The reduction of original demands may contribute significantly to the
aggregate improvement in system performance. Given the necessity of increasing the satisfactory rate of customer may also
improve the aggregate performance of proposed model.
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ANALYSIS OFNUMERICAL RESULTS (5)
We think that its more appropriate to show the improvement in terms of
profitinstead of in terms of cost
ANALYSIS OF
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The Weights Configuration Sensitivity Analysis
The authors want to find out the best configurations of the weightsassociated with the three sub-objective functions (w1, w2, and w3).
Implication:
Adjusting the corresponding weights associated with the three sub-objective functions have a significant effect on enhancing overallimprovement.
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ANALYSIS OFNUMERICAL RESULTS (6)
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This work has presented a novel approach that integrates hierarchical
cluster analysis and integer programming to formulate a
hierarchical GLs network model for dealing with the facility location
problem by minimizing total costs and maximizing operational
profit and the satisfaction rate of customers.
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CONCLUSIONS (1)
Compared to previous literature, the proposedmethod has 2 unique features:
The corresponding integrated supply & demand sides of the three-layer
hierarchical GLs network are formulated using generalized mathematicalform.
Internal and external factors (e.g., fundamental investment cost requirements,basic requirements of operational costs, related operational and disaster risks,and the satisfaction rate of customers) are considered by the proposed model.
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CONCLUSIONS (2)Case Result for the most suitable site for locating a hub forinternational express delivery enterprises in each area.
Taiwan Taipei
China Shenyang, Beijing, Shanghai, Chongqing, and Hong Kong
USA Los Angeles, Phoenix, Dallas, Houston, Chicago, New York, and Boston
Managers of international express delivery enterprises can conveniently
employ the proposed model as a decision-making support tool to
assist in strategically determining precedence for locating thecorresponding facilities, according to operational goals and overseas
investment resources.
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FUTURE RESEARCH The proposed model can be extended to determine dynamic multi-
resource allocation based on hierarchical GLs network configurationsproblems.
in-depth identification of qualitative and quantitative factors, such asdemand variation, risk uncertainty, and the time difference betweendifferent zones, also warrant further research.
Utilization of elaborate measures for demand data aggregation issuggested in further research to demonstrate the applicability of theproposed method.
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