American Journal of Oper ations Research, 2011, 1, 277-283
doi:10.4236/ajor.2011.14032 Published Online December 2011 (
Copyright © 2011 SciRes. AJOR
Analysis of Facility Systems’ Reliability Subject to Edge
Failures: Based on the p-Median Problem
Zongtian Wei1,2, Huayong Xiao1, Yuxi Quan2
1Department of Applied Mathematics, Northwestern Polytechnical University, Xian, China
2Department of Mat hem at i cs, Xian University of Architecture and Technology, Xian, China
E-mail:, huayongx@nwp,
Received August 28, 2011; revised October 3, 2011; accepted October 17, 2011
We view a facility system as a kind of supply chain and model it as a connected graph in which the nodes
represent suppliers, distribution centers or customers and the edges represent the paths of goods or informa-
tion. The efficiency, and hence the reliability, of a facility system is to a large degree adversely affected by
the edge failures in the network. In this paper, we consider facility systems' reliability analysis based on the
classical p-median problem when subject to edge failures. We formulate two models based on deterministic
case and stochastic case to measure the loss in efficiency due to edge failures and give computational results
and reliability envelopes for a specific example.
Keywords: Facility System, Reliability, Edge Failure, p-Median Problem, Operating Efficiency
1. Introduction
Church and Scaparra [1] defined critical infrastructure as
those elements which are necessary for life line support
and safety. They include such systems as communication
systems, transportation systems, water and sewer sys-
tems, health services facilities, etc. Each of these systems
has unique properties that may define specific issues in
operation and management in order to provide a consis-
tent and continuing level of operation.
Every facility system in operation maybe faces various
disruptions. Such disruptions have begun to receive sig-
nificant attention from practitioners and researchers after
the terrorist attacks of September 11, 2001. One reason
for this growing interest is the spate of major disruptions
since the new century such as the foot-and-mouth disease
scare in the UK in 2001, the terrorist attacks on Septem-
ber 11, 2001 and the west-coast port lockout in 2002 in
the US, the Asia SARS outbreak in 2003, the Indian
Ocean tsunami on December 26, 2004, and the Wen-
chuan earthquake on May 12, 2008 in China, etc. In fact,
various natural disasters or intentional strikes constantly
occur in the world, e.g., congestions, inclement weather,
earthquakes, debris flows, sandstorms or terrorist attacks.
Another reason is that, firms are much less vertically
integrated than they were in the past, and their supply
chains are increasingly global. Today, firms tend to as-
semble final products from increasingly complex com-
ponents, which are procured from suppliers rather than
produced in-house. These suppliers are located through-
out the globe, many in regions that are unstable politi-
cally or economically or subject to wars and natural dis-
Facility system disruption s can have significant physi-
cal costs [2]. Therefore, Reliability, Robustness and Re-
silience (3R) are receiving high attention in the design of
facility systems [3].
The reliability of a facility system is the probability
that all suppliers are operable [4]. This concept is intro-
duced from the network reliability theory. Generally
speaking, the key differen ce between networks reliability
and facility systems reliability is that the former are pri-
marily concerned with connectivity; they consider the
cost of constructing the network but not the cost that
results from a disruption, whereas the latter consider both
types of costs and generally assume connectivity after a
disruptio n [ 5] .
In location science, the problem of locating p-supply
facilities that yields the smallest weighted distance is
called the p-median problem (PMP). The PMP has been
the subject of considerable research, starting with the
theorems of Hakimi [6,7], the first heuristic of Teitz and
Bart [8] and an integer linear programming model of
Revelle and Swain [9]. Church [10] provided a detailed
summary of different approaches for solving the PMP.
A number of papers in the location literature have ad-
dressed the problem of finding the optimal location of
protection devices to reduce the impact of possible dis-
ruptions to infrastructure systems. For example, Carr et
al. [11] presented a model for optimizing the placement
of sensors in water supply networks to detect mali-
ciously injected contaminants. James and Salhi [12]
investigated the problem of placing protection devices
in electrical supply networks to reduce the amount of
outage time.
Church et al. [13] presented a model called the r-in-
terdiction median problem. This model can be used to
identify which r of the existing set of p-facilities, when
interdicted or lost impacts delivery efficiency the most.
Such a model can be used to identify the worst case of
loss, when losing a pre-specified number of facilities.
The model is restricted in two ways: it is based upon the
assumption that the terrorist or interdictor is successful in
each and every strike, and it is also based upon the as-
sumption that exactly r facilities will be struck and lost.
Such a model does address a worst case scenario, but it
does not exactly capture the issues that would be key to
understanding the range of failures and possible out-
In [1], the authors argued that first, it is important to
recognize that a strike or disaster may not impair a facil-
ity’s operation. That is, a terrorist strike may be success-
ful only a certain percentage of the time. The same is
true for a natural disaster. When it does occur, there is a
threat that operations at a facility may need to be sus-
pended, but it is not absolute. Second, interdiction may
not be intelligent when the strikes impact a non-critical
facility. Although it is important to model “worst-case”
scenarios, it is also important to model and understand
the range of possible failures and impacts. Therefore,
they proposed a family of models which can be used to
model the range of possible impacts associated with the
threat of losing one or more facilities to a natural disaster
or intentional strike. They show how to model determi-
nistic loss and probabilistic loss. In addition, they pre-
sented results associated with the application of the worst
case and the best case expected loss models to a data set.
There is a mature literature on reliable, robust and re-
silient network (e.g., supply chain network (SCN)) de-
sign and analysis under component failures. Unfortu-
nately, so far we have not found the explicit study of
facility systems’ reliability subject to edge failures. In
fact, generally the reliability, an d henc e the efficien cy, of
a facility systems is to a large degree adversely affected
by failures of the edges. Thus the network-based facility
system reliability models to be investigated are more
practical and closer to the reality of facility system man-
In this paper, adopting the facility location analysis
framework, we will mainly consider facility systems’
reliability analysis based on the classical p-med ian prob-
lem when subject to edge disruptions. The remainder of
the paper is organized as follows. In Section 2, we for-
mulate two reliability models based on the p-median
problem and edge failures. We use a scenario based al-
gorithm to compute a specific example and give the re-
sults and reliability envelop es in Section 3. Section 4 is a
summary of this paper.
In the following, by “loss” we refer to the edge disrup-
tions (failures) mentioned above or, sometimes the nec-
essary closure.
2. Facility System Reliability Analysis
The PMP locates p facilities to minimize the demand-
weighted total (or average) distance between demands
and the nearest facility. The exact opposite of the p-me-
dian problem occurs when an existing system of p facili-
ties, which may or may not be located optimally. When
either closing or considering the loss of one or more
transportation lines by a disaster, the basic question is
what happens to the operating efficiency of the system.
2.1. The Deterministic Reliability Models
The following are the notations for our formulations.
: set of customers, indexed by . i
: set of potential facility locations, indexed by . j
We view a facility system as a weighted connected
simple graph
h: demand at customer . iI
,, ,GVEHD, where VI ; is
the edge set with the edges denoting goods or informa-
tion paths;
is the vertex weight set with the weight
i of vertex i, denoting the demand of customer ,
and is the edge weight set with the weight ij of
h i
,, denoting the length (e.g., distance) between
and under the existing conditions. By ij we
also denote the shortest path length (or distance) between
and if
,ij E
. Evidently, . 0ij
Let be the opened facility (server) set of an exist-
ing facility system G, and
E be the potential
failure edge set, where the edge failure is defined as an
edge losses its designed function completely. Therefore,
a failed edge in is equivalent to delete (or close) the
corresponding line from the facility system. We also as-
sume that the edge failures are independent and multiple
edge failures may occur simultaneou sly.
Copyright © 2011 SciRes. AJOR
Let r be the set of scenarios corresponding to the
closure or deletion of edges from , i.e., every
Sr G
S explicitly specifies the failed r edges in
Let ijs be the shortest distance between customer
and facility in scenario
d i
. Define
ijs . Assume that in any scenario
Nj d
jC, customer is served by an opened facility
which is the nearest one from if is
ii N
Associated with each customer
iper-unit pen-
alty cost i
Iis a
tat represents the cost of not serving the
customer if is
. i
my represent a lost-sales cost,
or the cost to pay a competitor to serve the customer
temporarily. Denote 1
Y, omer i is assigned
to facility j in scenario
if cust
; 0, othewise. r
We formulate the deterministic reliability model (DRM)
as the following integer-linear programming problem:
s.t. 1,,
ris is
iijs ijsii
sS iIN jCiIN
ijs r
hd Yh
 
 
 
0, 1
Y, , , iI jCr
S (2)
The objective function selects failed edges from F
in order to minimize the resulting weighted distance.
Constraints (1) require that each customer be served by
at most one server in any scenario. Constraints (2) re-
quire the assignment variables to be binary.
For a given edge loss level (the number of closed
or deleted edges), this model can be used to evaluate a
facility system’s operational efficiency under the best
case, namely the minimal loss of the system’s efficiency.
Changing the “min” to “max” in the objective function,
then we obtain the worst case model, that is the model to
measure the maximal increase in weighted distance un-
der the edge failure level .
2.2. The Stochastic Reliability Models
The reliability model formulated above is based upon a
deterministic analysis. Up to this point we have modeled
edge loss a certainty. We now consider the case where
loss is not a certainty upon an edge failure. Usually, the
chances of losing an edge are based upon some probabil-
ity. We wish to derive the maximal or minimal expected
efficiencies associated with an existing system. To do so
we need to identify both the worst case and the best case
expected outcomes.
E be the target edge (the potential failure
edge) set of an attack. Assume that an attacker can hit
each edge in
at most once and that the edges in
will be hit simultaneously.
Let be the scenario set when edges in
S r
have been attacked. Each r
S specifies which
edges in r
have been attacked. Denote
is attacked in s
EeFe scenario , then any
E can be used to represent a failed edge set in
, so we call
the sub-scenario of
. Let
ijF be the shortest distance between customer and
an opened facility in scenario
. Define
iF ijF
. Assume that in any scenario
, customer is served by an opened facility ijC
which is the nearest one from if s
. Let
be the failure probability of edge after one attack.
It is easy to see that scenario jF
occurs with probability
Denote the assignment variables as s
ijF , if cus-
tomer is served by facility in scenario
; 0,
Let be the opened facility (server) set of an exist-
ing facility system. We formulate the stochastic reliabil-
ity model (SRM) as the following integer-linear pro-
gramming problem (The penalty cost
are defined in
Section 3.1.):
s.t. 1,,
rss iF
i ijFijF
i i
ijF s
Yi E
0,1 ,,
YiIj 
,s s
CFE (4)
The objective function selects edges from F to
minimize the weighted distance expectation after
edges in F have been attacked. Constraints (3) require
that each customer be served by at most one server in
any scenario
. Constraints (4) require the assignment
variables to be b inary.
For a given failure level , this model can be used to
evaluate a facility system’s operational efficiency under
the best case. Changing “min” in the objective function
to “max”, then we obtain the worst case model, i.e., the
model to measure the maximal increase in expected
weighted distance under failure level .
3. The Reliability Envelopes
The models described above can be applied to a given
facility system over a range of edge loss level r. We
use the weighted distance to measure the efficiency of a
facility system and efficiency is measured at 100% if all
edges are operating. If an edge is lost due to a natural
disaster, intentional strike or planned closure, then the
efficiency is lost and overall efficiency decreases. If
many edges exist, then there exist several possible out-
Copyright © 2011 SciRes. AJOR
Copyright © 2011 SciRes. AJOR
Figure 1 shows the optimal solution, where the 6 dis-
tribution centers are city 1, city 7, city 10, city 19, city 22
and city 44, and the edges marked by red color represent
the delivery routes from each distribution center to its
comes of losing just one edge. One can easily enumerate
each of the possible ways of losing one edge as well as
calculate the impact of each possible loss in terms of
changes in efficiency. The results of this series of calcu-
lations will define a range of losses from the best case
(i.e. the least decrease in efficiency) to the worst case (i.e.
the greatest decrease in efficiency). We then have a re-
gion defined by an upper curve and a lower curve, where
the upper and the lower curve represent the solutions of
the least or the greatest impact associated with a given
loss level, respectively. The region depicted between
these two curves can be defined as the operational enve-
lope or reliability envelope. For a given edge loss level,
this envelope specifies the range of possible system per-
formance from the best-case to the worst-case. Actual
performance will fall within this range.
Given this operating system of 6 facilities and a poten-
tial failure edges set
 
1 5,47,2 1,3 ,37,40,4 10,38,522,23,
which is consisted of 8 edges (the sub-graph GF
connected, i.e., both of is and N
iF are not
), we
then consider edge loss level from 1 to 8 and solve the
worst case DRM and the best case DRM. The solutions
are given in Table 1.
In Table 1, for each edge loss level, the objective
function values and efficiency for each case are also
given as a percentage, where 100% represents the oper-
ating level before edge failures. Of cause, the edges
shown in volume 6 of Table 1 are the most important
objective of protection.
In this section we apply the DRM and the SRM to a
data set to generate reliability envelopes. Our data set is
derived from the 2008 China census data: a 49-node set
consisting of the capitals of all the provinces in China
plus the two special administrative regions Hong Kong
and Macau, as well as other 15 big cities. The demand of
city , i is settled to be the city’s administrative re-
gion population divided by 104. The transportation links
are the recent national highways and the transportation
cost between city and city , equal to the dis-
tance between the two cities.
i h
ij ij
Figure 2 presents the values of operational efficien-
cies (in percent) as a graph, depicting the lower and the
upper boundaries of the reliability envelope. Notice that
the greatest marginal impact for the worst case occurs
when the edge loss level is small while for the best case
occurs when the edge loss level is great.
We optimally solve a 6-median problem in order to
obtain an existing facility system. It is also important to note that the greatest difference
Figure 1. Optimal solution of a PMP with p = 6.
Table 1. Results of the worst-case DRM and the best-case DRM.
Level Best-Case Worst-Case
r Objec. V alue Failed Edges Efficiency Objec. Value Failed Edges Efficiency
0 11,572,831 - 100% 11,572,831 - 100%
1 11,599,381 6 99.77% 12,989,073 5 89.10%
2 11,669,755 3, 6 99.17% 13,558,323 5, 7 83.54%
3 11,742,050 3, 4, 6 98.56% 13,852,403 2, 5, 7 83.54%
4 11,887,640 3, 4, 6, 8 97.35% 14,059,421 1, 2, 5, 7 82.31%
5 12,051,692 1, 3, 4, 6, 8 96.03% 14,205,011 1, 2, 5, 7, 8 81.47%
6 12,345,772 1, 2, 3, 4, 6, 8 93.74% 14,277,306 1, 2, 4, 5, 7, 8 81.06%
7 12,735,040 1, 2, 3, 4, 6, 7, 8 90.87% 14,347,680 1, 2, 3, 4, 5, 7, 8 80.66%
8 14,376,780 1, 2, 3, 4, 5, 6, 7, 880.50% 14,376,780 1, 2, 3, 4, 5, 6, 7, 8 80.50%
Figure 2. Reliability envelope of solutions in Table 1.
between the worst case and the best case, or the thickness
of the envelop occurs when the edge loss level is moder-
By using the same data set as in the above DRM, we
then solve the worst case and the best case SRM with
edge failure probability p = 0.3 and p = 0.7, respectively.
The solutions of the latter and the correspond ing reliabil-
Figure 3. Reliability envelope of solutions in Table 2.
ity envelope are showed in Table 2 and Figure 3, re-
spectively. Notice that the characteristics of this reliabil-
ity envelope are similar to that of the DRM except that,
on the same edge loss level, the efficiency losses of the
SRM are less than that of the DRM, since we assume
that the failure probability of an edge under a strike is 1
in the DRM. We can also observe from Table 2 that, for
Table 2. Results of the worst case SRM and the best case SRM with edge failure probability 0.7.
Level Best-Case Worst-Case
r Objec. Value Attacked Edges Efficiency Objec. Value Attacked Edges Efficiency
0 11,572,831 - 100% 11,572,831 - 100%
1 11,591,416 6 99.84% 12,564,200 5 92.11%
2 11,640,678 3, 6 99.42% 12,915,856 5, 7 89.60%
3 11,691,284 3, 4, 6 98.99% 13,121,712 2, 5, 7 88.20%
4 11,793,197 3, 4, 6, 8 98.13% 13,257,602 1, 2, 5, 7 87.29%
5 11,908,034 1, 3, 4, 6, 8 97.19% 13,359,515 1, 2, 5, 7, 8 86.63%
6 12,113,890 1, 2, 3, 4, 6, 8 95.53% 13,410,122 1, 2, 4, 5, 7, 8 86.30%
7 12,377,354 1, 2, 3, 4, 6, 7, 8 93.50% 13,459,383 1, 2, 3, 4, 5, 7, 8 85.98%
8 13,479,218 1, 2, 3, 4, 5, 6, 7, 885.86% 13,479,218 1, 2, 3, 4, 5, 6, 7, 8 85.86%
Copyright © 2011 SciRes. AJOR
a given edge attacked level, the most important edges of
protection are shown in volume 6.
4. Summary and Conclusions
In this paper, we propose two types of scenario based
facility location models in order to an alyze the reliability
of an existing facility system when subject to edge fail-
ures. We distinguish deterministic and sto chastic cases to
formulate and compute a specific example. Reliability
envelopes in these two different cases are also given. The
information in the reliability envelopes can be very use-
ful in looking at ways to protect a facility system.
Whether the protection is against a natural disaster or
intentional strike, reducing the probability of success
even by modest amounts could have an impact on system
efficiency. For example, this could be done by placing
extra strength in key sections such as bridges or tunnels
which spaced in disaster-prone areas, or by adding a
surveillance system with guards to help protect against
an intruder. Either techniques may not completely elimi-
nate a loss, by reduce the edge failure probability to zero ,
but such strategies may generate more benefits in terms
of improved expected system operating efficiencies than
what it might cost. Therefore, the value of our analysis
could lead to higher levels of safety as well as efficient
levels of resource allocation for security measures
(whether that involves a possible natural disaster or an
While a large body of literature focus on the reliable
and robust facility system design and analysis under
component failures, the existing works are mainly con-
centrated on the “node” (e.g., suppliers, distribution cen-
ters) losses. To the best of our knowledge, researchers
and practitioners have not paid enough attention to the
impact of edge failures to facility systems’ efficiency by
so far. Comparing to the related works done in this field,
our work have at least the following innovations.
Firstly, combining edge failures into facility systems
reliability analysis is more realistic than only considering
vertex failures. In fact, natural disasters or intentional
attacks damage the edges of a facility system more easily.
Secondly, in the PMP and other uncapacitated facility
location problems, when one or more “nodes” have
failed, the overall supplement of the facility system will
decrease dramatically but the total demand does not
change. If in this situation all demands must to be met,
then every node must has no any capacity limit. However,
the capacity of nodes are designed a priori, when a vertex
failure happen , how can it’s adj acent nodes guaran tee the
increased demands, let alone more than one vertex fail-
ure occur simultaneously?
The recovery time for a failed edge maybe shorter than
that for a failed vertex, but this is not always the case. So
we do not explicitly point out the time horizon in our
models. In addition, the evaluation of edge failure prob-
ability is important an d difficult, and we will discuss this
question in another work. We set the failure probability
of all edges as the same when solving the example since
we aimed to demonstrate the impact of edge failures to
the efficiency of a facility system.
5. Acknowledgements
This paper was supported by the Open Fund of Xi’an
Jiaotong University ( No. 2010-4), SXESF (No. 09JK545)
and BSF (No. JC0924).
6. References
[1] R. L. Church and M. P. Scaparra, “Critical Infrastruc-
ture,” Springer, Berlin, 2007.
[2] R. Kembel, “The Fibre Channel Consultant: A Compre-
hensive Introduction,” Northwest Learning Associates,
Tucson, 2000.
[3] Y. Sheffi, “The Resilient Enterprise: Overcoming Vul-
nerability for Competitive Advantage,” MIT Press, Cam-
bridge, 2005.
[4] M. Bundschuh, D. Klabjan and D. L. Thurston, “Model-
ing Robust and Reliable Supply Chains,” Working Paper,
University of Illinois, Urbana-Champaign, 2003.
[5] L. V. Snyder and Z. J. M. Shen, “Managing Disruptions
to Supply Chains,” The Bridge (National Academy of En-
gineering), Vol. 36, No. 4, 2006, pp. 39-45.
[6] S. L. Hakimi, “Optimum Location of Switching Centers
and the Absolute Centers and Medians of a Graph,” Op-
erations Research, Vol. 12, No. 3, 1964, pp. 450-459.
[7] S. L. Hakimi, “Optimum Distribution of Switching Cen-
ters and Some Graph Related Theoretic Properties,” Op-
erations Research, Vol. 13, 1965, pp. 462-475.
[8] M. B. Teitz and P. Bart, “Heuristic Methods for Estimat-
ing the Generalized Vertex Median of a Weighted Graph,”
Operations Research, Vol. 16, No. 5, 1968, pp. 955-961.
[9] C. S. Revelle and R. Swain, “Central Facilities Location,”
Geographical Analysis, Vol. 2, No. 1, 1970, pp. 30-42.
[10] R. L. Church, “COBRA: A New Formulation for the
Classic p-Median Location Problem,” Annals of Opera-
tions Research, Vol. 122, No. 1-4, 2003, pp. 103-120.
[11] R. D. Carr, et al., “Robust Optimization of Contaminant
Sensor Placement for Community Water Sy stems,” Mathe-
matical Programming, Vol. 107, No. 1-2, 2005, pp. 337-
[12] J. C. James and S. Salhi, “A Tabu Search Heuristic for
the Location of Multi-Type Protection Devices on Elec-
trical Supply Tree Networks,” Journal of Combinatorial
Copyright © 2011 SciRes. AJOR
Optimization, Vol. 6, No. 1, 2002, pp. 81-98.
[13] R. L. Church, M. P. Scaparra and R. S. Middleton, “Iden-
tifying Critical Infrastructure, The Median and Covering
Facility Inte rdiction Problems, ” Annals of the Association
of American Geographers, Vol. 94, No. 3, 2004, pp. 491-
502. doi:10.1111/j.1467-8306.2004.00410.x
Copyright © 2011 SciRes. AJOR