## 6.6.3 Facility Locations on Probabilistic Networks

As a further illustration of the application of probabilistic network ideas to urban service system problems, we now reexamine briefly the question of facility location assuming a probabilistic network model. Throughout our discussion we shall be using the discrete and finite state-space model which we described in Section 6.6.2: each probabilistic network can be in one of m states with the probability of it being in state r given by Pr.

Two important observations must be made in the beginning:
 1 When there are two or more facilities on a probabilistic network and assuming that any demand point will always be served by its nearest facility at the moment when that demand is generated, the facility serving a particular demand point will depend on the state of the network. This point becomes quite obvious once it is realized that shortest distances and paths change with the state of the network. 2 A finest-grained sample space should be used in solving facilitylocation problems on probabilistic networks. This can best be explainedinitially through the following example.

Example 21: Using the Finest-Grained Sample Space

Consider the network of Figure 6.42. The lengths (1, 3), (2, 3), (2, 4), and (3, 4) are deterministic with values of 5, 2, 5, and 5 units, respectively, while L(1, 2) takes on the values of 1 or 13 units, with probability 0.5 for each value Thus, E[L(1, 2)] = 7.

Assume now that we wish to solve a single-median problem and that somehow it is known that the optimal location for the median is at one of the nodes of the network.22 The demand weights are shown in parentheses next to each node.

Suppose that E[L(1, 2)] were substituted for the length L(1, 2). Then the optimal location of the facility is at node 3 with an expected travel time of 3 2/3 units (= 1/3 · 5 + 1/3 · 2 + 1/3 · 4). If, however, we use a finest-grained sample space and assuming that the current state of the network is always known whenever a trip is undertaken, it can be seen that the minimum distance between nodes 1 and 2 is either 1 or 7 units [via node 3 when L(1, 2) = 13], each with probability 0.5. The expected travel distance when the facility is located at node 2 is then equal to 3 units [= 1/3(4) + 1/3(0) + 1/3(5)] and node 2 is a better location than node 3.

From the example it is clear that the finest-grained sample space is needed because the shortest path between any two points may vary with the state of the network. In fact, our example also demonstrated that the substitution of expected value E[L(i, j)] for the actual values r(i, j) of the link lengths may lead to erroneous results. Unfortunately, as we noted earlier, this substitution is often used in practice, since most deterministic network models are constructed, in the first place, by using "average" travel times as deterministic substitutes for the random variables L(i, j) that link lengths really are. Such a substitution may be justified only when the current state of the network is not known whenever a trip is started.

We now proceed to develop a single-median result for probabilistic networks. For an undirected network G(N, A) with n nodes, we have:

Definition: A point x* on an undirected probabilistic network G is an expected l-median of G. if for every point x G.

Our result, completely analogous to Hakimi's for deterministic networks, states:

Theorem: At least one solution to the expected l-median problem exists on a node in an undirected probabilistic network.

This theorem can be proved (see [MIRC 79b]) under the following assumption:

Homogeneity assumption: The time required to travel a fraction of the link (i, j) is equal to · r(i, j) for all r = 1, 2, . . ., m.

The homogeneity assumption states in effect that the speed of travel on any given link is uniform--an assumption that is both straightforward and reasonable since the network model of the urban area can easily be constructed such that this assumption holds.

The theorem above can be extended, as one might suspect, to the case of the expected k-medians. In fact, the following even more general result has been shown to hold [MIRC 79b]:

Definition: A set of k points X*k on an undirected probabilistic network G is a set of expected optimal k-medians of G if for every set Xk G,

and u(t) is the utility function of travel time t.

Then

Theorem: At least one set of expected optimal k-medians exists on the nodes in an undirected stochastic network if the utility function for travel time is convex.

Similar very general results for medians also apply to directed networks. Finally, we note that an implicit assumption in the foregoing discussion of the medians problem on probabilistic networks has been that the appropriate connectivity conditions apply. For instance, our definition of the expected single median would be meaningless if it were not true that "those nodes with nonzero demand (hj > 0) are always, that is, for all m possible states of the network, connected." Were this not the case, then some distance(s) between one or more nodes and the facility would be infinite and so would the expression for (x).

The study of probabilistic networks [0n the other hand, the problem of network reliability is a classical one and has been studied extensivelywith reference tocommunications and other networks [FRAN 71].] and their applications to urban service systems has begun in earnest only recently. This is not due to lack of promise of highly useful results but rather to the conceptual and computational difficulties to which we alluded earlier. It can be safely anticipated that this will be one of the most active areas of investigation by operations researchers, management scientists, and others in the next few years.

22 We have shown this to be true for deterministic networks, but we have yet to prove it for probabilistic networks.