Applying the flow-capturing location-allocation model to an authentic network: Edmonton, Canada

Traditional location-allocation models aim to locate network facilities to optimally serve demand expressed as weights at nodes. For some types of facilities demand is not expressed at nodes, but as passing network traffic. The flow-capturing location-allocation model responds to this type of demand and seeks to maximize one-time exposure of such traffic to facilities. This new model has previously been investigated only with small and contrived problems. In this paper, we apply the flow-capturing location-allocation model to morning-peak traffic in Edmonton, Canada. We explore the effectiveness of exact, vertex substitution, and greedy solution procedures; the first two are computationally demanding, the greedy is very efficient and extremely robust. We hypothesize that the greedy algorithm's robustness is enhanced by the structured flow present in an authentic urban road network. The flow-capturing model was derived to overcome flow cannibalization, wasteful redundant flow-capturing; we demonstrate that this is an important consideration in an authentic network. We conclude that real-world testing is an important aspect of location model development.     

Locating the two-median of a tree network with continuous link demands

Typical formulations of thep-median problem on a network assume discrete nodal demands. However, for many problems, demands are better represented by continuous functions along the links, in addition to nodal demands. For such problems, optimal server locations need not occur at nodes, so that algorithms of the kind developed for the discrete demand case can not be used. In this paper we show how the 2-median of a tree network with continuous link demands can be found using an algorithm based on sequential location and allocation. We show that the algorithm will converge to a local minimum and then present a procedure for finding the global minimum solution.     

Data Surrogation Error in p-Median Models

The p-median model locates facilities to provide optimal service to target populations. Where, for some reason, an inappropriate variable is used to stand in for a target population's demand, less than ideal facility systems can result. This effect is termed surrogation error. In this paper, I introduce this concept and perform an experiment which, with data for 25 Canadian cities, demonstrates that significant surrogation error can occur if general population is used in place of children or senior populations. I identify some of the correlates of surrogation error and conclude with a warning to location scientists to be conscious of, and to try to avoid, this problem.     

The least element property of center location on tree networks with applications to distance and precedence constrained problems

In the classicalp-center location model on a network there is a set of customers, and the primary objective is to selectp service centers that will minimize the maximum distance of a customer to a closest center. Suppose that thep centers receive their supplies from an existing central depot on the network, e.g. a warehouse. Thus, a secondary objective is to locate the centers that optimize the primary objective as close as possible to the central depot. We consider tree networks and twop-center models. We show that the set of optimal solutions to the primary objective has a semilattice structure with respect to some natural ordering. Using this property we prove that there is ap-center solution to the primary objective that simultaneously minimizes every secondary objective function which is monotone nondecreasing in the distances of thep centers from the existing central depot.Restricting the location models to a rooted path network (real line) we prove that the above results hold for the respective classicalp-median problems as well.     

A hierarchical location-allocation model with travel based on expected referral distances

The hierarchical p-median location-allocation model assumes that patrons always travel to the closest facility of appropriate level and that their interests are best served when the distances they must travel to do this are minimized. This assumption about travel behavior is unrealistic, patrons in the real world are known, for instance, to bypass lower level facilities that can serve their needs to attend higher level facilities. We introduce the concept of “expected distance under referral” to deal with such irrationality and incorporate it into a location-allocation model that minimizes the negative effects of such irrational behavior. We demonstrate the model with several types of non-optimal travel behavior.     

The gravity p-median model

In this paper we propose a new model for the p-median problem. In the standard p-median problem it is assumed that each demand point is served by the closest facility. In many situations (for example, when demand points are communities of customers and each customer makes his own selection of the facility) demand is divided among the facilities. Each customer selects a facility which is not necessarily the closest one. In the gravity p-median problem it is assumed that customers divide their patronage among the facilities with the probability that a customer patronizes a facility being proportional to the attractiveness of that facility and to a decreasing utility function of the distance to the facility.     

Hierarchical location-allocation problems: A classification scheme

In the literature, the p-median problem has been well studied. For the p-median problem our objective is to locate p facilities among n(? p) locations such that the total weighted travel distance is minimized. In the problem formulation, it is tacitly assumed that the facilities are of one type.In many practical situations, systems that provide products/services generally consist of k( ? 2) distinct types of facilities. In such problems, we would like to locate p_i type i facilities, i = 1, 2, … k, among n ( ? Σ_i^k = ₁p_i) available locations. Here also our objective may be to locate these facilities such that the ‘total weighted travel distance’ is minimized. What makes these problems difficult and interesting is that the extension of the p-median problem formulation and solution procedures to these problems is not always obvious, easy or straightforward. The problem formulation and solution procedures depend upon the hierarchical relationship among the facility types and the flow of goods and services allowed among them.In this paper we attemp to classify the hierarchical location-allocation problems.     

Facility location for market capture when users rank facilities by shorter travel and waiting times

A firm wants to locate several multi-server facilities in a region where there is already a competitor operating. We propose a model for locating these facilities in such a way as to maximize market capture by the entering firm, when customers choose the facilities they patronize, by the travel time to the facility and the waiting time at the facility. Each customer can obtain the service or goods from several (rather than only one) facilities, according to a probabilistic distribution. We show that in these conditions, there is demand equilibrium, and we design an ad hoc heuristic to solve the problem, since finding the solution to the model involves finding the demand equilibrium given by a nonlinear equation. We show that by using our heuristic, the locations are better than those obtained by utilizing several other methods, including MAXCAP, p-median and location on the nodes with the largest demand.     

Discrete stochastic location models

In this paper, we study how the two classical location models, the simple plant location problem and thep-median problem, are transformed in a two-stage stochastic program with recourse when uncertainty on demands, variable production and transportation costs, and selling prices is introduced. We also discuss the relation between the stochastic version of the SPLP and the stochastic version of thep-median.     

A dynamic programming heuristic for the P-median problem

A new heuristic algorithm is proposed for the P-median problem. The heuristic restricts the size of the state space of a dynamic programming algorithm. The approach may be viewed as an extension of the myopic or greedy adding algorithm for the P-median model. The approach allows planners to identify a large number of solutions all of which perform well with respect to the P-median objective of minimizing the demand weighted average distance between customer locations and the nearest of the P selected facilities. In addition, the results indicate regions in which it is desirable to locate facilities. Computational results from three test problems are discussed.     

On the choice of aggregation points for continuousp-median problems: A case for the gravity centre

In large scale location-allocation studies it is necessary to use data-aggregation in order to obtain solvable models. A detailed analysis is given of the errors induced by this aggregation in the evaluation of thep-median objective function. Then it is studied how to choose the points at which to aggregate given groups of demand points so as to minimise this aggregation error. Forp-median problems with euclidean distances, arguments are given in favour of the centre of gravity of the groups. These arguments turn out to be much stronger for rectangular distance. Aggregating at the centroid also leads to much higher precision bounds on the errors for rectangular distance. Some numerical results are obtained validating the theoretical developments. This research was partially done while the author was on visit at the Laboratoire d’Analyse Appliquée et Optimisation at the Université de Bourgogne, Dijon, France. Thanks to E. Carrizosa, B. Rayco and four anonymous referees for many thoughtful remarks.     

A new formulation for the conditional -median and -center problems

In this paper we discuss the conditional p-median and p-center problems on a network. Demand nodes are served by the closest facility whether existing or new. The formulation presented in this paper provided better results than those obtained by the best known formulation.     

Capacitated,balanced, sequential location-allocation problems on chains and trees

This paper considers finite horizon, multiperiod, sequential, minisum location-allocation problems on chain graphs and tree networks. The demand has both deterministic and probabilistic components, and increases dynamically from period to period. The problem is to locate one additionalcapacitated facility in each of thep specified periods, and to determine the service allocations of the facilities, in order to optimally satisfy the demand on the network. In this context, two types of objective criteria or location strategies are addressed. The first is a myopic strategy in which the present period cost is minimized sequentially for each period, and the second is a discounted present worth strategy. For the chain graph, we analyze ap-facility problem under both these criteria, while for the tree graph, we analyze a 3-facility myopic problem, and a 2-facility discounted present worth problem. All these problems are nonconvex, and we specify a finite set of candidate solutions which may be compared in order to determine a global optimal solution.     

A Hybrid Heuristic for the p-Median Problem

Given n customers and a set F of m potential facilities, the p-median problem consists in finding a subset of F with p facilities such that the cost of serving all customers is minimized. This is a well-known NP-complete problem with important applications in location science and classification (clustering). We present a multistart hybrid heuristic that combines elements of several traditional metaheuristics to find near-optimal solutions to this problem. Empirical results on instances from the literature attest the robustness of the algorithm, which performs at least as well as other methods, and often better in terms of both running time and solution quality. In all cases the solutions obtained by our method were within 0.1% of the best known upper bounds.     

p-Median theorems for competitive locations

In competitive location theory, one wishes to optimally choose the locations ofr facilities to compete againstp existing facilities for providing service (or goods) to the customers who are at given discrete points (or nodes). One normally assumes that: (a) the level of demand of each customer is fixed (i.e. this demand is not a function of how far a customer is from a facility), and (b) the customer always uses the closest available facility. In this paper we study competitive locations when one or both of the above assumptions have been relaxed. In particular, we show that for each case and under certain assumptions, there exists a set of optimal locations which consists entirely of nodes.This work was supported by a National Science Foundation Grant ECS-8121741.     

An analysis of p-median location problem: Effects of backup service level and demand assignment policy

Any solution to facility location problems will consider determining the best suitable locations with respect to certain criteria. Among different types of location problems, involving emergency service system (ESSs) are one of the most widely studied in the literature, and solutions to these problems will mostly aim to minimize the mean response time to demands. In practice, however, a demand may not be served from its nearest facility if that facility is engaged in serving other demands. This makes it a requirement to assign backup services so as to improve response time and service quality. The level of backup service is a key, strategic-level planning factor, and must be taken into consideration carefully. Moreover, in emergency service operations conducted in congested demand regions, demand assignment policy is another important factor that affects the system performance. Models failing to adopt sufficient levels of backup service and realistic demand assignment policies may significantly deteriorate the system performance.Considering the classic p-median problem (pMP) location model, this paper investigates the effects of backup service level, demand assignment policy, demand density, and number of facilities and their locations on the solution performance in terms of multiple metrics. For this purpose, we adopt a combined optimization and simulation approach. We will first modify the classic pMP to account for distances to backup services. Next, we employ a discrete event simulation to evaluate the performance of location schemes obtained from the deterministic mathematical model. Our results provide insights for decision-makers while planning ESS operations.     

Avoiding local optima in thep-hub location problem using tabu search and GRASP

In the discretep-hub location problem, various nodes interact with each other by sending and receiving given levels of traffic (such as telecommunications traffic, data transmissions, airline passengers, packages, etc.). It is necessary to choosep of the given nodes to act as hubs, which are fully interconnected; it is also necessary to connect each other node to one of these hubs so that traffic can be sent between any pair of nodes by using the hubs as switching points. The objective is to minimize the sum of the costs for sending traffic along the links connecting the various nodes. Like many combinatorial problems, thep-hub location problem has many local optima. Heuristics, such as exchange methods, can terminate once such a local optimum is encountered. In this paper, we describe new heuristics for thep-hub location problem, based on tabu search and on a greedy randomized adaptive search procedure (GRASP). These recently developed approaches to combinatorial optimization are capable of examining several local optima, so that, overall, superior solutions are found. Computational experience is reported in which both tabu search and GRASP found optimal hub locations (subject to the assumption that nodes must be assigned to the nearest hub) in over 90% of test problems. For problems for which such optima are not known, tabu search and GRASP generated new best-known solutions.     

A Cut and Branch Approach for the Capacitated <Emphasis Type="Italic">p</Emphasis>-Median Problem Based on Fenchel Cutting Planes

The capacitated p-median problem (CPMP) consists of finding p nodes (the median nodes) minimizing the total distance to the other nodes of the graph, with the constraint that the total demand of the nodes assigned to each median does not exceed its given capacity. In this paper we propose a cutting plane algorithm, based on Fenchel cuts, which allows us to considerably reduce the integrality gap of hard CPMP instances. The formulation strengthened with Fenchel cuts is solved by a commercial MIP solver. Computational results show that this approach is effective in solving hard instances or considerably reducing their integrality gap.      

A compelling argument for the gravity p-median model

The p-median model is used to locate P facilities to serve a geographically distributed population. Conventionally, it is assumed that the population always travels to the nearest facility.  and  re-estate three arguments on why this assumption might be incorrect, and they introduce the gravity p-median model to relax the assumption. We favor the gravity p-median model, but we note that in an applied setting, the three arguments are incomplete. In this communication, we point at the existence of a fourth compelling argument for the gravity p-median model.     

Location coverage models with demand originating from nodes and paths: Application to cellular network design

Location covering problems, though well studied in the literature, typically consider only nodal (i.e. point) demand coverage. In contrast, we assume that demand occurs from both nodes and paths. We develop two separate models – one that handles the situation explicitly and one which handles it implicitly. The explicit model is formulated as a Quadratic Maximal Covering Location Problem – a greedy heuristic supported by simulated annealing (SA) that locates facilities in a paired fashion at each stage is developed for its solution. The implicit model focuses on systems with network structure – a heuristic algorithm based on geometrical concepts is developed. A set of computational experiments analyzes the performance of the algorithms, for both models. We show, through a case study for locating cellular base stations in Erie County, New York State, USA, how the model can be used for capturing demand from both stationary cell phone users as well as cell phone users who are in moving vehicles.