The maximum reliability location problem and α-reliablep-center problem: Derivatives of the probabilistic location set covering problem

In the last several years, the modeling of emergency vehicle location has focussed on the temporal availability of the vehicles. Vehicles are not available for service when they are engaged in earlier calls. To incorporate this dynamic aspect into facility location decisions, models have been developed which provide additional levels of coverage. In this paper, two new models are derived from the probabilistic location set covering problem. These models allow the examination of the relationships between the number of facilities being located, the reliability that a vehicle will be available, and a coverage standard. In addition, these models incorporate sectoral specific estimates of the availability of the vehicles. Solution of these models reveals that the use of sectoral estimates leads to facility locations which are distributed to a greater spatial extent over the region to be serviced.     

The capacitated maximal covering location problem with backup service

The maximal covering location problem has been shown to be a useful tool in siting emergency services. In this paper we expand the model along two dimensions — workload capacities on facilities and the allocation of multiple levels of backup or prioritized service for all demand points. In emergency service facility location decisions such as ambulance sitting, when all of a facility's resources are needed to meet each call for service and the demand cannot be queued, the need for a backup unit may be required. This need is especially significant in areas of high demand. These areas also will often result in excessive workload for some facilities. Effective siting decisions, therefore, must address both the need for a backup response facility for each demand point and a reasonable limit on each facility's workload. In this paper, we develop a model which captures these concerns as well as present an efficient solution procedure using Lagrangian relaxation. Results of extensive computational experiments are presented to demonstrate the viability of the approach.     

带次模惩罚的优先设施选址问题的近似算法

研究带次模惩罚的优先设施选址问题, 每个顾客都有一定的服务水平要求, 开设的设施只有满足了顾客的服务水平要求, 才能为顾客提供服务, 没被服务的顾客对应一定的次模惩罚费用. 目标是使得开设费用、连接费用与次模惩罚费用之和最小. 给出该问题的整数规划、 线性规划松弛及其对偶规划. 基于原始对偶和贪婪增广技巧, 给出该问题的两个近似算法, 得到的近似比分别为3和2.375.     

Two new heuristics for the location set covering problem

Summary In this paper we present two new greedy-type heuristics for solving the location set covering problem. We compare our new pair of algorithms with the pair GH1 and GH2 [Vasko and Wilson (1986)] and show that they perform better for a selected set of test problems.     

A Lagrangean heuristic for the maximal covering location problem

We develop a Lagrangean heuristic for the maximal covering location problem. Upper bounds are given by a vertex addition and substitution heuristic and lower bounds are produced through a subgradient optimization algorithm. The procedure was tested in networks of up to 150 vertices. A duality gap was generally present at the end of the heuristic for the larger problems. The test problems were run in an IBM 3090-600J ‘super-computer’; the maximum computing time was kept below three minutes of CPU.     

Approximation algorithms for the stochastic priority facility location problem

In this article, we present a primal-dual 3-approximation algorithm for the stochastic priority facility location problem. Combined with greedy augmentation procedure, such performance factor is further improved to 1.8526.     

An algorithm portfolio for the dynamic maximal covering location problem

The location of facilities (antennas, ambulances, police patrols, etc) has been widely studied in the literature. The maximal covering location problem aims at locating the facilities in such positions that maximizes certain notion of coverage. In the dynamic or multi-period version of the problem, it is assumed that the nodes’ demand changes with the time and as a consequence, facilities can be opened or closed among the periods. In this contribution we propose to solve dynamic maximal covering location problem using an algorithm portfolio that includes adaptation, cooperation and learning. The portfolio is composed of an evolutionary strategy and three different simulated annealing methods (that were recently used to solve the problem). Experiments were conducted on 45 test instances (considering up to 2500 nodes and 200 potential facility locations). The results clearly show that the performance of the portfolio is significantly better than its constituent algorithms.     

The gradual covering decay location problem on a network

In covering problems it is assumed that there is a critical distance within which the demand point is fully covered, while beyond this distance it is not covered at all. In this paper we define two distances. Within the lower distance a demand point is fully covered and beyond the larger distance it is not covered at all. For a distance between these two values we assume a gradual coverage decreasing from full coverage at the lower distance to no coverage at the larger distance.     

An adaptation of SH heuristic to the location set covering problem

In a recent paper, a new surrogate heuristic (SH) has been proposed for the set covering problem. Here we present an adaptation of it in order to solve more efficiently the location set covering problem. We will show that our new version not only outperforms algorithm SH but that it is more accurate than the pair CMA/FMC. Its power is experimentally tested over a set of 65 randomly generated problems.     

Solving covering problems and the uncapacitated plant location problem on trees

Given a tree network on n vertices, a neighborhood subtree is defined as the set of all points on the tree within a certain radius of a given point, called the center. It is shown that for any two neighborhood subtrees containing the same endpoint of a longest path in the tree one is contained in the other. This result is then used to obtain O(n²) algorithms for the minimum cost covering problem and the minimum cost operating problem as well as an O(n³) algorithm for the uncapacitated plant location problem on the tree.     

Planar maximal covering location problem under block norm distance measure

This paper introduces a new model for the planar maximal covering location problem (PMCLP) under different block norms. The problem involves locating g facilities anywhere on the plane in order to cover the maximum number of n given demand points. The generalization, in this paper, is that the distance measures assigned to facilities are block norms of different types and different proximity measures. First, the PMCLP under different block norms is modelled as a maximum clique partition problem on an equivalent multi-interval graph. Then, the equivalent graph problem is modelled as an unconstrained binary quadratic problem (UQP). Both the maximum clique partition problem and the UQP are NP-hard problems; therefore, we solve the UQP format through a genetic algorithm heuristic. Computational examples are given.     

A taxonomy for emergency service station location problem

The emergency service station (ESS) location problem has been widely studied in the literature since 1970s. There has been a growing interest in the subject especially after 1990s. Various models with different objective functions and constraints have been proposed in the academic literature and efficient solution techniques have been developed to provide good solutions in reasonable times. However, there is not any study that systematically classifies different problem types and methodologies to address them. This paper presents a taxonomic framework for the ESS location problem using an operations research perspective. In this framework, we basically consider the type of the emergency, the objective function, constraints, model assumptions, modeling, and solution techniques. We also analyze a variety of papers related to the literature in order to demonstrate the effectiveness of the taxonomy and to get insights for possible research directions.     

Improving emergency service in rural areas: a bi-objective covering location model for EMS systems

Emergency medical service (EMS) systems are public services that often provide the first line of response to urgent health care needs within a community. Unfortunately, it has been widely documented that large disparities in access to care exist between rural and urban communities. While rural EMS is provided through a variety of resources (e.g. air ambulances, volunteer corps, etc.), in this paper we focus on ground ambulatory care. In particular our goal is to balance the level of first-response ambulatory service provided to patients in urban and rural areas by locating ambulances at appropriate stations. In traditional covering location models the objective is to maximize demand that can be covered; consequently, these models favor locating ambulances in more densely populated areas, resulting in longer response times for patients in more rural areas. To address the issue of fairness in semi-rural/semi-urban communities, we propose three bi-objective covering location models that directly consider fairness via a secondary objective. Results are discussed and compared which provide a menu of alternatives to policy makers.     

Locating a semi-obnoxious covering facility with repelling polygonal regions

A facility is to be located in the Euclidean plane to serve certain sites by covering them closely. Simultaneously, a set of polygonal areas must be protected from the negative effects from that facility. The problem is formulated as a margin maximization model. Necessary optimality conditions are studied and a finite dominating set of solutions is obtained, leading to a polynomial algorithm. The method is illustrated on some examples.     

Undesirable facility location with minimal covering objectives

An undesirable facility is to be located within some feasible region of any shape in the plane or on a planar network. Population is supposed to be concentrated at a finite number n of points. Two criteria are taken into account: a radius of influence to be maximised, indicating within which distance from the facility population disturbance is taken into consideration, and the total covered population, i.e. lying within the influence radius from the facility, which should be minimised. Low complexity polynomial algorithms are derived to determine all nondominated solutions, of which there are only O(n³) for a fixed feasible region or O(n²) when locating on a planar network. Once obtained, this information allows almost instant answers and a trade-off sensitivity analysis to questions such as minimising the population within a given radius (minimal covering problem) or finding the largest circle not covering more than a given total population.     

A dynamic programming operator for tour location problems applied to the covering tour problem

This paper presents an evaluation operator for single-trip vehicle routing problems where it is not necessary to visit all the nodes. Such problems are known as Tour Location Problems. The operator, called Selector, is a dynamic programming algorithm that converts a given sequence of nodes into a feasible tour. The operator returns a subsequence of this giant tour which is optimal in terms of length. The procedure is implemented in an adaptive large neighborhood search to solve a specific tour location problem: the Covering Tour Problem. This problem consists in finding a lowest-cost Hamiltonian cycle over a subset of nodes such that nodes outside the tour are within a given distance from a visited node. The metaheuristic proposed is competitive as shown by the quality of results evaluated using the output of a state-of-the-art exact algorithm.     

A computational approach for eliminating error in the solution of the location set covering problem

The location set covering problem continues to be an important and challenging spatial optimization problem. The range of practical planning applications underscores its importance, spanning fire station siting, warning siren positioning, security monitoring and nature reserve design, to name but a few. It is challenging on a number of fronts. First, it can be difficult to solve for medium to large size problem instances, which are often encountered in combination with geographic information systems (GIS) based analysis. Second, the need to cover a region efficiently often brings about complications associated with the abstraction of geographic space. Representation as points can lead to significant gaps in actual coverage, whereas representation as polygons can result in a substantial overestimate of facilities needed. Computational complexity along with spatial abstraction sensitivity combine to make advances in solving this problem much needed. To this end, a solution framework for ensuring complete coverage of a region with a minimum number of facilities is proposed that eliminates potential error. Applications to emergency warning siren and fire station siting are presented to demonstrate the effectiveness of the developed approach. The approach can be applied to convex, non-convex and non-contiguous regions and is unaffected by arbitrary initial spatial representations of space.     

The minmax regret gradual covering location problem on a network with incomplete information of demand weights

The gradual covering location problem seeks to establish facilities on a network so as to maximize the total demand covered, allowing partial coverage. We focus on the gradual covering location problem when the demand weights associated with nodes of the network are random variables whose probability distributions are unknown. Using only information on the range of these random variables, this study is aimed at finding the “minmax regret” location that minimizes the worst-case coverage loss. We show that under some conditions, the problem is equivalent to known location problems (e.g. the minmax regret median problem). Polynomial time algorithms are developed for the problem on a general network with linear coverage decay functions.     

A continuous covering location model with risk consideration

This paper proposes a continuous covering location model with risk consideration. The investigated model is an extension of the discrete covering location models in continuous space. The objective function consists of installation and risk costs. Because of uncertain covering radius, customer satisfaction degree of covering radius is introduced by fuzzy concept. Since, the uncertainty may cause risk of uncovering customers; the risk cost is added to the objective function. The installation cost is assigned to a zone with a predetermined radius from its center. The model is solved by a fuzzy method named α$\alpha$-cut. After solving the model based on different α$\alpha$-values, the zones with the largest possibilities are determined for locating new facilities and the best locations are calculated based on the obtained possibilities. Then, the model is solved to determine the best covering values. This paper, also introduces a risk analysis method based on Response Surface Methodology (RSM) to consider risk management in the location models. Finally, a numerical example is expressed to illustrate the proposed model.     

Set covering location models with stochastic critical distances

This paper formulates a new version of set covering models by introducing a customer-determined stochastic critical distance. In this model, all services are provided at the sites of facilities, and customers have to go to the facility sites to obtain the services. Due to the randomness of their critical distance, customers patronize a far or near facility with a probability. The objective is to find a minimum cost set of facilities so that every customer is covered by at least one facility with an average probability greater than a given level α. We consider an instance of the problem by embedding the exponential effect of distance into the model. An algorithm based on two searching paths is proposed for solutions to the instance. Experiments show that the algorithm performs well for problems with greater α, and the experimental results for smaller α are reported and analysed.