Mixed integer programming-based solution procedure for single-facility location with maximin of rectilinear distance

In this paper, we study the 1-maximin problem with rectilinear distance. We locate a single undesirable facility in a continuous planar region while considering the interaction between the facility and existing demand points. The distance between facility and demand points is measured in the rectilinear metric. The objective is to maximize the distance of the facility from the closest demand point. The 1-maximin problem has been formulated as an MIP model in the literature. We suggest new bounding schemes to increase the solution efficiency of the model as well as improved branch and bound strategies for implementation. Moreover, we simplify the model by eliminating some redundant integer variables. We propose an efficient solution algorithm called cut and prune method, which splits the feasible region into four equal subregions at each iteration and tries to eliminate subregions depending on the comparison of upper and lower bounds. When the sidelengths of the subregions are smaller than a predetermined value, the improved MIP model is solved to obtain the optimal solution. Computational experiments demonstrate that the solution time of the original MIP model is reduced substantially by the proposed solution approach.     

A Parametric 1-Maximin Location Problem

We introduce a version of the weighted 1-maximin problem in a convex polygon, where the weights are functions of a parameter. The 1-maximin problem is applicable in the location of undesirable facilities. Its objective is to find an optimal location such that the minimum weighted distance to a given set of points is maximized. We show that the parametric 1-maximin problem is equivalent to a 1-minimax problem, where the costs are non-linearly decreasing functions of distance. Using different values of the parameter in the 1-maximin problem, one can model different disutility functions for the users of the facility. Furthermore, the parameterization provides for a systematic way of reducing the effects of the weights, resulting in the unweighted 1-maximin problem in the limit. For two example problems we construct the optimal trajectory as a function of the parameter, and demonstrate that the trajectory may be discontinuous.     

Efficient heuristics for the rectilinear distance capacitated multi-facility Weber problem

In this paper, we consider the capacitated multi-facility Weber problem with rectilinear distance. This problem is concerned with locating m capacitated facilities in the Euclidean plane to satisfy the demand of n customers with the minimum total transportation cost. The demand and location of each customer are known a priori and the transportation cost between customers and facilities is proportional to the rectilinear distance separating them. We first give a new mixed integer linear programming formulation of the problem by making use of a well-known necessary condition for the optimal facility locations. We then propose new heuristic solution methods based on this formulation. Computational results on benchmark instances indicate that the new methods can provide very good solutions within a reasonable amount of computation time.     

A multi-objective integrated facility location-hardening model: Analyzing the pre- and post-disruption tradeoff

Two methods of reducing the risk of disruptions to distribution systems are (1) strategically locating facilities to mitigate against disruptions and (2) hardening facilities. These two activities have been treated separately in most of the academic literature. This article integrates facility location and facility hardening decisions by studying the minimax facility location and hardening problem (MFLHP), which seeks to minimize the maximum distance from a demand point to its closest located facility after facility disruptions. The formulation assumes that the decision maker is risk averse and thus interested in mitigating against the facility disruption scenario with the largest consequence, an objective that is appropriate for modeling facility interdiction. By taking advantage of the MFLHP’s structure, a natural three-stage formulation is reformulated as a single-stage mixed-integer program (MIP). Rather than solving the MIP directly, the MFLHP can be decomposed into sub-problems and solved using a binary search algorithm. This binary search algorithm is the basis for a multi-objective algorithm, which computes the Pareto-efficient set for the pre- and post-disruption maximum distance. The multi-objective algorithm is illustrated in a numerical example, and experimental results are presented that analyze the tradeoff between objectives.     

A single facility stochastic location problem undera-distance

In this paper we consider a stochastic facility location model in which the weights of demand points are not deterministic but independent random variables, and the distance between the facility and each demand point isA-distance. Our objective is to find a solution which minimizes the total cost criterion subject to a chance constraint on cost restriction. We show a solution method which solves the problem in polynomial order computational time. Finally the case of rectilinear distance, which is used in many facility location models, is discussed.     

Is Linear Programming Necessary for Single Facility Location with Maximin of Rectilinear Distance?

This paper discusses the problem of locating a single obnoxious or undesirable facility so as to maximize its rectilinear distance from a given set of existing facilities. An outline of published linear programming methods is given. Based on these ideas we present an algorithm which exploits known properties of the optimal solution and does not use linear programming at all.     

Linear time optimal approaches for reverse obnoxious center location problems on networks

This paper is concerned with a reverse obnoxious (undesirable) center location problem on networks in which the aim is to modify the edge lengths within an associated budget such that a predetermined facility location on the underlying network becomes as far as possible from the existing customer points under the new edge lengths. Exact combinatorial algorithms with linear time complexities are developed for the problem under the weighted rectilinear norm and the weighted Hamming distance. Furthermore, it is shown that the problem with integer decision variables can also be solved in linear time.     

A decomposition approach for facility location and relocation problem with uncertain number of future facilities

In this paper, we discuss two challenges of long term facility location problem that occur simultaneously; future demand change and uncertain number of future facilities. We introduce a mathematical model that minimizes the initial and expected future weighted travel distance of customers. Our model allows relocation for the future instances by closing some of the facilities that were located initially and opening new ones, without exceeding a given budget. We present an integer programming formulation of the problem and develop a decomposition algorithm that can produce near optimal solutions in a fast manner. We compare the performance of our mathematical model against another method adapted from the literature and perform sensitivity analysis. We present numerical results that compare the performance of the proposed decomposition algorithm against the exact algorithm for the problem.     

On solving the discrete location problems when the facilities are prone to failure

The classical discrete location problem is extended here, where the candidate facilities are subject to failure. The unreliable location problem is defined by introducing the probability that a facility may become inactive. The formulation and the solution procedure have been motivated by an application to model and solve a large size problem for locating base stations in a cellular communication network. We formulate the unreliable discrete location problems as 0–1 integer programming models, and implement an enhanced dual-based solution method to determine locations of these facilities to minimize the sum of fixed cost and expected operating (transportation) cost. Computational tests of some well-known problems have shown that the heuristic is efficient and effective for solving these unreliable location problems.     

Rectilinear distance to a facility in the presence of a square barrier

This paper derives analytical expressions for the rectilinear distance to a facility in the presence of a square barrier. The distribution of the barrier distance is derived for two regular patterns of facilities: square and diamond lattices. This distribution, which provides all the information about the barrier distance, will be useful for facility location problems with barriers and reliability analysis of facility location. The distribution of the barrier distance demonstrates how the location and the size of the barrier affect the barrier distance. A?numerical example shows that the total barrier distance increases as the barrier gets closer to a facility, whereas the maximum barrier distance increases as the barrier becomes greater in size.     

Locating an undesirable facility with a minimax criterion

The problem considered in this paper involves the location of an undesirable facility such that the maximum weighted inverse square distance from the facility to n given points is minimized. The region in which the facility is to be located is bounded and contains the n points with which the facility to be located will interact. Applications can include siting an undesirable facility that produces some form of pollutant such as radiation, noise and some gases. Any problem that involves the location of a facility that emits pollutants whose concentrations follow the inverse square law is a candidate for the use of this work. A mathematical programming algorithm is developed for the situation in which the location problem involves a convex polygonal region. An interactive computer graphics approach is described for the case when the location problem involves a general region.     

Probabilistic Formulation of the Emergency Service Location Problem

The problem of locating emergency service facilities is studied under the assumption that the locations of incidents (accidents, fires, or customers) are random variables. The probability distribution for rectilinear travel time between a new facility location and the random location of the incident P _i is developed for the case of P _i being uniformly distributed over a rectangular region. The location problem is considered in a discrete space. A deterministic formulation is obtained and recognized to be a set cover problem. Probabilistic variation of the central facility location problem is also presented.An example and some computational experience are provided to emphasize the impact of the probabilistic formulation on the location decision.     

A branch and bound algorithm for locating input and output points of departments on the block layout

We consider the problem of locating input and output (I/O) points of each department for a given layout. The objective of the problem is to minimise the total distance of material flows between the I/O points. Here, distances between the I/O points are computed as the lengths of the shortest path (not the rectilinear distances) between the I/O points. We developed a procedure to eliminate dominated candidate positions of I/O points that do not need to be considered. With this procedure, a large number of dominated candidate positions can be eliminated. A linear programming (LP) model for minimising the total rectilinear distance of flows is used to obtain a lower bound. Using the elimination procedure and the LP model, a branch and bound algorithm is developed to find an optimal location of the I/O points. Results from computational experiments show that the suggested algorithm finds optimal solutions in a very short time even for large-sized problems.     

Finding efficient solutions for rectilinear distance location problems efficiently

Given n planar existing facility locations, a planar new facility location X is called efficient if there is no other location Y for which the rectilinear distance between Y and each existing facility is at least as small as between X and each existing facility, and strictly less for at least one existing facility. Rectilinear distances are typically used to measure travel distances between points via rectilinear aisles or street networks. We first present a simple arrow algorithm, based entirely on geometrical analysis, that constructs all efficient locations. We then present a row algorithm which is of order n(log n) that constructs all efficient locations, and establish that no alternative algorithm can be of a lower order.     

The obnoxious p facility network location problem with facility interaction

Three heuristics are proposed to solve the maximin formulation for siting p facilities on a network considering a pollution dispersion equation and facility interaction. Initially, the single facility problem is approached by building up polygons which model pollution spread about the nodes of the network. This is extended in the first heuristic to the p facility problem. The second combines both the p-maximin and p-maxisum objectives in a lexicographic manner. The third is based on recent developments of Simulated Annealing. The proposed heuristics are evaluated for up to six facilities on a set of randomly generated networks having 20 to 40 nodes and low or medium arc density.     

Continuous location model of a rectangular barrier facility

This paper develops a bi-objective model for determining the location, size, and shape of a finite-size facility. The objectives are to minimize both the closest and barrier distances. The closest distance represents the accessibility of customers, whereas the barrier distance represents the interference to travelers. The distributions of the closest and barrier distances are derived for a rectangular facility in a rectangular city where the distance is measured as the rectilinear distance. The analytical expressions for the distributions demonstrate how the location, size, and shape of the facility affect the closest and barrier distances. A numerical example shows that there exists a trade-off between the closest and barrier distances.     

A graph-pair representation and MIP-model-based heuristic for the unequal-area facility layout problem

Owing to its theoretical as well as practical significance, the facility layout problem with unequal-area departments has been studied for several decades, with a wide range of heuristic and a few exact solution procedures developed by numerous researchers. In one of the exact procedures, the facility layout problem is formulated as a mixed-integer programming (MIP) model in which binary (0/1) variables are used to prevent departments from overlapping with one another. Obtaining an optimal solution to the MIP model is difficult, and currently only problems with a limited number of departments can be solved to optimality. Motivated by this situation, we developed a heuristic procedure which uses a “graph pair” to determine and manipulate the relative location of the departments in the layout. The graph-pair representation technique essentially eliminates the binary variables in the MIP model, which allows the heuristic to solve a large number of linear programming models to construct and improve the layout in a comparatively short period of time. The search procedure to improve the layout is driven by a simulated annealing algorithm. The effectiveness of the proposed graph-pair heuristic is demonstrated by comparing the results with those reported in recent papers. Possible extensions to the graph-pair representation technique are discussed at the end of the paper.     

Capacitated facility location/network design problems

We introduce a combined facility location/network design problem in which facilities have constraining capacities on the amount of demand they can serve. This model has a number of applications in regional planning, distribution, telecommunications, energy management, and other areas. Our model includes the classical capacitated facility location problem (CFLP) on a network as a special case. We present a mixed integer programming formulation of the problem, and several classes of valid inequalities are derived to strengthen its LP relaxation. Computational experience with problems with up to 40 nodes and 160 candidate links is reported, and a sensitivity analysis provides insight into the behavior of the model in response to changes in key problem parameters.     

Algorithms for the generalized quadratic assignment problem combining Lagrangean decomposition and the Reformulation-Linearization Technique

In this paper, we propose two exact algorithms for the GQAP (generalized quadratic assignment problem). In this problem, given M facilities and N locations, the facility space requirements, the location available space, the facility installation costs, the flows between facilities, and the distance costs between locations, one must assign each facility to exactly one location so that each location has sufficient space for all facilities assigned to it and the sum of the products of the facility flows by the corresponding distance costs plus the sum of the installation costs is minimized. This problem generalizes the well-known quadratic assignment problem (QAP). Both exact algorithms combine a previously proposed branch-and-bound scheme with a new Lagrangean relaxation procedure over a known RLT (Reformulation-Linearization Technique) formulation. We also apply transformational lower bounding techniques to improve the performance of the new procedure. We report detailed experimental results where 19 out of 21 instances with up to 35 facilities are solved in up to a few days of running time. Six of these instances were open.     

A Linear Programming Approach to the Solution of Constrained Multi-Facility Minimax Location Problems where Distances are Rectangular

The problem of locating new facilities with respect to existing facilities is stated as a linear programming problem where inter-facility distances are assumed to be rectangular. The criterion of location is the minimization of the maximum weighted rectangular distance in the system. Linear constraints which (a) limit the new facility locations and (b) enforce upper bounds on the distances between new and existing facilities and between new facilities can be included. The dual programming problem is formulated in order to provide for an efficient solution procedure. It is shown that the duLal variables provide information abouLt the complete range of new facility locations which satisfy the minimax criterion.