Heuristics for the facility location and design (1|1)-centroid problem on the plane

A chain (the leader) wants to set up a single new facility in a planar market where similar facilities of a competitor (the follower), and possibly of its own chain, are already present. The follower will react by locating another single facility after the leader locates its own facility. Fixed demand points split their demand probabilistically over all facilities in the market in proportion to their attraction to each facility, determined by the different perceived qualities of the facilities and the distances to them, through a gravitational model. Both the location and the quality (design) of the new leader’s facility are to be found. The aim is to maximize the profit obtained by the leader following the follower’s entry. Four heuristics are proposed for this hard-to-solve global optimization problem, namely, a grid search procedure, an alternating method and two evolutionary algorithms. Computational experiments show that the evolutionary algorithm called UEGO_cent.SASS provides the best results.     

A two-level evolutionary algorithm for solving the facility location and design (1|1)-centroid problem on the plane with variable demand

In this work, the problem of a company or chain (the leader) that considers the reaction of a competitor chain (the follower) is studied. In particular, the leader wants to set up a single new facility in a planar market where similar facilities of the follower, and possibly of its own chain, are already present. The follower will react by locating another single facility after the leader locates its own facility. Both the location and the quality (representing design, quality of products, prices, etc.) of the new leader’s facility have to be found. The aim is to maximize the profit obtained by the leader considering the future follower’s entry. The demand is supposed to be concentrated at n demand points. Each demand point splits its buying power among the facilities proportionally to the attraction it feels for them. The attraction of a demand point for a facility depends on both the location and the quality of the facility. Usually, the demand is considered in the literature to be fixed or constant regardless the conditions of the market. In this paper, the demand varies depending on the attraction for the facilities. Taking variable demand into consideration makes the model more realistic. However, it increases the complexity of the problem and, therefore, the computational effort needed to solve it. Three heuristic methods are proposed to cope with this hard-to-solve global optimization problem, namely, a grid search procedure, a multistart algorithm and a two-level evolutionary algorithm. The computational studies show that the evolutionary algorithm is both the most robust algorithm and the one that provides the best results.     

A discrete competitive facility location model with variable attractiveness

We consider the discrete version of the competitive facility location problem in which new facilities have to be located by a new market entrant firm to compete against already existing facilities that may belong to one or more competitors. The demand is assumed to be aggregated at certain points in the plane and the new facilities can be located at predetermined candidate sites. We employ Huff's gravity-based rule in modelling the behaviour of the customers where the probability that customers at a demand point patronize a certain facility is proportional to the facility attractiveness and inversely proportional to the distance between the facility site and demand point. The objective of the firm is to determine the locations of the new facilities and their attractiveness levels so as to maximize the profit, which is calculated as the revenue from the customers less the fixed cost of opening the facilities and variable cost of setting their attractiveness levels. We formulate a mixed-integer nonlinear programming model for this problem and propose three methods for its solution: a Lagrangean heuristic, a branch-and-bound method with Lagrangean relaxation, and another branch-and-bound method with nonlinear programming relaxation. Computational results obtained on a set of randomly generated instances show that the last method outperforms the others in terms of accuracy and efficiency and can provide an optimal solution in a reasonable amount of time.     

A cover-based competitive location model

In this paper we propose a new approach to estimating market share captured by competing facilities. The approach is based on cover location models. Each competing facility has a ‘sphere of influence’ determined by its attractiveness level. More attractive facilities have a larger radius of the sphere of influence. The buying power of a customer within the sphere of influence of several facilities is equally divided among the competing facilities. The buying power of a customer within the sphere of influence of no facility is lost. Assuming the presence of competition in the area, the objective is to add a number of new facilities to a chain of existing facilities in such a way that the increase of market share captured by the chain is maximized. The model is formulated and analysed. Optimal and heuristic solution algorithms are designed. Computational experiments demonstrate the effectiveness of the proposed algorithms.     

Optimizing pricing and location decisions for competitive service facilities charging uniform price

In this paper, we present the problem of optimizing the location and pricing for a set of new service facilities entering a competitive marketplace. We assume that the new facilities must charge the same (uniform) price and the objective is to optimize the overall profit for the new facilities. Demand for service is assumed to be concentrated at discrete demand points (customer markets); customers in each market patronize the facility providing the highest utility. Customer demand function is assumed to be elastic; the demand is affected by the price, facility attractiveness, and the travel cost for the highest-utility facility. We provide both structural and algorithmic results, as well as some managerial insights for this problem. We show that the optimal price can be selected from a certain finite set of values that can be computed in advance; this fact is used to develop an efficient mathematical programming formulation for our model.     

Profit maximization and reduction of the cannibalization effect in chain expansion

We consider the facility location problem for an expanding chain which competes with other chains offering the same goods or service in a geographical area. Customers are supposed to select the facility with maximum utility to be served and facilities in the expanding chain may have different owners. We first use the weighted method to develop an integer linear programming model to obtain Pareto optimal locations related to the inner competition between the owners of the old facilities and the owners of the new facilities. This model is applied to maximizing the profit of the expanding chain taking into account the loss in market share of its old facilities caused by the entering of new facilities (cannibalization effect). A study with data of Spanish municipalities shows that the cannibalization effect can be significantly reduced by sacrificing a small portion of profit.     

A robust model for a leader–follower competitive facility location problem in a discrete space

This paper aims at determining the optimal locations for the leader’s new facilities under the condition that the number of the follower’s new facilities is unknown for the leader. The leader and the follower have some facilities in advance. The first competitor, the leader, opens p new facilities in order to increase her own market share. On the other hand, she knows that her competitor, the follower, will react to her action and locate his new facilities as well. The number of the follower’s new facilities is unknown for the leader but it is assumed that the leader knows the probability of opening different numbers of the follower’s new facilities. The leader aims at maximizing her own market share after the follower’s new facilities entry. The follower’s objective is also to maximize his own market share. Since the number of the follower’s new facilities is unknown for leader, “Robust Optimization” is used for maximizing the leader’s market share and making the obtained results “robust” in various scenarios in terms of different numbers of the follower’s new facilities. The optimal locations for new facilities of both the leader and the follower are chosen among pre-determined potential locations. It is assumed that the demand is inelastic. The customers probabilistically meet their demands from all different facilities and the demand level which is met by each facility is computed by Huff rule. The computational experiments have been applied to evaluate the efficiency of the proposed model.     

Competitive facility location problem with attractiveness adjustment of the follower: A bilevel programming model and its solution

We are concerned with a problem in which a firm or franchise enters a market by locating new facilities where there are existing facilities belonging to a competitor. The firm aims at finding the location and attractiveness of each facility to be opened so as to maximize its profit. The competitor, on the other hand, can react by adjusting the attractiveness of its existing facilities with the objective of maximizing its own profit. The demand is assumed to be aggregated at certain points in the plane and the facilities of the firm can be located at predetermined candidate sites. We employ Huff’s gravity-based rule in modeling the behavior of the customers where the fraction of customers at a demand point that visit a certain facility is proportional to the facility attractiveness and inversely proportional to the distance between the facility site and demand point. We formulate a bilevel mixed-integer nonlinear programming model where the firm entering the market is the leader and the competitor is the follower. In order to find the optimal solution of this model, we convert it into an equivalent one-level mixed-integer nonlinear program so that it can be solved by global optimization methods. Apart from reporting computational results obtained on a set of randomly generated instances, we also compute the benefit the leader firm derives from anticipating the competitor’s reaction of adjusting the attractiveness levels of its facilities. The results on the test instances indicate that the benefit is 58.33% on the average.     

Strategic competitive location: improving existing and establishing new facilities

Competitive facility location models consider two main strategies for increasing the market share captured by a chain subject to a budget constraint. One strategy is the improvement of existing facilities. The second strategy is the construction of new facilities. In this paper we analyse these two strategies as well as the joint strategy which is a combination of the two. All three strategies are formulated as a unified model. The best solution to an individual strategy is a feasible solution to the joint one. Therefore, the joint strategy must yield solutions that are at least as good as the solutions to each of the individual strategies. Based on the results of extensive experiments, we conclude that the increase in market share captured by a chain when the joint strategy is employed can be significantly higher than increases obtained by individual strategies. A branch and bound procedure and a tabu search heuristic are constructed for the solution of the unified model. Both algorithms performed very well on a set of test problems with up to 900 demand points. A total of 62% of the test problems were optimally solved by the branch and bound procedure.     

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.     

Modelling lost demand in competitive facility location

In this paper, we propose a simple new approach to model lost demand (also referred to as elastic demand) in competitive facility location. A ‘dummy’ competing facility that attracts the lost demand is added to the list of competing facilities. All competitive facility location models, regardless of their complexity or assumptions, can be modified to include lost demand and be solved by the same algorithms designed for standard models once the dummy facility is added to the data as an additional competitor.     

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.     

A multi-objective genetic algorithm for a bi-objective facility location problem with partial coverage

In this study, we present a bi-objective facility location model that considers both partial coverage and service to uncovered demands. Due to limited number of facilities to be opened, some of the demand nodes may not be within full or partial coverage distance of a facility. However, a demand node that is not within the coverage distance of a facility should get service from the nearest facility within the shortest possible time. In this model, it is assumed that demand nodes within the predefined distance of opened facilities are fully covered, and after that distance the coverage level decreases linearly. The objectives are defined as the maximization of full and partial coverage, and the minimization of the maximum distance between uncovered demand nodes and their nearest facilities. We develop a new multi-objective genetic algorithm (MOGA) called modified SPEA-II (mSPEA-II). In this method, the fitness function of SPEA-II is modified and the crowding distance of NSGA-II is used. The performance of mSPEA-II is tested on randomly generated problems of different sizes. The results are compared with the solutions of the most well-known MOGAs, NSGA-II and SPEA-II. Computational experiments show that mSPEA-II outperforms both NSGA-II and SPEA-II.     

Competitive facility location and design with reactions of competitors already in the market

A new retail facility is to locate and its service quality is to determine where similar facilities of competitors offering the same goods are already present. The market share captured by each facility depends on its distance to customers and its quality, which is described by a probabilistic Huff-like model. In order to maximize the profit of the new facility, a two-stage method is developed, which takes into account the reactions of the competitors. In the quality decision stage, the competitive decision process occurring among facilities is modelled as a game, whose solution is given by its Nash equilibrium. The solution, which can be represented as functions of the location of the new facility, is obtained by analytical resolution of a system of equations in the case of one facility in the market or by polynomial approximation in the case of multiple facilities. In the location decision stage, an interval based global optimization method is used to determine the best location of the new facility. Numerical experiments on randomly generated instances demonstrate the effectiveness of the method.     

Location of Multiple-Server Congestible Facilities for Maximizing Expected Demand, when Services are Non-Essential

We formulate a model for locating multiple-server, congestible facilities. Locations of these facilities maximize total expected demand attended over the region. The effective demand at each node is elastic to the travel time to the facility, and to the congestion at that facility. The facilities to be located are fixed, so customers travel to them in order to receive service or goods, and the demand curves at each demand node (which depend on the travel time and the queue length at the facility), are known. We propose a heuristic for the resulting integer, nonlinear formulation, and provide computational experience.     

An analytical approach to the facility location and capacity acquisition problem under demand uncertainty

This article presents an analysis of facility location and capacity acquisition under demand uncertainty. A novel methodology is proposed, in which the focus is shifted from the precise representation of facility locations to the market areas they serve. This is an extension of the optimal market area approach in which market area size and facility capacity are determined to minimize the total cost associated with fixed facility opening, variable capacity acquisition, transportation, and shortage. The problem has two variants depending on whether the firm satisfies shortages by outsourcing or shortages become lost sales. The analytical approach simplifies the problem considerably and leads to intuitive and insightful models. Among several other results, it is shown that fewer facilities are set up under lost sales than under outsourcing. It is also shown that the total cost in both models is relatively insensitive to small deviations in optimal capacity choices and parameter estimations.     

Approximation algorithms for supply chain planning and logistics problems with market choice

We propose generalizations of a broad class of traditional supply chain planning and logistics models that we call supply chain planning and logistics problems with market choice. Instead of the traditional setting, we are given a set of markets, each specified by a sequence of demands and associated with a revenue. Decisions are made in two stages. In the first stage, one chooses a subset of markets and rejects the others. Once that market choice is made, one needs to construct a minimum-cost production plan (set of facilities) to satisfy all of the demands of all the selected markets. The goal is to minimize the overall lost revenues of rejected markets and the production (facility opening and connection) costs. These models capture important aspects of demand shaping within supply chain planning and logistics models. We introduce a general algorithmic framework that leverages existing approximation results for the traditional models to obtain corresponding results for their counterpart models with market choice. More specifically, any LP-based α-approximation for the traditional model can be leveraged to a \frac11-e^-1/a{\frac{1}{1-e^{-1/\alpha}}} -approximation algorithm for the counterpart model with market choice. Our techniques are also potentially applicable to other covering problems.     

A Procedure for Locating Emergency-Service Facilities for All Possible Response Distances

The problem of locating emergency-service facilities involves the assignment of a set of demand points to a set of facilities. One way to formulate the problem is to minimize the number of required facilities, given that the maximum distance between the demand points and their nearest facility does not exceed some specified value. We present a procedure for determining the numbers of such facilities for all possible values of the maximum distance. Computational results are presented for a microcomputer implementation.     

Optimal timing decisions for the introduction of new technologies

High technology industries, such as the communications industry, are characterized by frequent development of new technologies. These new technologies are often available before the capacities of existing facilities that use an old technology are exhausted. Whenever a new technology facility is introduced, a fixed set-up cost is generally incurred; however, the annual operating costs are often reduced. The optimal timing of the introduction of new facilities is therefore of interest.In this paper, we examine such timing decisions. The study was motivated by an application involving electronic plug-in units that enhance the operation of communication facilities. First, we develop optimal timing decisions for linearly growing demand. The analysis is then extended to nonlinear demand. For linear demand, one of two decisions is optimal: Either introduce the new technology immediately, or as late as possible. However, for nonlinear demand, these decisions may be nonoptimal.     

Customer Allocation in Maximum Capture Problems

The maximum capture (MAXCAP) model and its variants have been widely used to find the maximum capture that a firm can get as it enters a spatial market where there are already existing (competitor??s) facilities. While the model obtains the optimal demand capture, it however allows the customers to be assigned to the non-closest facility which may incur additional operating costs. A two stage method can be used that overcomes the drawback of the original model while requiring a negligible extra computational effort. To make the original model mathematically self contained and more concise two revised formulations of the problem RMAXCAP-1 and RMAXCAP-2 are proposed which assure that the customers patronize only their closest entering facilities. These models are tested on different sizes of datasets and their performances are compared.