Location of retail facilities under conditions of uncertainty

Models for the optimal location of retail facilities are typically premised on current market conditions. In this paper we incorporate future market conditions into the model for the location of a retail facility. Future market conditions are analyzed as a set of possible scenarios. We analyze the problem of finding the best location for a new retail facility such that the market share captured at that location is as close to the maximum as possible regardless of the future scenario. The objective is the minimax regret which is widely used in decision analysis. To illustrate the models an example problem is analyzed and solved in detail.     

Simple dynamic location problem with uncertainty: a primal-dual heuristic approach

Abstract

In this paper, the simple dynamic facility location problem is extended to uncertain realizations of the potential locations for facilities and the existence of customers as well as fixed and variable costs. With limited knowledge about the future, a finite and discrete set of scenarios is considered. The decisions to be made are where and when to locate the facilities, and how to assign the existing customers over the whole planning horizon and under each scenario, in order to minimize the expected total costs. Whilst assignment decisions can be scenario dependent, location decisions have to take into account all possible scenarios and cannot be changed according to each scenario in particular. We first propose a mixed linear programming formulation for this problem and then we present a primal-dual heuristic approach to solve it. The heuristic was tested over a set of randomly generated test problems. The computational results are provided.     

A dynamic vehicle routing problem with multiple delivery routes

This paper considers a vehicle routing problem where each vehicle performs delivery operations over multiple routes during its workday and where new customer requests occur dynamically. The proposed methodology for addressing the problem is based on an adaptive large neighborhood search heuristic, previously developed for the static version of the problem. In the dynamic case, multiple possible scenarios for the occurrence of future requests are considered to decide about the opportunity to include a new request into the current solution. It is worth noting that the real-time decision is about the acceptance of the new request, not about its service which can only take place in some future routes (a delivery route being closed as soon as a vehicle departs from the depot). In the computational results, a comparison is provided with a myopic approach which does not consider scenarios of future requests.     

The p-median problem under uncertainty

Consider the need to currently locate p facilities but it is possible that up to q additional facilities will have to be located in the future. There are known probabilities that 0 ? r ? q facilities will need to be located. The p-median problem under uncertainty is to find the location of p facilities such that the expected value of the objective function in the future is minimized. The problem is formulated on a graph, properties of it are proven, an integer programming formulation is constructed, and heuristic algorithms are suggested for its solution. The heuristic algorithms are modified to reduce the run time by about two orders of magnitude with minimal effect on the quality of the solution. Optimal solutions for many problems are found effectively by CPLEX. Computational results using the heuristic algorithms are presented.     

Bounds on the Optimal Location to the Weber Problem under Conditions of Uncertainty

Optimal and Heuristic bounds are given for the optimal location to the Weber problem when the locations of demand points are not deterministic but may be within given circles. Rectilinear, Euclidean and square Euclidean types of distance measure are discussed. The exact shape of all possible optimal points is given in the rectilinear and square Euclidean cases. A heuristic method for the computation of the region of possible optimal points is developed in the case of Euclidean distance problem. The maximal distance between a possible optimal point and the deterministic solution is also computed heuristically.     

最小最大后悔准则下新增设施选址策略研究

基于新增设施选址问题,考虑网络节点权重不确定性,以设施中最大负荷量最小为目标,提出最小最大后悔准则下的新增设施选址问题。在网络节点权重确定时,通过证明将网络图中无穷多个备选点离散为有限个设施候选点,设计了时间复杂度为O(mn²)的多项式算法;在节点权重为区间值时,通过分析最大后悔值对应的最坏情境权重结构,进而确定最大后悔值最小的选址,提出时间复杂度为O(2ⁿm²n³)的求解算法;最后给出数值算例。     

Locating disaster response facilities in Istanbul

We study the problem of locating disaster response and relief facilities in the city of Istanbul, where a massively destructive earthquake is expected to occur in the near future. The Metropolitan Municipality of Istanbul decided to establish facilities to preposition relief aid and execute post-disaster response operations. We propose a two-tier distribution system that utilizes existing public facilities locally in addition to the new facilities that will act as regional supply points. We develop mathematical models to decide on the locations of the new facilities with the objectives of minimizing the average-weighted distance between casualty locations and closest facilities, and opening a small number of facilities, subject to distance limits and backup requirements under regional vulnerability considerations. We analyze the trade-offs between these two objectives under various disaster scenarios and investigate the solutions for several modelling extensions. The results demonstrate that a small number of facilities will be sufficient and their locations are robust to various parameter and modelling changes.     

Some Properties for the Euclidean Distance Matrix and Positive Semidefinite Matrix Completion Problems

The Euclidean distance matrix (EDM) completion problem and the positive semidefinite (PSD) matrix completion problem are considered in this paper. Approaches to determine the location of a point in a linear manifold are studied, which are based on a referential coordinate set and a distance vector whose components indicate the distances from the point to other points in the set. For a given referential coordinate set and a corresponding distance vector, sufficient and necessary conditions are presented for the existence of such a point that the distance vector can be realized. The location of the point (if it exists) given by the approaches in a linear manifold is independent of the coordinate system, and is only related to the referential coordinate set and the corresponding distance vector. An interesting phenomenon about the complexity of the EDM completion problem is described. Some properties about the uniqueness and the rigidity of the conformation for solutions to the EDM and PSD completion problems are presented.     

Information enhancement—A tool for approximate representation of optimal strategies from influence diagrams

The main source of complexity problems for large influence diagrams is that the last decisions have intractably large spaces of past information. Usually, it is not a problem when you reach the last decisions; but when calculating optimal policies for the first decisions, you have to consider all possible future information scenarios. This is the curse of knowing that you shall not forget. The usual approach for addressing this problem is to reduce the information through assuming that you do forget something (Nilsson and Lauritzen, 2000, LIMID [1]), or to abstract the information through introducing new nodes (Jensen, 2008) [2]. This paper takes the opposite approach, namely to assume that you know more in the future than you actually will. We call the approach information enhancement. It consists in reducing the space of future information scenarios by adding information links. We present a systematic way of determining fruitful information links to add.     

Dynamic facility location when the total number of facilities is uncertain: A decision analysis approach

Models developed to analyze facility location decisions have typically optimized one or more objectives, subject to physical, structural, and policy constraints, in a static or deterministic setting. Because of the large capital outlays that are involved, however, facility location decisions are frequently long-term in nature. Consequently, there may be considerable uncertainty regarding the way in which relevant parameters in the location decision will change over time. In this paper, we propose two approaches for analyzing these types of dynamic location problems, focussing on situations where the total number of facilities to be located in uncertain. We term this type of location problem NOFUN (Number Of Facilities Uncertain). We analyze the NOFUN problem using two well-established decision criteria: the minimization of expected opportunity loss (EOL), and the minimization of maximum regret. In general, these criteria assume that there are a finite number of decision options and a finite number of possible states of nature. The minisum EOL criterion assumes that one can assign probabilities for the occurrence of the various states of nature and, therefore, find the initial set of facility locations that minimize the sum of expected losses across all future states. The minimax regret criteria finds the pattern of initial facility locations whose maximum loss is minimized over all possible future states.     

Financial scenario generation for stochastic multi-stage decision processes as facility location problems

The quality of multi-stage stochastic optimization models as they appear in asset liability management, energy planning, transportation, supply chain management, and other applications depends heavily on the quality of the underlying scenario model, describing the uncertain processes influencing the profit/cost function, such as asset prices and liabilities, the energy demand process, demand for transportation, and the like. A common approach to generate scenarios is based on estimating an unknown distribution and matching its moments with moments of a discrete scenario model. This paper demonstrates that the problem of finding valuable scenario approximations can be viewed as the problem of optimally approximating a given distribution with some distance function. We show that for Lipschitz continuous cost/profit functions it is best to employ the Wasserstein distance. The resulting optimization problem can be viewed as a multi-dimensional facility location problem, for which at least good heuristic algorithms exist. For multi-stage problems, a scenario tree is constructed as a nested facility location problem. Numerical convergence results for financial mean-risk portfolio selection conclude the paper.     

Probability maximization models for portfolio selection under ambiguity

This paper considers several probability maximization models for multi-scenario portfolio selection problems in the case that future returns in possible scenarios are multi-dimensional random variables. In order to consider occurrence probabilities and decision makers’ predictions with respect to all scenarios, a portfolio selection problem setting a weight with flexibility to each scenario is proposed. Furthermore, by introducing aspiration levels to occurrence probabilities or future target profit and maximizing the minimum aspiration level, a robust portfolio selection problem is considered. Since these problems are formulated as stochastic programming problems due to the inclusion of random variables, they are transformed into deterministic equivalent problems introducing chance constraints based on the stochastic programming approach. Then, using a relation between the variance and absolute deviation of random variables, our proposed models are transformed into linear programming problems and efficient solution methods are developed to obtain the global optimal solution. Furthermore, a numerical example of a portfolio selection problem is provided to compare our proposed models with the basic model.     

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 procedure for the strategic planning of locations,capacities and districting of jails: application to Chile

Using the Chilean jail system as a case study, we present a model for finding the location of a number of new jails, as well as the size and districting of new and existing jails, so that the cost of the system is minimized. We propose a minimum regret procedure for choosing a good solution, given that several future scenarios are possible. The jail capacities range from a few tens to 2000 inmates. The population is composed of pre-trial detainees, people under trial and sentenced offenders. Each one of these categories has different requirements, in terms of the site of their confinement. Inmates stay in the system for periods that go from a few days to life sentences. Some overcrowding is allowed, but penalized by the model. Several scenarios of future population are used for finding a solution for the Chilean system, where there are some 33?000 inmates.     

考虑顾客便利半径和质量阈值的竞争设施选址问题

在竞争设施选址问题中,顾客选择行为是决定设施占领市场份额的重要因素,其描述了需求在设施之间的分配方式。为了贴近顾客真实的光顾行为,本文提出了一种考虑顾客便利半径和质量阈值的顾客选择规则,并研究了在该规则下市场中新进入公司的竞争设施选址问题。提出了一种基于排名的遗传算法(RGA)求解该问题,并将该算法与经典遗传算法(GA)和基于排名的离散优化算法(RDOA)进行了比较,结果说明了算法的有效性以及模型中质量阈值的重要性。     

Implementation of Health Facilities in a New City

A new city is under construction in a developing country. The town will be organized in modules, and the population projection is established till the 90s. The ratio of medical personnel to inhabitants is fixed according to health policy criteria. The primary care system should be composed of a set of health centres which are identical with regard to equipment and personnel. The problem is to determine the number--and thus the size--of the health centres, and their location. The solution depends on two opposing factors: the total construction cost, which is increasing with the number of centres, and the walking distance for the patient, which is decreasing with the number of centres. For a given critical distance, we find--using techniques of location theory in network--the smallest number of centres that will ensure that all inhabitants are located within the critical distance. A Fortran program in which the sensitivity of the solution is studied as a function of the given critical distance is developed.     

Strategic facility location: A review

Facility location decisions are a critical element in strategic planning for a wide range of private and public firms. The ramifications of siting facilities are broadly based and long-lasting, impacting numerous operational and logistical decisions. High costs associated with property acquisition and facility construction make facility location or relocation projects long-term investments. To make such undertakings profitable, firms plan for new facilities to remain in place and in operation for an extended time period. Thus, decision makers must select sites that will not simply perform well according to the current system state, but that will continue to be profitable for the facility's lifetime, even as environmental factors change, populations shift, and market trends evolve. Finding robust facility locations is thus a difficult task, demanding that decision makers account for uncertain future events. The complexity of this problem has limited much of the facility location literature to simplified static and deterministic models. Although a few researchers initiated the study of stochastic and dynamic aspects of facility location many years ago, most of the research dedicated to these issues has been published in recent years. In this review, we report on literature which explicitly addresses the strategic nature of facility location problems by considering either stochastic or dynamic problem characteristics. Dynamic formulations focus on the difficult timing issues involved in locating a facility (or facilities) over an extended horizon. Stochastic formulations attempt to capture the uncertainty in problem input parameters such as forecast demand or distance values. The stochastic literature is divided into two classes: that which explicitly considers the probability distribution of uncertain parameters, and that which captures uncertainty through scenario planning. A wide range of model formulations and solution approaches are discussed, with applications ranging across numerous industries.     

Condorcet Winners for Public Goods

In this work, we consider a public facility allocation problem decided through a voting process under the majority rule. A location of the public facility is a majority rule winner if there is no other location in the network where more than half of the voters would have been closer to than the majority rule winner. We develop fast algorithms for interesting cases with nice combinatorial structures. We show that the computing problem and the decision problem in the general case, where the number of public facilities is more than one and is considered part of the input size, are all NP-hard. Finally, we discuss majority rule decision making for related models.     

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.     

Solving the Simple Plant Location Problem using a Data Correcting Approach

The Data Correcting Algorithm is a branch and bound type algorithm in which the data of a given problem instance is `corrected' at each branching in such a way that the new instance will be as close as possible to a polynomially solvable instance and the result satisfies an acceptable accuracy (the difference between optimal and current solution). In this paper the data correcting algorithm is applied to determining exact and approximate optimal solutions to the simple plant location problem. Implementations of the algorithm are based on a pseudo-Boolean representation of the goal function of this problem, and a new reduction rule. We study the efficiency of the data correcting approach using two different bounds, the Khachaturov-Minoux bound and the Erlenkotter bound. We present computational results on several benchmark instances, which confirm the efficiency of the data-correcting approach.