Strategic facility location: A review

Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models

In this study, we consider facility location decisions for a humanitarian relief chain responding to quick-onset disasters. In particular, we develop a model that determines the number and locations of distribution centres in a relief network and the amount of relief supplies to be stocked at each distribution centre to meet the needs of people affected by the disasters. Our model, which is a variant of the maximal covering location model, integrates facility location and inventory decisions, considers multiple item types, and captures budgetary constraints and capacity restrictions. We conduct computational experiments to illustrate how the proposed model works on a realistic problem. Results show the effects of pre- and post-disaster relief funding on relief system's performance, specifically on response time and the proportion of demand satisfied. Finally, we discuss the managerial implications of the proposed model.

https://www.tandfonline.com/doi/pdf/10.1080/13675560701561789

Facility location in humanitarian relief

The maximum covering location model has been used extensively in analyzing locations for public service facilities. The model is extended to account for the chance that when a demand arrives at the system it will not be covered since all facilities capable of covering the demand are engaged serving other demands. An integer programming formulation of the new problem is presented. Several properties of the formulation are proven. A heuristic solution algorithm is presented and computational results with the algorithm are discussed. Directions for future study are also discussed.

A Maximum Expected Covering Location Model: Formulation, Properties and Heuristic Solution

Classical facility location models like the P-median problem (PMP) and the uncapacitated fixed-charge location problem (UFLP) implicitly assume that, once constructed, the facilities chosen will always operate as planned. In reality, however, facilities "fail" from time to time due to poor weather, labor actions, changes of ownership, or other factors. Such failures may lead to excessive transportation costs as customers must be served from facilities much farther than their regularly assigned facilities. In this paper, we present models for choosing facility locations to minimize cost, while also taking into account the expected transportation cost after failures of facilities. The goal is to choose facility locations that are both inexpensive under traditional objective functions and also reliable. This reliability approach is new in the facility location literature. We formulate reliability models based on both the PMP and the UFLP and present an optimal Lagrangian relaxation algorithm to solve them. We discuss how to use these models to generate a trade-off curve between the day-to-day operating cost and the expected cost, taking failures into account, and we use these trade-off curves to demonstrate empirically that substantial improvements in reliability are often possible with minimal increases in operating cost.

/pdf/reliability-models-for-facility-location-the-expected-40ennvpo72.pdf

Reliability Models for Facility Location: The Expected Failure Cost Case

The belief that mathematical location modeling can identify "optimal" location patterns rests on the basis that some realistic objective can be identified and by some measure quantified. For example, in the area of private facilities location analysis, a reasonably accurate statement of the objective of locating warehouses is to minimize the costs of manufacturing and distribution. Since most cost elements included in the objectives of private facility location can be reasonably estimated, the models can picture with some degree of accuracy the real location problem they are designed to solve. Unlike private facility location analysis, the objectives of public facility location analysis are more difficult to embrace and to quantify. The difficulty in defining direct measures of public objectives has resulted in a search for some surrogate measure with which the decision maker may be comfortable. Two different surrogate measures which have received attention in location models are: (1) total weighted distance or time for travel to the facilities, and (2) the distance or time that the user most distant from a facility would have to travel to reach that facility, that is, the maximal service distance.* The use of a maximal service distance as a measure of the value of a given locational configuration has been discussed at length by Toregas and ReVelle 1 who show that it is an important surrogate measurement for the value of a given locational configuration. For a given location solution, the maximum distance which any user would have to travel to reach a facility would reflect the worst possible performance of the system. In the regional location of emergency facilities such as fire stations or ambulance dispatching stations, the concept of the maximal service distance is well established. ~,3 The maximal service distance concept appears in the Location Set Covering

Charles R. Velle

Papers

The maximal covering location problem