On Covering Problems and the Central Facilities Location Problem

Abstract

A number of variations of facilities location problems have appeared in the research literature in the past decade. Among these are problems involving the location of multiple new facilities in a discrete solution space, with the new facilities located relative to a set of existing facilities having known locations. In this paper a number of discrete solution space location problems are treated. Specifically, the covering problem and the central facilities location problem are shown to be related. The covering problem involves the location of the minimum number of new facilities among a finite number of sites such that all existing facilities (customers) are cooered by at least one new facility. The central facilities location problem consists of the location of a given number of new facilities among a finite number of sites such that the sum of the weighted distances between existing facilities and new facilities is minimized. Computational experience in using the same heuristic solution procedure to solve both problems is provided and compared with other existing solution procedures.

Design and operation for an electric taxi fleet

“…For example, in electric vehicle charging terminal location, like in other critical applications such as ambulance and fire terminal location, the interest is to guarantee that the different geographic zones are covered by a facility (closer than a previously fixed covering distance). This class of problems are known as Covering Location Problems (see [WC74], [SVB93] and more recently [VP10] for a complete review of covering problems). In that context, the covering issue can be sometimes modelled as a problem constraint.…”

Handbook of EOQ Inventory Problems

“…They conclude that GA-based models can be practical when also information of Geographic Information Systems (GIS) can be incorporated. Covering problems consider the maximum distance that can be covered (Current et al 1990;White and Case 1974). The center problems do not use an exogenous maximal distance but let the model decide the distance endogenously such that the predetermined number of facilities can serve all customers (Tansel et al 1983).…”

Optimal Location of Compressed Natural Gas (CNG) Refueling Station Using the Arc Demand Coverage Model

“…In MCLP, the number of opened facilities is fixed and the objective is maximizing demand coverage by these facilities. In related article, White and Case [10] presented a special MCLP that the objective was maximizing the number of covered demand nodes. Church and Revelle used relaxed linear programming for MCLP, also solved this problem by using of branch and bound method [9].…”