A Stopping Rule for Facilities Location Algorithms

Love, Robert F.; Yeong, Wee Yong

doi:10.1080/05695558108974573

Cited by 28 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…derived the geometrical characterizations for the set of efficient, weakly efficient and properly efficient solutions of the Euclidean MFLP when it includes certain convex locational constraints. Love and Yoeng (1981), Hearn (1983), Juel (1984), and Love and Dowling (1989) explored the bounding method that continuously updates a lower bound on the optimal objective function value during each iteration. This method is based on the idea that the convex hull and the current value of the gradient determine an upper bound on the objective function's improvement.…”

Section: Euclidean Distance Minisum Location Problemmentioning

confidence: 99%

Demand Point Aggregation Analaysis for Location Models

Sadigh

Fallah

2009

Facility Location

View full text Add to dashboard Cite

Section: Euclidean Distance Minisum Location Problemmentioning

confidence: 99%

Demand Point Aggregation Analaysis for Location Models

Sadigh

Fallah

2009

Facility Location

View full text Add to dashboard Cite

“…However, a limitation of this approach is that the objective function is not differentiable at the demand points, so it could fail if the optimal solution coincides with one of the demand points (Kuhn 1973;Chandrasekaran and Tamir 1989;Wesolowsky 1993;Church and Murray 2009). Accounting for this is therefore necessary, and can be readily done (Love and Yeong 1981;Church and Murray 2009). Isodapanes, which are lines representing equal transportation cost, can also be used to solve the Weber problem (Weber 1909;Hoover 1937).…”

Section: Problem Specificationmentioning

confidence: 99%

Serving regional demand in facility location

Yao

Murray

2014

Papers in Regional Science

View full text Add to dashboard Cite

Location modelling is employed in urban and regional planning to site facilities that provide services of some sort. Issues to be considered usually include the number of facilities to locate, where to site those facilities and how demand is to be served. Given the geographic nature of location problems, a key issue is how to represent facilities and demand in geographic space. Traditionally, spatial abstraction as discrete demand is assumed as it simplifies model formulation and reduces computational complexity. However, errors in derived solutions are likely not negligible, especially when demand varies continuously across a region. This paper discusses a single facility location problem that considers demand to be continuously distributed and allows a facility to be located anywhere in space, the continuous Weber problem. An approach for dealing with continuous demand is proposed that is integrated through geographical information system (GIS) functionality. Empirical results highlight the advantages of the developed approach and the importance of solution integration with GIS.

show abstract

“…Elzinga and Hearn [4] have also proved the superiority of the second bound. A discussion of the computational merits of the two bounds is given by Love and Yeong [12]. A third bound is given by Drezner (31 for the single-facility case with Euclidean distances.…”

mentioning

confidence: 99%

Bounding methods for facilities location algorithms

Dowling

Love

1986

Naval Research Logistics

Self Cite

View full text Add to dashboard Cite

Single‐ and multi‐facility location problems are often solved with iterative computational procedures. Although these procedures have proven to converage, in practice it is desirable to be able to compute a lower bound on the objective function at each iteration. This enables the user to stop the iterative process when the objective function is within a prespecified tolerance of the optimum value. In this article we generalize a new bounding method to include multi‐facility problems with lp distances. A proof is given that for Euclidean distance problems the new bounding procedure is superior to two other known methods. Numerical results are given for the three methods.

show abstract

A Stopping Rule for Facilities Location Algorithms

Abstract: The single-facility location model with Euclidean distances and its multifacility and Q p distance

Cited by 28 publications

References 11 publications

Demand Point Aggregation Analaysis for Location Models

Demand Point Aggregation Analaysis for Location Models

Serving regional demand in facility location

Bounding methods for facilities location algorithms

Contact Info

Product

Resources

About