Abstract
This paper presents a hybridization of Strategic Oscillation with Path Relinking to provide a set of high-quality nondominated solutions for the Multiobjective k-Balanced Center Location problem. The considered location problem seeks to locate k out of m facilities in order to serve n demand points, minimizing the maximum distance between any demand point and its closest facility while balancing the workload among the facilities. An extensive computational experimentation is carried out to compare the performance of our proposal, including the best method found in the state-of-the-art as well as traditional multiobjective evolutionary algorithms.
Highlights
This paper presents a hybridization of Strategic Oscillation with Path Relinking to provide a set of high-quality nondominated solutions for the Multiobjective k-Balanced Center Location problem
Facility Location Problems (FLPs) are classical optimization problems that require finding the best placement to locate a set of facilities, which must serve a set of demand points
The aim of the k-Balanced Center Location (k-BCL) problem is to locate k out of m facilities in order to serve a set of n demand points, with the aim of minimizing the maximum distance between each demand point and its closest selected facility (k-Center problem) while simultaneously balancing the number of demand points assigned to each selected facility (k-Balanced problem)
Summary
Facility Location Problems (FLPs) are classical optimization problems that require finding the best placement to locate a set of facilities, which must serve a set of demand points. Note that the term best depends on the measure employed to model the FLP The definition of this function is customized to the specific application, and it is typically set as a distance (or cost) function between demand points and facilities. We consider a biobjective, discrete, unweighted, incapacitated, deterministic FLP, recently introduced in Davoodi [12]: the k-Balanced Center Location problem (k-BCL problem). The aim of the k-BCL problem is to locate k out of m facilities in order to serve a set of n demand points, with the aim of minimizing the maximum distance between each demand point and its closest selected facility (k-Center problem) while simultaneously balancing the number of demand points assigned to each selected facility (k-Balanced problem).
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