Improved approximation guarantees for lower-bounded facility location problem

facility location;lower-bounded facility location;capacity-discounted facility location;local-search algorithm;Combinatorics and Optimization

Ahmadian, Sara

University of Waterloo

关键词: facility location;    lower-bounded facility location;    capacity-discounted facility location;    local-search algorithm;    Combinatorics and Optimization;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/5513/1/Ahmadian_Sara.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

We consider the lower-bounded facility location (LBFL) problem (, also known as load-balanced facility location), which is a generalization of uncapacitated facility location (UFL) problem where each open facility is required to serve a minimum number of clients. More formally, in the LBFL problem, we are given a set of clients Ɗ, a set of facilities Ƒ, a non-negative facility-opening cost f_i for each i ∈ Ƒ, a lower bound M, and a distance metric c(i,j) on the set Ɗ ∪ Ƒ, where c(i,j) denotes the cost of assigning client j to facility i. A feasible solution S specifies the set of open facilities F_S ⊆ Ƒ and the assignment of each client j to an open facility i(j) such that each open facility serves at least M clients. Our goal is to find feasible solution S that minimizes ∑_{i ∈ F_S} f_i + ∑_j c(i,j).The current best approximation ratio for LBFL is 550. We substantially advance the state-of-the-art for LBFL by devising an approximationalgorithm for LBFL that achieves a significantly-improved approximation guarantee of83.