English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper considers facility location problems in which a firm entering amarket seeks to open a set of available locations so as to maximize itsexpected market share, assuming that customers choose the alternative thatmaximizes a random utility function. We introduce a novel deterministicequivalent reformulation of this probabilistic model and, extending the resultsof previous studies, show that its objective function is submodular under anyrandom utility maximization model. This reformulation characterizes the demandbased on a finite set of preference profiles. Estimating their prevalencethrough simulation generalizes a sample average approximation method from theliterature and results in a maximum covering problem for which we develop a newbranch-and-cut algorithm. The proposed method takes advantage of thesubmodularity of the objective value to replace the least influentialpreference profiles by an auxiliary variable that is bounded by submodularcuts. This set of profiles is selected by a knee detection method. We provide atheoretical analysis of our approach and show that its computationalperformance, the solution quality it provides, and the efficiency of the kneedetection method it exploits are directly connected to the entropy of thepreference profiles in the population. Computational experiments on existingand new benchmark sets indicate that our approach dominates the classicalsample average approximation method on large instances, can outperform the bestheuristic method from the literature under the multinomial logit model, andachieves state-of-the-art results under the mixed multinomial logit model.

A model-free approach for solving choice-based competitive facility location problems using simulation and submodularity