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

We consider an extension of the set covering problem (SCP) introducing(i)~multicover and (ii)~generalized upper bound (GUB)~constraints. For theconventional SCP, the pricing method has been introduced to reduce the size ofinstances, and several efficient heuristic algorithms based on such reductiontechniques have been developed to solve large-scale instances. However, GUBconstraints often make the pricing method less effective, because they oftenprevent solutions from containing highly evaluated variables together. Toovercome this problem, we develop heuristic algorithms to reduce the size ofinstances, in which new evaluation schemes of variables are introduced takingaccount of GUB constraints. We also develop an efficient implementation of a2-flip neighborhood local search algorithm that reduces the number ofcandidates in the neighborhood without sacrificing the solution quality. Inorder to guide the search to visit a wide variety of good solutions, we alsointroduce a path relinking method that generates new solutions by combining twoor more solutions obtained so far. According to computational comparison onbenchmark instances, the proposed method succeeds in selecting a small numberof promising variables properly and performs quite effectively even forlarge-scale instances having hard GUB constraints.

Relaxation heuristics for the set multicover problem with generalized upper bound constraints