Relaxation heuristics for the set multicover problem with generalized upper bound constraints
Shunji Umetani, Masanao Arakawa, Mutsunori Yagiura

TL;DR
This paper develops heuristic algorithms for the set multicover problem with generalized upper bound constraints, improving solution efficiency and quality for large-scale instances by introducing new evaluation schemes, local search, and path relinking.
Contribution
It introduces novel heuristic evaluation schemes and an efficient local search combined with path relinking to effectively handle GUB constraints in large-scale set multicover problems.
Findings
Effective variable selection for large instances
High-quality solutions with fewer promising variables
Superior performance on benchmark instances
Abstract
We consider an extension of the set covering problem (SCP) introducing (i)~multicover and (ii)~generalized upper bound (GUB)~constraints. For the conventional SCP, the pricing method has been introduced to reduce the size of instances, and several efficient heuristic algorithms based on such reduction techniques have been developed to solve large-scale instances. However, GUB constraints often make the pricing method less effective, because they often prevent solutions from containing highly evaluated variables together. To overcome this problem, we develop heuristic algorithms to reduce the size of instances, in which new evaluation schemes of variables are introduced taking account of GUB constraints. We also develop an efficient implementation of a 2-flip neighborhood local search algorithm that reduces the number of candidates in the neighborhood without sacrificing the solution…
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Taxonomy
TopicsVehicle Routing Optimization Methods · Advanced Graph Theory Research · Optimization and Search Problems
