An effective and simple heuristic for the set covering problem

• Published in: European journal of operational research Vol. 176; no. 3; pp. 1387 - 1403
• Main Authors: Lan, Guanghui, DePuy, Gail W., Whitehouse, Gary E.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.02.2007; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: Algorithms; Applied sciences; Combinatorial optimization; Construction; Exact sciences and technology; Flows in networks. Combinatorial problems; Heuristic; Meta-RaPS; Operational research and scientific management; Operational research. Management science; Problem solving; Set covering; Studies; Combinatorial optimization; Set covering; Meta-RaPS; Randomization; Covering problem; Priority; Heuristic method; Coverage; Selection rule
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2005.09.028

This paper investigates the development of an effective heuristic to solve the set covering problem (SCP) by applying the meta-heuristic Meta-RaPS (Meta-heuristic for Randomized Priority Search). In Meta-RaPS, a feasible solution is generated by introducing random factors into a construction method. Then the feasible solutions can be improved by an improvement heuristic. In addition to applying the basic Meta-RaPS, the heuristic developed herein integrates the elements of randomizing the selection of priority rules, penalizing the worst columns when the searching space is highly condensed, and defining the core problem to speedup the algorithm. This heuristic has been tested on 80 SCP instances from the OR-Library. The sizes of the problems are up to 1000 rows × 10,000 columns for non-unicost SCP, and 28,160 rows × 11,264 columns for the unicost SCP. This heuristic is only one of two known SCP heuristics to find all optimal/best known solutions for those non-unicost instances. In addition, this heuristic is the best for unicost problems among the heuristics in terms of solution quality. Furthermore, evolving from a simple greedy heuristic, it is simple and easy to code. This heuristic enriches the options of practitioners in the optimization area.