×

A study of greedy, local search, and ant colony optimization approaches for car sequencing problems. (English) Zbl 1033.90503

Cagnoni, Stefano (ed.) et al., Applications of evolutionary computing. EvoWorkshops 2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, and EvoSTIM, Essex, UK, April 14–16, 2003. Proceedings. Berlin: Springer (ISBN 3-540-00976-0/pbk). Lect. Notes Comput. Sci. 2611, 246-257 (2003).
Summary: This paper describes and compares several heuristic approaches for the car sequencing problem. We first study greedy heuristics, and show that dynamic ones clearly outperform their static counterparts. We then describe local search and ant colony optimization (ACO) approaches, that both integrate greedy heuristics, and experimentally compare them on benchmark instances. ACO yields the best solution quality for smaller time limits, and it is comparable to local search for larger limits. Our best algorithms proved one instance being feasible, for which it was formerly unknown whether it is satisfiable or not.
For the entire collection see [Zbl 1017.68610].

MSC:

90B20 Traffic problems in operations research
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
90C27 Combinatorial optimization
90C59 Approximation methods and heuristics in mathematical programming

Software:

CSPLib
PDFBibTeX XMLCite
Full Text: Link