Neighborhoods revisited: an experimental investigation into the effectiveness of variable neighborhood descent for scheduling

Variable neighborhood search (VNS) systematically exploits the idea to alternate between neighbor-hoods within a local search [4]. Several VNS variants have been described so far and many of themhave been applied with success to a variety of combinatorial optimization problems; see [3, 4] for anoverview. Yet, even though scheduling is a central problem in production environments, we are notaware of any VNS applications to scheduling problems. In this paper we focus on the application of aparticular VNS technique called variable neighborhood descent (VND) to scheduling problems in whicha solution can be represented as a single permutation of all jobs. This class of scheduling problemsincludes single machine problems as well as a variety of multiple machine problems. In this abstract, wepresent some results of VND when applied to single machine problems minimizing the total tardinessproblem, the total weighted tardiness, and the sum of weighted completion times, as well as to thepermutation flow shop problem. In addition we investigate whether VND local search performs betterthan a single neighborhood local search algorithm when it is embedded in a metaheuristic like iteratedlocal search (ILS) [6].