Solving employee timetabling problems by generalized local search

Andrea Schaerf, Amnon Meisels

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations


Employee timetabling is the operation of assigning employees to tasks in a set of shifts during a fixed period of time, typically a week. We present a general definition of employee timetabling problems (ETPs) that captures many real world problem formulations and includes complex constraints. We investigate the use of several local search techniques for solving ETPs. In particular, we propose a generalization of local search that makes use of a novel search space that includes also partial assignments. We describe the distinguishing features of this generalized local search that allows it to navigate the search space effectively. We show that, on large and difficult instances of real world ETPs, where systematic search fails, local search methods perform well and solve the hardest instances. According to our experimental results on various local search techniques, generalized local search is the best method for solving large ETP instances.

Original languageEnglish GB
Title of host publicationAI*IA 99
Subtitle of host publicationAdvances in Artificial Intelligence - 6th Congress of Italian Association for Artificial Intelligence, Selected Papers
EditorsEvelina Lamma, Paola Mello
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540673504, 9783540462385, 9783540673507
StatePublished - 1 Jan 2000
Event6th Congress of Italian Association for Artificial Intelligence, AIIA 1999 - Bologna, Italy
Duration: 14 Sep 199917 Sep 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1792 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference6th Congress of Italian Association for Artificial Intelligence, AIIA 1999

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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