Optimal due date assignment and resource allocation in a group technology scheduling environment

Dvir Shabtay, Yisrael Itskovich, Liron Yedidsion, Daniel Oron

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


We study a single machine scheduling problem in which the scheduler determines due dates for different jobs in a group technology environment. In group technology (GT) environment, a partition of the jobs into groups (families) is given and jobs of the same family are required to be processed consecutively. The partition of the jobs into families is done in order to achieve efficiency of high-volume production by exploiting similarities of different products and activities in their production. Since customers of similar jobs may expect that all jobs within the same group will be assigned with the same due date, we suggest an original due date assignment method in which all jobs within a family are restricted to be assigned the same due date, while each family can be assigned a due date without any restriction. The proposed method provides an extension of two earlier methods that appear in the literature, one which includes a single family and the other in which the number of families is identical to the number of jobs. Our objective is to find the job schedule and the due date for each group that minimizes an objective function which includes earliness, tardiness and due date assignment penalties. We also extend the analysis to address the case in which the job processing times are resource dependent. For this case we include the total weighted resource consumption and the makespan penalties to the objective function.

Original languageEnglish
Pages (from-to)2218-2228
Number of pages11
JournalComputers and Operations Research
Issue number12
StatePublished - 1 Dec 2010


  • Earlinesstardiness
  • Family due date assignment method
  • Group technology
  • Resource allocation
  • Single-machine scheduling

ASJC Scopus subject areas

  • Computer Science (all)
  • Modeling and Simulation
  • Management Science and Operations Research


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