Minimizing the earliness/tardiness costs on parallel machine with learning effects and deteriorating jobs: a mixed nonlinear integer programming approach


TOKSARI M. D., Guener E.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, vol.38, pp.801-808, 2008 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 38
  • Publication Date: 2008
  • Doi Number: 10.1007/s00170-007-1128-3
  • Journal Name: INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.801-808
  • Keywords: scheduling, earliness, tardiness, learning effect, deterioration jobs, parallel machine, sequence-dependent setup time, common due date, EARLINESS-TARDINESS PENALTIES, DEPENDENT PROCESSING TIMES, TOTAL COMPLETION-TIME, DUE-DATE ASSIGNMENT, SINGLE-MACHINE, SCHEDULING JOBS, FLOW-SHOP, SHAPED POLICIES, ALGORITHMS, MAKESPAN
  • Erciyes University Affiliated: No

Abstract

In this study, we introduce a mixed nonlinear integer programming formulation for parallel machine earliness/tardiness (ET) scheduling with simultaneous effects of learning and linear deterioration, sequence-dependent setups, and a common due-date for all jobs. By the effects of learning and linear deterioration, we propose that the processing time of a job is defined by increasing function of its execution start time and position in the sequence. The developed model allows sequence-dependent setups and sequence-dependent early/tardy penalties. The model can easily provide the optimal solution to problems involving about eleven jobs and two machines.