Annals of Operations Research

, Volume 159, Issue 1, pp 275–292

Fuzzy job shop scheduling with lot-sizing


    • School of Computer ScienceUniversity of Nottingham
  • Carole Fayad
    • School of Computer ScienceUniversity of Nottingham
  • Dobrila Petrovic
    • Faculty of Engineering and ComputingCoventry University
  • Edmund Burke
    • School of Computer ScienceUniversity of Nottingham
  • Graham Kendall
    • School of Computer ScienceUniversity of Nottingham

DOI: 10.1007/s10479-007-0287-9

Cite this article as:
Petrovic, S., Fayad, C., Petrovic, D. et al. Ann Oper Res (2008) 159: 275. doi:10.1007/s10479-007-0287-9


This paper deals with a problem of determining lot-sizes of jobs in a real-world job shop-scheduling in the presence of uncertainty. The main issue discussed in this paper is lot-sizing of jobs. A fuzzy rule-based system is developed which determines the size of lots using the following premise variables: size of the job, the static slack of the job, workload on the shop floor, and the priority of the job. Both premise and conclusion variables are modelled as linguistic variables represented by using fuzzy sets (apart from the priority of the job which is a crisp value). The determined lots’ sizes are input to a fuzzy multi-objective genetic algorithm for job shop scheduling. Imprecise jobs’ processing times and due dates are modelled by using fuzzy sets. The objectives that are used to measure the quality of the generated schedules are average weighted tardiness of jobs, the number of tardy jobs, the total setup time, the total idle time of machines and the total flow time of jobs. The developed algorithm is analysed on real-world data obtained from a printing company.


Job shop schedulingFuzzy rule-based systemLot-sizingBatchingFuzzy multi-objective genetic algorithmReal-world applicationDispatching rules

Copyright information

© Springer Science+Business Media, LLC 2007