Greedy randomized adaptive search for dynamic flexible job-shop scheduling


BAYKASOĞLU A., MADENOĞLU F. S. , Hamzadayı A.

JOURNAL OF MANUFACTURING SYSTEMS, vol.56, pp.425-451, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 56
  • Publication Date: 2020
  • Doi Number: 10.1016/j.jmsy.2020.06.005
  • Title of Journal : JOURNAL OF MANUFACTURING SYSTEMS
  • Page Numbers: pp.425-451
  • Keywords: Flexible job shop scheduling, Rescheduling, Dynamic scheduling, GRASP, MANUFACTURING SYSTEM, GENETIC ALGORITHM, GRASP ALGORITHM, OPTIMIZATION, EVOLUTIONARY, SIMULATION, MODEL

Abstract

Dynamic flexible job shop scheduling problem is studied under the events such as new order arrivals, changes in due dates, machine breakdowns, order cancellations, and appearance of urgent orders. This paper presents a constructive algorithm which can solve FJSP and DFJSP with machine capacity constraints and sequence-dependent setup times, and employs greedy randomized adaptive search procedure (GRASP). Besides, Order Review Release (ORR) mechanism and order acceptance/rejection decisions are also incorporated into the proposed method in order to adjust capacity execution considering customer due date requirements. The lexicographic method is utilized to assess the objectives: schedule instability, makespan, mean tardiness and mean flow time. A group of experiments is also carried out in order to verify the suitability of the GRASP in solving the flexible job shop scheduling problem. Benchmark problems are formed for different problem scales with dynamic events. The event-driven rescheduling strategy is also compared with periodical rescheduling strategy. Results of the extensive computational experiment presents that proposed approach is very effective and can provide reasonable schedules under event-driven and periodic scheduling scenarios.