Single machine scheduling problem with stochastic sequence-dependent setup times

ERTEM M., ÖZÇELİK F., SARAÇ T.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, cilt.57, sa.10, ss.3273-3289, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 57 Sayı: 10
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1080/00207543.2019.1581383
  • Dergi Adı: INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.3273-3289
  • Anahtar Kelimeler: single machine scheduling problem, stochastic sequence-dependent setup times, Genetic Algorithm, stochastic programming, value of the stochastic solution, TARDY JOBS, GENETIC ALGORITHM, PROCESSING TIMES, TOTAL TARDINESS, EXPECTED NUMBER, BOUNDED SETUP, SUPPLY CHAIN, FLOWSHOP, MINIMIZE, OPTIMIZATION
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

In this study, we consider stochastic single machine scheduling problem. We assume that setup times are both sequence dependent and uncertain while processing times and due dates are deterministic. In the literature, most of the studies consider the uncertainty on processing times or due dates. However, in the real-world applications (i.e. plastic moulding industry, appliance assembly, etc.), it is common to see varying setup times due to labour or setup tools availability. In order to cover this fact in machine scheduling, we set our objective as to minimise the total expected tardiness under uncertain sequence-dependent setup times. For the solution of this NP-hard problem, several heuristics and some dynamic programming algorithms have been developed. However, none of these approaches provide an exact solution for the problem. In this study, a two-stage stochastic-programming method is utilised for the optimal solution of the problem. In addition, a Genetic Algorithm approach is proposed to solve the large-size problems approximately. Finally, the results of the stochastic approach are compared with the deterministic one to demonstrate the value of the stochastic solution.