Scheduling batches in multi hybrid cell manufacturing system considering worker resources: A case study from pipeline industry

Yılmaz Ö. F., Çevikcan E., Durmuşoğlu M. B.

ADVANCES IN PRODUCTION ENGINEERING & MANAGEMENT, cilt.11, sa.3, ss.192-206, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 11 Sayı: 3
  • Basım Tarihi: 2016
  • Doi Numarası: 10.14743/apem2016.3.220
  • Dergi Adı: ADVANCES IN PRODUCTION ENGINEERING & MANAGEMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.192-206
  • Anahtar Kelimeler: Batch scheduling, Hybrid manufacturing cells, Hybrid cells batch scheduling, Goal programming, Heuristic, HCBS heuristic, ALGORITHM, OPTIMIZATION, FLEXIBILITY, ASSIGNMENT, ALLOCATION, HEURISTICS, IMPACT, PARTS
  • Karadeniz Teknik Üniversitesi Adresli: Hayır

Özet

This study considers batch scheduling problem in the multi hybrid cell manufacturing system (MHCMS) taking into account worker resources. This problem consists of determining sequence of batches, finding the starting time of each batch, and assigning workers to the batches in accordance with some pre-determined objectives. Due to a lack of studies on the batch scheduling problem in the MHCMS, a binary integer linear goal programming mathematical model is developed for bi-objective batch scheduling problem in this study. The formulated model is difficult to solve for large sized problem instances. To solve the model, we develop an efficient heuristic method called the Hybrid Cells Batch Scheduling (HCBS) heuristic. The proposed HCBS heuristic permits integrating batch scheduling and employee (worker) timetabling. Furthermore, we construct upper and lower bounds for the average flow time and the total number of workers. For evaluation of the performance of the heuristic, computational experiments are performed on generated test instances based on real production data. Results of the experiments show that the suggested heuristic method is capable of solving large sized problem instances in a reasonable amount of CPU time. (C) 2016 PEI, University of Maribor. All rights reserved.