Dynamic dispatching priority setting in customer-oriented manufacturing environments


GÜÇDEMİR H., SELİM H.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, cilt.92, ss.1861-1874, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 92
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1007/s00170-017-0258-5
  • Dergi Adı: INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1861-1874
  • Anahtar Kelimeler: Job shop scheduling, Customer satisfaction, Dynamic priority setting, Simulation optimization, JOB SHOPS, RULES, SIMULATION, ALGORITHM, OPTIMIZATION, PERFORMANCE, DESIGN
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

In today's competitive environment, customer-oriented view is essential in gaining sustainable competitive advantage. This study aims to reflect the customer-oriented view to production planning and control decisions. To this aim, a simulation optimization-based approach is developed for job shop systems with dynamic order arrivals. Product-type-based lot splitting is applied in order to improve the flow time, and machine-based dispatching rules are utilized for sublot scheduling to realize dynamic scheduling. Multiple customer segments with different importance weights and their expectations and penalties on order completion rate on due date, tardiness, and earliness are considered. A customer satisfaction-based objective function is defined. Customer-oriented dispatching rules are proposed in this study to ensure the prioritization of orders from the key customers in order fulfilling. In order to prevent customer losses by providing a balanced structure between the customer segments in terms of the satisfaction levels, weight setting functions that dynamically compute the weights in the proposed dispatching rules are proposed. It is aimed to determine the near-optimal values of the segment-based parameters of the related weight setting functions. To this aim, a differential evolution algorithm-based simulation optimization approach is proposed. To confirm its viability, the proposed approach is applied to a realistic job shop system.