This paper proposes a production control system, DRC-HPP, which uses the when/where rules in dual resource constrained (DRC) systems for a hybrid push-pull (HPP) control, to overcome some difficulties in modelling/implementing DRC/Kanban systems. These rules and the novel ‘process-or-transport’ and ‘whereto’ rules are embedded in some policies workers use to decide when to process (transport) parts, and where (whereto). Unlike most control systems, in which a group of workers is always responsible for transporting and another group is always responsible for processing parts, workers in DRC-HPP are responsible for both transporting and processing parts, as in the Toyota Sewn Products Management System (TSS). Yet, unlike TSS, DRC-HPP can be applied in any layout type. Workers transport parts when they are idle in part processing to enhance their utilisations and synchronise transportation. Since the transportation does not require special worker skills, the cost of training workers is not incurred. DRC-HPP is compared with different benchmarks through simulation experiments to evaluate its performance. It performs well under relatively short transportation times with respect to processing times. If they are relatively longer, the issue becomes to determine the number of workers to achieve a performance level. DRC-HPP also facilitates bottleneck management.

Manufacturing systems are uncertain and dynamic systems, hence, they require real-time scheduling to adapt to changing manufacturing conditions. Current real-time scheduling approaches have been devised mainly for machine-only constrained systems, in which the shop capacity is constrained only by machine capacity, rather than for dual resource constrained (DRC) systems, in which the shop capacity is constrained by machine and worker capacity. In particular, there is no study on DRC system scheduling in which the ‘where’ and ‘when’ worker assignment rules, basic features of DRC systems, are altered in real-time (dynamically selected) to respond to new manufacturing conditions. Besides, multi-criteria DRC system scheduling has not yet been addressed extensively. Also, there has been little research on the interactions of dynamically selected job dispatching, worker assignment and job routing rules, which have a significant impact on DRC system performance. This paper proposes a multi-criteria real-time scheduling methodology for DRC systems to address these issues, and investigates these interactions. The methodology employs artificial neural networks as meta-models to reduce computational complexity and a fuzzy inference system to cope with multiple performance criteria. Various simulation experiments demonstrate that the methodology provides satisfactory results for real-time DRC systems scheduling.

Taşıma sistemlerinde yolcu talebinin bilinmesi; rotaların belirlenmesi, sefer saatlerinin ve sıklığının planlanması, çalışan sayısının optimize edilmesi gibi birçok operasyon faaliyeti için hayati bir görevdir. Bununla birlikte talebin düzensizliği, trend ve mevsimsellik bileşenlerini barındırması, tahmin edilebilmesini zor bir hale sokmaktadır. Son zamanlarda geleneksel talep tahmin yöntemlerinin yanı sıra modern yapay zekâ teknikleri de kullanılmaya başlanmıştır. Bu çalışmada, literatürde sıklıkla tercih edilen Box-Jenkins metodolojisinden SARIMA yöntemi ile 2017’de Facebook tarafından geliştirilen Prophet yönteminin toplu taşıma araçlarındaki yolcu sayısı tahminindeki performanslarının karşılaştırılması ve yöntemlerin değerlendirilmesi amaçlanmıştır. İki yöntem kullanılarak başarılı birer tahmin modeli geliştirilmiştir. Modelleme sonucunda SARIMA ve Prophet yöntemlerinin MAPE değerleri sırasıyla 0,11 ve 0,13 olarak hesaplanmıştır.