ÇOKLU DEPOLAMA SİSTEMİNE SAHİP BİR LOJİSTİK FİRMASINDA TIR YÜKLEME OPERASYONLARININ PLANLAMASI: ESNEK ATÖLYE ÇİZELGELEME TABANLI BİR MODEL VE SEZGİSEL ALGORİTMALAR.

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Bibliographic Details
Title:	ÇOKLU DEPOLAMA SİSTEMİNE SAHİP BİR LOJİSTİK FİRMASINDA TIR YÜKLEME OPERASYONLARININ PLANLAMASI: ESNEK ATÖLYE ÇİZELGELEME TABANLI BİR MODEL VE SEZGİSEL ALGORİTMALAR.
Alternate Title:	PLANNING TRUCK LOADING OPERATIONS IN A LOGISTICS COMPANY WITH MULTI-WAREHOUSE SYSTEMS: A MODEL BASED ON FLEXIBLE JOB SHOP SCHEDULING AND HEURISTIC ALGORITHMS.
Authors:	GÜNER, Kemal¹, KARAGÖZ, Enes¹, EROĞLU, Ayşe¹, AKYOL ÖZER, Emine¹ emineakyol@eskisehir.edu.tr
Source:	Journal of Industrial Engineering (Turkish Chamber of Mechanical Engineers). 2025, Vol. 36 Issue 3, p493-514. 22p.
Subjects:	Truck loading & unloading, Heuristic algorithms, Logistics managers, Warehouse management systems, Production scheduling, Decision support systems, Scheduling
Abstract (English):	Inefficiencies in today's logistics processes negatively impact operational efficiency due to delays in storage, preparation, and transportation stages. This study addresses the problem of scheduling truck loading operations at a logistics company with three different storage systems: traditional, AS/RS, and hybrid. The objective is to minimize truck waiting times by assigning forklifts to pallets based on their precedence relationships. The problem is modeled as a flexible job shop scheduling problem and solved using the GAMS software with small, medium, and large datasets. However, as the dataset size increased, the solution time also increased, and the model failed to produce results for the large dataset. Therefore, the Non-Delay algorithm and the Earliest Completion Time based algorithm were employed. Although both algorithms produced good solutions within short computation times, GSA demonstrated a clear superiority. GSA reached the best solution in approximately 85% of the instances, whereas the success rate of EETZA remained around 63% on the same data sets. Finally, a decision support system based on the Non-Delay algorithm was developed to help decision makers manage the operational process more effectively and quickly. [ABSTRACT FROM AUTHOR]
Abstract (Turkish):	Lojistik süreçlerde yaşanan depolama, hazırlık ve taşıma kaynaklı gecikmeler, operasyonel verimliliği olumsuz yönde etkilemektedir. Bu çalışmada, geleneksel, otomatik depolama ve geri alma sistemi (Automated storage and retrieval system, AS/RS) ve karma olmak üzere üç farklı depolama sistemine sahip bir lojistik firmasındaki tır yükleme operasyonlarının planlanması problemi ele alınmıştır. Problemin amacı, yüklenecek paletlerin öncelik ilişkilerini dikkate alarak tır bekleme sürelerini enküçükleyen forklift atamalarını belirlemektir. Problem, esnek atölye tipi çizelgeleme problemi olarak modellenmiş ve oluşturulan matematiksel model GAMS programında küçük, orta ve büyük ölçekli veri setleri ile çözülmüştür. Ancak modelde, veri seti büyüdükçe çözüm süresi artmış ve büyük ölçekli veri setlerinde çözüm elde edilememiştir. Bu nedenle, Gecikmesiz Algoritma (Non-Delay Algorithm, GSA) ve En Erken Tamamlanma Zamanı tabanlı bir algoritma (Earliest Completion Time Based Algorithm, EETZA) kullanılmıştır. Her iki algoritma ile de kısa sürede iyi çözümler elde edilmiş olsa da, GSA'nın belirgin bir üstünlüğe sahip olduğu görülmektedir. GSA, bu problemlerin yaklaşık %85'inde en iyi çözüme ulaşırken, EETZA'nın başarısı aynı veri setlerinde yaklaşık %63 seviyesinde kalmıştır. Son olarak, GSA tabanlı bir karar destek sistemi geliştirilerek karar vericilerin operasyonel süreci daha etkin ve hızlı yönetmesi sağlanmıştır. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Inefficiencies in today's logistics processes negatively impact operational efficiency due to delays in storage, preparation, and transportation stages. This study addresses the problem of scheduling truck loading operations at a logistics company with three different storage systems: traditional, AS/RS, and hybrid. The objective is to minimize truck waiting times by assigning forklifts to pallets based on their precedence relationships. The problem is modeled as a flexible job shop scheduling problem and solved using the GAMS software with small, medium, and large datasets. However, as the dataset size increased, the solution time also increased, and the model failed to produce results for the large dataset. Therefore, the Non-Delay algorithm and the Earliest Completion Time based algorithm were employed. Although both algorithms produced good solutions within short computation times, GSA demonstrated a clear superiority. GSA reached the best solution in approximately 85% of the instances, whereas the success rate of EETZA remained around 63% on the same data sets. Finally, a decision support system based on the Non-Delay algorithm was developed to help decision makers manage the operational process more effectively and quickly. [ABSTRACT FROM AUTHOR]
ISSN:	13003410
DOI:	10.46465/endustrimuhendisligi.1755558