@article{article_1079515, title={Optimization of welding job-shop scheduling problem under variable workstation constraint: an industrial application with Arena simulation based genetic algorithm}, journal={Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi}, volume={28}, pages={139–147}, year={2022}, url={https://izlik.org/JA86TH82EC}, author={Karaoglan, Aslan Deniz}, keywords={Arena simülasyonu, Genetik algoritma, Emekyoğun proje tipi üretim, Tamamlanma zamanı minimizasyonu, Değişken iş istasyonu kısıtı, Kaynak atölyesi çizelgeleme problemi}, abstract={Job-shop scheduling is a difficult issue for ’labor-intensive project type manufacturing’. Because in this type of production, the actual processing times are not exactly known until the production is finished and these processing times vary depending on the order’s technical specifications. It is an appropriate method to use probability distributions to forecast the processing times. This paper provides an industrial application for the scheduling of a labor-intensive project type working welding job-shop under variable workstation constraints. This constraint is consequence of a special production type that is depending on the length of the products. The aim is minimizing the makespan of a group of waiting orders. Genetic algorithm (GA) is used for this purpose to establish the entry sequence of the job-shop’s waiting orders and dispatching them to the 6 identical welding stations. The dynamic conditions of the job-shop are simulated by the Arena simulation program. Stochastic processing times are used as the input data of the algorithm. Using stochastic processing times under variable workstation constraint for welding job-shop scheduling is not investigated previously. According to the experimental results, GA and Arena simulation together effectively reduces the makespan in this type of problem under variable workstation constraint. The GA aided Arena schedule outperforms the schedules proposed without using GA for this problem. Simulation results indicate that the total manufacturing time of pending orders is nearly 9.25% reduced when compared with the schedules proposed without using GA.}, number={1}