Treffer: Improving Welding Job Scheduling in Shipbuilding with Optimization Algorithms.

This research addresses an optimization problem by scheduling welding jobs for the preassembly elements in robotic manufacturing cells within the shipbuilding industry. The key operational pillars include the required delivery time for the preassembly elements and their welding quality. To address these, constrained programming (CP) and several metaheuristic algorithms were proposed. To evaluate their performance in the case study, the IBM ILOG CPLEX module has been utilized as a Constrained Programming Optimizer (CPO) for modeling and solving the proposed job scheduling problem, ensuring constraint satisfaction and efficient solution exploration. Additionally, these algorithms have been integrated into the discrete event simulation (DES) model developed in Siemens Tecnomatix Plant Simulation using Python. The results obtained by metaheuristics were compared by varying the operational parameters of these algorithms with the GA Wizard algorithm integrated into the DES itself, observing that these external algorithms significantly outperform the GA Wizard. Conversely, the results show that for the proposed problem, CPO provides a significantly superior solution, achieving minimal delays. However, this technique has certain limitations that highlight the value of implementing metaheuristic algorithms for process optimization in the shipbuilding industry, particularly for more complex problems. Thus, a robust optimization methodology is proposed for job shop scheduling in ship manufacturing by integrating academic aspects, such as computational optimization methods, with the DES software commonly used in industry. [ABSTRACT FROM AUTHOR]

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