توسعه يك مدل زمان‌بندي براي عمليات جرثقيل سقفي در يك محيط جريان كارگاهي

Efficient an‎d effective management of resources an‎d equipment plays a significant role in enhancing productivity an‎d maintaining the competitiveness of manufacturing an‎d industrial systems. One of these key pieces of equipment is the overhead crane, which is responsible for transporting materials, heavy parts, an‎d products within manufacturing an‎d industrial environments. Overhead cranes are designed with various features, each tailored to the specific task the crane is intended to perform. However, the operational limitations of these cranes often necessitate coordination with other equipment within the production unit. One of the main challenges is the occurrence of interference in movement paths. Due to their limited number an‎d inability to perform multiple tasks simultaneously, overhead cranes lead to idle times in production an‎d increased operational costs, often becoming a bottleneck in manufacturing processes. In this study, the objective is to reduce production time in a manufacturing unit by optimizing the scheduling of overhead crane activities in a hybrid job shop environment. Coordinated scheduling of overhead crane movements in a workshop environment is recognized as a complex optimization problem, primarily due to the inclusion of multiple operational constraints such as collision avoidance between cranes, adherence to task precedence, an‎d the presence of shared workspaces. Conventional an‎d classical optimization approaches, which rely solely on mathematical models, often fail to address the real-world dimensions of this problem. The reason for this failure lies in the NP-hard nature of the problem, which makes obtaining an exact optimal solution for realistic problem sizes computationally infeasible. This study proposes an approach that first introduces a mathematical model in the form of a Mixed Integer Programming (MILP) formulation, which serves as the foundation of the research by precisely defining all constraints an‎d objectives of the problem. Subsequently, a metaheuristic algorithm—specifically, a Genetic Algorithm—is employed to generate solutions that aim to minimize the overall completion time of all tasks. Finally, the results obtained from the mathematical model an‎d the metaheuristic approach are compared, an‎d their performance is eva‎luated. Using the mathematical model, in comparison to the genetic algorithm for small-scale problems, led to a 12.27% improvement in the objective function value.

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