| Summary: | Abstract Robotic assembly lines serve as a foundational element of modern manufacturing, facilitating the efficient production of high-quality goods. Reducing the energy consumption of robots in these assembly lines is essential to promoting greener manufacturing practices, lowering costs, and achieving global energy efficiency goals. This study seeks to create a model that optimizes robotic assembly line systems by minimizing cycle time and energy consumption, either independently or simultaneously. The research assumes an unlimited supply of various robot types, each with distinct variants, processing times, and energy demands for specific tasks. The problem is modeled using Integer Linear Programming (ILP) in the LINGO (21) solver. For multi-objective scenarios involving both cycle time and energy consumption, a weighted sum approach is applied to convert the problem into a single-objective format. To tackle large-scale problems more effectively, several concepts and rules are proposed to accelerate data processing. The results demonstrated improved performance compared to benchmark problems. The analysis indicated that reducing cycle time contributes to lower energy consumption, driven by an increase in the number of stations and robots. Additionally, the Pareto front analysis of cycle time and energy consumption revealed that energy usage remains nearly constant across a wide range of cycle times.
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