Digital technologies are transforming the industrial landscape and disrupting traditional business models. New business opportunities related to Industry 4.0 are emerging, so companies must adapt to the new environment. This work puts forward a multi-objective optimization algorithm to improve productivity and reduce the costs and energy consumption of autonomous industrial processes with the aim of achieving sustainable growth. The processes analyzed encompass an assembly line production with robotic cells and the subsequent material handling systems (MHS) using autonomous guided vehicles (AGVs) for indoor transport. An efficient algorithm has been implemented to integrate and minimize industrial robot arm working times, AGVs travel times and their trajectory, and the energy consumed in industrial processes while maximizing global business profits when manufacturing different products in an indoor industrial environment. Furthermore, this is carried out by considering the kinematics and dynamics of autonomous industrial processes and sustainable strategies to ensure compliance with government policies on environmental issues. These objectives are in line with the European Union (EU) guidelines on reducing greenhouse gas (GHG) emissions, renewable energy share, and improvements in energy efficiency for climate change mitigation and adaptation policies. Based on the difference in energy consumption between optimized and unoptimized industrial processes, the economic benefits can be quantified in terms of GHG emission quotas, volume of fuel consumed, and the indirect benefits with respect to improving corporate brand image. The methodology presented here has been successfully applied to several real case studies covering different manufacturing processes, robotic operations, and products. The results show that higher profits and sustainable growth are achieved when this methodology is used. It helps design Flexible Manufacturing Systems (FMS) and leads to shorter working times and higher energy efficiency and annual profits. In addition, Pareto frontiers show the trade-off between profits and product manufacturing times for different case studies.

数字技术正在改变工业格局并颠覆传统商业模式。与工业4.0相关的新商机不断涌现，企业必须适应新环境。这项工作提出了一种多目标优化算法，以提高生产力，降低自主工业过程的成本和能源消耗，以实现可持续增长。分析的过程包括使用机器人单元的装配线生产以及使用自动导引车 (AGV) 进行室内运输的后续材料处理系统 (MHS)。已经实施了一种有效的算法来集成和最小化工业机器人手臂的工作时间、AGV 的行驶时间及其轨迹，以及在室内工业环境中制造不同产品时工业过程中消耗的能源，同时最大限度地提高全球商业利润。此外，这是通过考虑自主工业过程和可持续战略的运动学和动力学来实现的，以确保符合政府关于环境问题的政策。这些目标符合欧盟 (EU) 关于减少温室气体 (GHG) 排放、可再生能源份额以及提高能源效率以缓解和适应气候变化政策的指导方针。根据优化和未优化工业过程的能耗差异，可以将经济效益量化为温室气体排放配额、燃料消耗量、以及提升企业品牌形象的间接效益。此处介绍的方法已成功应用于多个真实案例研究，涵盖不同的制造过程、机器人操作和产品。结果表明，使用这种方法可以获得更高的利润和可持续的增长。它有助于设计柔性制造系统 (FMS)，并缩短工作时间，提高能源效率和年利润。此外，帕累托边界显示了不同案例研究的利润和产品制造时间之间的权衡。结果表明，使用这种方法可以获得更高的利润和可持续的增长。它有助于设计柔性制造系统 (FMS)，并缩短工作时间，提高能源效率和年利润。此外，帕累托边界显示了不同案例研究的利润和产品制造时间之间的权衡。结果表明，使用这种方法可以获得更高的利润和可持续的增长。它有助于设计柔性制造系统 (FMS)，并缩短工作时间，提高能源效率和年利润。此外，帕累托边界显示了不同案例研究的利润和产品制造时间之间的权衡。