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Conference Papers Year : 2023

Robust Configuration Design of Sustainable Reconfigurable Manufacturing System Under Uncertainty


A reconfigurable manufacturing system (RMS) is a dynamic manufacturing system with flexible production capacity and Ability to meet changing needs. RMSs are viewed as the Systems of the future are becoming more advanced due to technological advancements. Their potential to produce highly personalized and complicated items in any quantity and at any cost while taking advantage of mass manufacturing [1]. This paradigm provides excellent performance and reactivity to changes, particularly if system response to uncertainties or unknown cases and productivity are regarded as critical [2]. In order to ensure a reliable system design, it is crucial to integrate primary RMS features such as customization, convertibility, modularity, diagnosability, scalability, and integrability. [3]. CNC machines and reconfigurable machine tools (RMT) are the core components of RMS for machining. RMT-based RMS is more cost-effective and adaptable than CNC-based RMS [4]. RMS often employs RMTs and modular reconfigurable machines (MRMs) with adjustable and modular architectures [5]. By adding or removing modules from an RMT, the production rate of that machine to perform a particular operation may vary. The new machine configuration may be capable of performing another operation in the system (convertibility) or may have more capacity to complete the same operation (scalability) [6]. An RMT can be used as a group of devices by modifying its configuration to meet various manufacturing needs. Typically, an RMT comprises modules that can be assembled and disassembled during the operation phase to acquire various configurations for satisfying manufacturing requirements [7]. The development of RMTs can prevent the implementation of multiple machines that share numerous costly and standard modules but are rarely used concurrently. A sustainable future is the most critical concern for humans in the modern world, so this context has led to the development of the sustainable reconfigurable manufacturing system (SRMS) [8]. SRMS relies on manufacturing with less greenhouse gas emissions, less use of nonrenewable or toxic materials, less waste and energy consumption, and respect for human social life. [9]. Due to its effect on operating costs and environmental sustainability, energy consumption in RMS is a crucial factor. Energy consumption is principally caused by the need for energy to operate machinery and equipment and by the labor and energy-intensive process of reconfiguration. To reduce energy waste, it is crucial to choose energy-efficient equipment, optimize reconfiguration processes, and deploy energy-aware control systems. Manufacturers may successfully minimize energy consumption and improve the overall energy efficiency of RMS by concentrating on energy optimization measures, such as equipment efficiency and process optimization. In this paper, a new mixed integer linear programming is introduced to handle configuration changes and capacity scalability for family products in RMS. Two objective functions are minimizing the total cost and energy consumption of the system by taking advantage of the different production capabilities of RMTs. The costs include the exploitation cost of RMTs, reconfiguring cost, maintaining inventory, and purchasing raw materials. The reconfiguration cost can be in module changes and/or exploitations of new tools. For dealing with fluctuating demand and market changes, the two most important parameters have been considered uncertain, and a scenario-based robust optimization is implemented. Therefore, the primary contributions of this work are the development of a novel multi-objective optimization for choosing the ideal configuration of RMTs in RMS and using a strong robust optimization for handling uncertainty. The remainder of the paper is arranged as follows. Section II describes the problem description. The third section introduces the solution approach, and Section IV provides an instructive example and numerical findings. Section V discusses the results and conclusion of the study.
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emse-04349781 , version 1 (18-12-2023)



Alireza Ostovari, Lyes Benyoucef, Hicham Haddou Benderbal, Xavier Delorme. Robust Configuration Design of Sustainable Reconfigurable Manufacturing System Under Uncertainty. IEEE 2023 International Conference on Networking, Sensing and Control (ICNSC), Oct 2023, Marseille, France. pp.1-7, ⟨10.1109/ICNSC58704.2023.10319022⟩. ⟨emse-04349781⟩
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