Framework for the design and evaluation of a reconfigurable production system based on movable robot integration

Abstract

Enterprises now face a global, dynamic, and unpredictable economic environment. In response to quick changes of the demand, the production needs to have the ability to adapt rapidly to meet the production requirements. The Reconfigurable Manufacturing System (RMS) paradigm enables such capabilities. However, defining design and reconfiguration rules is highly challenging, as it requires a broad knowledge encompassing the inclusion of technological, production, and economic metrics, as well as an understanding of a reconfiguration strategy, determining the necessary reconfiguration frequency of the system. For now, no global methodology taking all those aspects into account has been proposed. This article presents an original framework for the design, evaluation, and reconfiguration of the Reconfigurable Production System (RPS). New metrics to measure reconfigurability are defined. The design approach consists in three main steps which are individually developed. The selection of the appropriated production system is based on the comparison of reconfigurability and productivity indicators. Finally, the reconfiguration strategy is presented. The methodology is applied on a case study from the automotive industry.

Remote integration of advanced manufacturing technologies into production systems: integration processes, key challenges and mitigation actions

“…Integrating new AMT into the production system often stems from developing new products. Accordingly, the main reason for changing a production system is new product development, thereby triggering AMT investment and integration (Bellgran and Säfsten, 2010; Eynaud et al ., 2021). From a production perspective, typical requirements are tolerances, technical specifications and production volume.…”

“…The ability to constantly adjust and adapt the production system is a major determinant for competitive and sustainable customer-order-driven production (Narain et al ., 2000; Bellgran and Säfsten, 2010; Wang and Zhang, 2022). In this regard, literature has explored the development of flexible and reconfigurable production systems (ElMaraghy, 2005; Mehrabi et al ., 2002; Rösiö and Bruch, 2018; Eynaud et al ., 2021).…”

Digital Twin Framework for Reconfiguration Management: Concept & Evaluation

“…APPENDIX A RESULTS OF THE SYSTEMATIC LITERATURE REVIEW [58] 0 0 1 1 0 0 0 0 0 0 0 0 0 [59] 1 0 0 0 1 0 0 0 0 0 0 0 1 [60] 1 1 1 0 1 0 0 0 0 0 0 0 1 [61] 0 1 1 0 1 0 0 0 1 0 0 0 1 [62] 1 1 1 0 1 0 0 0 0 0 0 0 1 [63] 1 0 1 1 1 0 0 0 0 1 0 0 1 [64] 1 1 1 0 1 0 0 0 1 0 1 0 0 [65] 1 1 1 1 1 0 0 0 0 0 0 0 1 [66] 0 0 0 1 1 0 1 0 0 0 1 0 1 [67] 1 1 1 0 0 1 0 0 0 0 0 0 0 [68] 1 0 1 0 1 0 0 0 0 0 1 0 1 [69] 1 0 0 0 1 0 1 1 0 0 1 0 1 [70] 1 0 1 1 1 0 1 0 0 0 1 0 1 [71] 1 1 1 1 1 0 0 0 0 0 1 1 1 [72] 0 0 0 0 0 0 0 0 0 0 0 0 0 [73] 0 0 1 0 1 1 0 0 1 0 1 0 0 [74] 1 1 1 1 1 1 0 0 1 1 1 0 1 [75] 1 0 0 1 1 1 1 0 0 0 0 1 0 [39] 1 1 0 1 1 0 1 0 0 1 1 0 1 [76] 1 0 1 1 1 0 0 0 0 1 0 0 0 [77] 1 0 1 1 1 0 1 1 0 0 1 1 0 [78] 1 1 0 1 1 0 0 0 0 0 1 1 0 [79] 1 0 1 0 1 0 0 0 1 1 1 0 1 [80] 1 0 1 0 1 0 1 0 0 0 0 1 0 [81] 1 0 1 0 1 0 0 0 1 1 0 0 1 [82] 0 0 1 0 1 1 1 1 0 0 0 1 0 [83] 0 0 0 0 1 0 0 0 0 0 0 1 0 [84] 1 0 1 0 1 0 0 0 0 0 0 1 1 [85] 1 0 1 0 1 1 0 0 0 1 0 0 1 [86] 1 1 1 1 1 1 1 1 0 1 0 0 1 [87] 1 1 1 0 1 0 0 0 0 1 1 0 0 [88] 1 0 1 1 1 0 0 0 0 0 1 1 1 [89] 0 0 0 0 1 0 0 0 0 0 1 0 0 [90] 1 0 1 0 1 0 1 1 1 1 1 0 0 [91] 1 0 0 1 0 0 1 1 1 1 1 0 0 [92] 0 0 1 0 1 0 1 1 1 1 1 0 1 [93] 1 0 1 0 1 0 0 0 0 1 0 0 1 [94] 1 1 1 1 1 0 0 0 0 0 0 1 1 [95] 1 0 1 0 1 0 0 0 0 0 0 1 1 …”

Collective Intelligence Application in a Kitting Picking Zone of the Automotive Industry

“…On the other hand, the second one is about systems composed by agents, where each one has different characteristics from the others. Using multiagent systems in this context help to deal with problems like online scheduling in smart factories [14] and the organization of reconfigurable production systems [15], [16]. Likewise, we can find the use of multiagent systems to detect anomalies in several process in the industry, as it is presented in [17].…”

“…Consequently, we propose in this article an architecture to apply multiagent learning and collective intelligence techniques to warrant a good performance of the whole logistic process of supplying materials to the assembly process in a Renault plant. The topics covered by the papers [15], [16] and [17] are a source of inspiration for the proposal presented in the following section.…”