Reconfiguration schemes evaluation based on preference ranking of key characteristics of reconfigurable manufacturing systems

Abstract

To address the problem of how to build quantitative evaluation index models that reflect the essential characteristics of reconfigurable manufacturing system (RMS) and rank alternative reconfiguration schemes, which possess both advantages and disadvantages, an evaluation method based on the preference ranking organization method for enrichment evaluation (PROMETHEE) is proposed. Based on a consideration of the reconfiguration of the reconfigurable machine components and manufacturing cells, quantitative models of the key characteristics of an RMS (scalability, convertibility, diagnosability, modularity, integrability, and customization) are established, after which the quantitative models are used as the basis for constructing an RMS evaluation index system. The analytic hierarchy process (AHP) is used to assign the weights for these indices. During the evaluation process, PROMETHEE I is first applied to analyze the advantages and disadvantages of each alternative scheme. Then, PROMETHEE II is adopted to analyze the net advantages of the schemes. Finally, all of the alternative configurations are ranked according to the analysis results above. The workshop of an institute that has both research and production capabilities was used as an example to validate the effectiveness and practicability of the proposed method. The example contains 10 alternative reconfiguration schemes, and each scheme consists of six evaluation indices. The computation result shows that quantitative models of six key RMS characteristics are equipped with the ability of quantitative description of the RMS reconfiguration scheme, which gives intuitive decision-making information combined with PROMETHEE, including advantage and disadvantage between alternative schemes, for a decision-maker to select the satisfactory configuration. In addition, only a 7.2 % data loss during the evaluation data processing means the rationality of the selected evaluation index and evaluation algorithm.

A New Method for Manufacturing Process Autonomous Planning in Intelligent Manufacturing System

“…When the production line resources are abnormal, or the market feedback demand changes, or the user request to change the orders, etc., the intelligent manufacturing system can start manufacturing process autonomously planning through MES (Manufacturing Execution System), and A-CAPP will feed back the results to MES, and then realize the RMS (Reconfiguration Manufacturing System). Therefore, A-CAPP is the driving force to realize the dynamic adjustment of the production line via configuration, and RMS is the concrete implementer to realize RMS [11][12][13]. A-CAPP and RMS crucial parts of the intelligent manufacturing system cooperate with MES to complete the intelligent adjustment of production line resources.…”

Optimum machine capabilities for reconfigurable manufacturing systems

“…Reduction in product cost and responsiveness can be observed by customizing first the machining capabilities at product design stage and then the subsequent reuse of these capabilities at reconfiguration stage. It is also necessary to identify the maximum and minimum production capacity values among all configurations [7]. In this section different approaches are discussed in which scalability has been carried out through process planning and machine configurations.…”

“…This approach led to the machine tool reconfiguration by adding or subtracting machine modules going through different performance measures. A quantitative model was also developed for RMS scalability by Wang et al [7] which calculated the number of reconfigurations based on adjustment gradient. NSGA-II technique has also been used by Bensmaine et al [19] in the selection of optimal machines from the set of candidate machine configurations.…”

Reconfiguration point decision method based on dynamic complexity for reconfigurable manufacturing system (RMS)

“…Its architecture, hardware and software can be reassembled and adjusted to rapidly change the system function to respond to impacts from market fluctuation, technology innovation and policy change (Koren et al 1999;Koren 2013;Mehrabi et al 2000b). Because of its huge market potential, researchers have studied various aspects of RMS, including reconfigurability (Wang 2000), system layout planning (Goyal et al 2012;Wu et al 2007), system performance analysis (Cai 2004), part-family formation (Wang et al 2016a;Goyal et al 2013;Gupta et al 2013;Hasan et al 2014) and reconfiguration schemes evaluation (Wang et al 2016b). Zhao et al (2000aZhao et al ( , b, 2001a provide a stochastic RMS model that includes a framework, optimal configuration, optimal selection policy and performance measurement.…”