A web-based simulator for a discrete manufacturing system

Author(s):  
Wael M. Mohammed ◽  
Andrei Lobov ◽  
Borja Ramis Ferrer ◽  
Sergii Iarovyi ◽  
Jose L. Martinez Lastra
2017 ◽  
Vol 28 (5) ◽  
pp. 655-685 ◽  
Author(s):  
Christen Rose-Anderssen ◽  
James Baldwin ◽  
Keith Ridgway

Purpose The purpose of this paper is to critically evaluate the state of the art of applications of organisational systematics and manufacturing cladistics in terms of strengths and weaknesses and introduce new generic cladistic and hierarchical classifications of discrete manufacturing systems. These classifications are the basis for a practical web-based expert system and diagnostic benchmarking tool. Design/methodology/approach There were two stages for the research methods, with eight re-iterative steps: one for theory building, using secondary and observational data, producing conceptual classifications; the second stage for theory testing and theory development, using quantitative data from 153 companies and 510 manufacturing systems, producing the final factual cladogram. Evolutionary relationships between 53 candidate manufacturing systems, using 13 characters with 84 states, are hypothesised and presented diagrammatically. The manufacturing systems are also organised in a hierarchical classification with 13 genera, 6 families and 3 orders under one class of discrete manufacturing. Findings This work addressed several weaknesses of current manufacturing cladistic classifications which include the lack of an explicit out-group comparison, limited conceptual cladogram development, limited use of characters and that previous classifications are specific to sectors. In order to correct these limitations, the paper first expands on previous work by producing a more generic manufacturing system classification. Second, it describes a novel web-based expert system for the practical application of the discrete manufacturing system. Practical implications The classifications form the basis for a practical web-based expert system and diagnostic benchmarking tool, but also have a novel use in an educational context as it simplifies and relationally organises extant manufacturing system knowledge. Originality/value The research employed a novel re-iterative methodology for both theory building, using observational data, producing the conceptual classification, and through theory testing developing the final factual cladogram that forms the basis for the practical web-based expert system and diagnostic tool.


2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740071 ◽  
Author(s):  
Yan Wang ◽  
Zhicheng Ji

The difficulty in the energy efficiency analysis of discrete manufacturing system is the lack of evaluation index system. In this paper, a novel evaluation index system with three layers and 10 indexes was presented to analyze the overall energy consumption level of the discrete manufacturing system. Then, with the consideration of the difficulties in directly obtaining machine energy efficiency, a prediction method based on recursive variable forgetting factor identification was put forward to calculate it. Furthermore, a comprehensive quantitative evaluation method of rough set and attribute hierarchical model was designed based on the index structure to evaluate the energy efficiency level. Finally, an experiment was used to illustrate the effectiveness of our evaluation index system and method.


2012 ◽  
Vol 472-475 ◽  
pp. 2076-2079 ◽  
Author(s):  
Shu Feng Chai ◽  
Su Jun Luo ◽  
Li Jie Zhang

Since modern production system is a highly complicated discrete manufacturing system, it is very difficult to design the production line by traditional means. However, through building model of production system in virtual environment, analyzing and evaluating production system performance based on system simulating technology, the production system’s parameter and configuration can be optimized ahead of production plan to optimize production process and improve production efficiency. In this paper, the main shaft production line simulation model is constructed based on the object oriented discrete system software eM-Plant. The production line throughput, utilization and bottleneck operations are analyzed. Based on this, it can support the configuration of production line optimized. The example verifies that the modeling and simulation technology could be successfully used in manufacturing industry.


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