PurposeThis paper seeks to propose a new approach for tackling the uncertainty and imprecision of identifying suitable supplier offers, evaluating these offers and choosing the best alternatives in bi‐negotiation. In a build‐to‐order supply chain, the handling of uncertainties is addressed by a real time information sharing system and appropriate supplier selection.Design/methodology/approachA methodology integrated analytic hierarchy process (AHP) with bi‐negotiation agents based on the multi‐criteria decision‐making approach and software agent technique is then developed to take into account both qualitative and quantitative factors in supplier selection.FindingsDuring the decision‐making between buyer and suppliers, the AHP process matches product characteristics with supplier characteristics. Next, agents assist the user in the debate to negotiate a joint representation of the supplier chosen and automatically justify proposals with this joint representation.Originality/valueThis study focused on a multi‐attribute negotiation mechanism including qualitative conditions, which enables automated negotiation on multiple attributes. Finally, a fuzzy membership function represented the joint representation's cognition for each condition such as quantity, price, quality, and delivery for the outsourced component. A case study in a high‐end computer manufacturing company is given to demonstrate the potential of the methodology.

Manufacturing industries are gradually changing to green production due to the increasing production cost. Reducing tool wear in production can not only decrease production cost but also the effect the environment. Thus, it becomes a crucial issue for the machining industry. This study investigates the optimal machining parameters for the computer numerical controlled turning process of S45C steel in minimizing tool wear. The correlation between control parameters (speed, cutting depth, and feed rate) and production quality were constructed by using semantic rules and fuzzy quantification. The Taguchi method was additionally employed to determine the optimal turning parameters. Under the consideration of environmental protection and tool cost, the optimal machining parameters were furthermore derived from the fuzzy semantic rules. The practicability of the optimal parameters was moreover verified through turning experiments. It is found that the proposed method in this study is appropriate and applicable to universal applications.

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With the development of manufacturing technology and strict international environmental regulations, green production has become an imperative research topic in the manufacturing industry. Because cutting is affected by several factors, the development of green processes is vital for automated cutting using machines. These factors are often subjective and are set up merely based on the manual and experience of engineers, reducing tool life and deteriorating tool precision; ultimately, this approach increases production costs and reduces production efficiency. Moreover, the ecological environment can be seriously affected. Furthermore, the parameters must be adjusted according to variations in the processing state, which is a considerable drawback for the automated cutting industry. In this study, we investigated the precision computerized numerical-controlled cutting process as an example. We first assessed the literature and investigated tool wear and cutting noise as quality standards for green computerized numerical-controlled cutting. The cutting depth, cutting speed, feed rate, and the tip of the center were selected as control parameters. The consistency of the results was verified through an expert questionnaire conducted using analytic hierarchy process, and the weighted values of the control parameters were obtained. Simultaneously, seven environmental efficiency elements of the World Business Council for Sustainable Development and TRIZ 39 engineering parameters of the CSI project were used to establish the engineering parameters for the green production design. Furthermore, 40 inventive principles from a contradiction matrix were used to design an optimization strategy to develop and verify an innovation strategy of singular quality. Finally, the experimental results revealed that implementing the analytic hierarchy process coupled with the TRIZ innovative thinking mode and green production concept enables enterprises to reduce their consumption of raw materials and waste production during the design process. This approach effectively reduces the burden on the environment and thus facilitates industry competitiveness and sustainability.

Under the strict restrictions of international environmental regulations, how to reduce environmental hazards at the production stage has become an important issue in the practice of automated production. The precision computerized numerical-controlled (CNC) cutting process was chosen as an example of this, while tool wear and cutting noise were chosen as the research objectives of CNC cutting quality. The effects of quality optimizing were verified using the depth of cut, cutting speed, feed rate, and tool nose runoff as control parameters and actual cutting on a CNC lathe was performed. Further, the relationships between Fuzzy theory and control parameters as well as quality objectives were used to define semantic rules to perform fuzzy quantification. The quantified output value was introduced into game theory to carry out the multi-quality bargaining game. Through the statistics of strategic probability, the strategy with the highest total probability was selected to obtain the optimum plan of multi-quality and multi-strategy. Under the multi-quality optimum parameter combination, the tool wear and cutting noise, compared to the parameter combination recommended by the cutting manual, was reduced by 23% and 1%, respectively. This research can indeed ameliorate the multi-quality cutting problem. The results of the research provided the technicians with a set of all-purpose economic prospective parameter analysis methods in the manufacturing process to enhance the international competitiveness of the automated CNC industry.