Nowadays, the production model of many enterprises is multi-variety customized production, and the makespan and total tardiness are the main metrics for enterprises to make production plans. This requires us to develop a more effective production plan promptly with limited resources. Previous research focuses on dispatching rules and algorithms, but the application of the knowledge mining method for multi-variety products is limited. In this paper, a hybrid machine learning and population knowledge mining method to minimize makespan and total tardiness for multi-variety products is proposed. First, through offline machine learning and data mining, attributes of operations are selected to mine the initial population knowledge. Second, an addition–deletion sorting method (ADSM) is proposed to reprioritize operations and then form the rule-based initial population. Finally, the nondominated sorting genetic algorithm II (NSGA-II) hybrid with simulated annealing is used to obtain the Pareto solutions. To evaluate the effectiveness of the proposed method, three other types of initial populations were considered under different iterations and population sizes. The experimental results demonstrate that the new approach has a good performance in solving the multi-variety production planning problems, whether it is the function value or the performance metric of the acquired Pareto solutions.
In real production manufacturing process, there are many disturbances (e.g. machine fault, shortage of materials, tool damage) which can greatly interfere the original scheduling. These interventions will cost production managers extra time to schedule orders, which increase much workload and cost of maintenance. On account of this phenomenon, a novel system of data mining-based disturbances prediction for job shop scheduling is proposed. It consists of three modules: data mining module, disturbances prediction module, and manufacturing process module. First, in data mining module, historical data and new data are acquired by radio frequency identification or cable from database, and a hybrid algorithm is used to build a disturbance tree which is utilized as a classifier of disturbances happened before manufacturing. Then, in the disturbances prediction module, a disturbances pattern is built and a decision making will be determined according to the similarity between testing data attributes and mined pattern. Finally, in the manufacturing process module, scheduling will be arranged in advance to avoid the disturbances according to the results of decision making. Besides, an experiment is conducted at the end of this article to show the prediction process and demonstrate the feasibility of the proposed method.
Failure modes and effects analysis (FMEA) is a systematic approach that focuses on evaluating critical disturbances in a system. However, traditional FMEA has its own drawbacks, such as invalid computations and ambiguous priority definitions, which lead to many constraints in the application of complex production processes, especially in job shops with various resources. Therefore, this paper proposes an analytic disturbance prediction method for job shop with multiple resources and multiple evaluation indexes, which combines the vector computing techniques, FMEA, and fuzzy analytic hierarchy process (FAHP). In contrast to other work, this paper focuses on the establishment of FMEA mathematical model to improve the readability of multi-resource disturbance risk results. To this end, the projection of the disturbance vector is visualized to reduce repeated calculation results, triangles and trapezoids are used as membership functions to improve the accuracy of weight, and the differentiation index is used to reduce the ambiguity of priorities. The proposed method can effectively discover the critical disturbances and enable managers to undertake more assertive decisions.
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