A Fuzzy-Neural Ensemble and Geometric Rule Fusion Approach for Scheduling a Wafer Fabrication Factory

Abstract

In this study, the fuzzy-neural ensemble and geometric rule fusion approach is presented to optimize the performance of job dispatching in a wafer fabrication factory with an intelligent rule. The proposed methodology is a modification of a previous study by fusing two dispatching rules and diversifying the job slacks in novel ways. To this end, the geometric mean of the neighboring distances of slacks is maximized. In addition, the fuzzy c-means (FCM) and backpropagation network (BPN) ensemble approach was also proposed to estimate the remaining cycle time of a job, which is an important input to the new rule. A new aggregation mechanism was also designed to enhance the robustness of the FCM-BPN ensemble approach. To validate the effectiveness of the proposed methodology, some experiments have been conducted. The experimental results did support the effectiveness of the proposed methodology.

Simulation-Based Planning and Control of Transport Flows in Port Logistic Systems

“…The following dispatching methods were listed in a study by Wu and Chen [21]: dispatching rules; heuristics; data mining based approaches; agent technologies; and simulation. Moreover, the aforementioned work demonstrated that several studies have been conducted in an attempt to solve the problem of scheduling.…”

“…Moreover, the aforementioned work demonstrated that several studies have been conducted in an attempt to solve the problem of scheduling. Regarding the approaches used in these studies, Wu and Chen [21] highlight the following: (i) selection of the most appropriate dispatching rule among those that are available; (ii) inclusion of adjustable parameters that can optimize the dispatching rule; and (iii) best estimate of the schedule through the results of a limited number of simulations. As for the approach (ii), it is emphasized that this can only be used with factors that are defined previously and remain static [21].…”

Internal Due Date Assignment in a Wafer Fabrication Factory by an Effective Fuzzy-Neural Approach

“…(3) Generalized average method [30]: in FCM, the error is proportional to the distance to the center. For this reason, a natural way to aggregate the forecasts is…”

“…The process stops after five iterations because the upper bounds remain unchanged after the fifth iteration. The results of the five iterations are summarized in Table 9, from which the allowances which are 25,33,54,48,56,57,58,44,54,53,42,46,55,48,47,50,48,41,36,39,30,41,31,24,34,29,27,13,37,24,47,15,34,50,44,49,53,50,53, and 53 added to the cycle times are derived with an average of 42 (hours). The due date of a job is then set to the release time plus the upper bound of the cycle time.…”

Scheduling Semiconductor Multihead Testers Using Metaheuristic Techniques Embedded with Lot-Specific and Configuration-Specific Information

“…In wafer fabrication, a pattern of circuitry is imprinted onto the surfaces of a wafer, on which hundreds of dice are fabricated, and dice are individually tested in the process of wafer probe. In recent years, vast research of scheduling has been done in the field of wafer fabrication [1][2][3][4][5][6][7][8]. In [9,10] and the wafer probe process was scheduled as a mathematical model to minimize the makespan.…”