EVOPtimizer 進化操作法軟體-統計分析軟體/新永資訊有限公司

EVOPtimizer 進化操作法軟體

EVOPtimizer 進化操作法軟體

  • EVOPtimizer 進化操作法軟體
  • 編號
  • 類別
    統計分析軟體
  • 介紹
    該軟件使最有效的進化操作 (EVOP) 技術自動化。友好且易於使用,您將能夠在幾分鐘內將這款軟件應用於疑難問題。
  • 價格

EVOPtimizer Evolutionary Operating Method Software

Why EVOP
First of all EVOP is much easier to apply than traditional experimental techniques such as response surfaces, factorial designs and even Taguchi methods.  It is also more accurate.  A second order polynomial is the best approximation to the response surface other methods can make.  This assumption limits the accuracy of the final answer.  Another problem with conventional methods is the complexity with the number of factors.  The table below shows the minimum number of trials required for a response surface design as a function of the number of factors.


In addition, it usually takes several response surface designs (each modeling a smaller portion of the region) to obtain a satisfactory solution.

With sequential simplex optimization the number of trials in the initial simplex is k+1 where k is the number of experimental factors.  Optimization is achieved by evaluating the slope of the immediate area and moving in the best direction.

Another advantage of EVOP is the effect on current production.  The factor settings for the initial simplex can be set very close to current production settings.  This allows the initial experimental trials to be run with either no impact or a limited impact on either scrap or downtime.  Additional experimental trials improve existing production output over time.  EVOP is a great way to pursue continuous improvement!

Traditional experimental techniques are difficult to use when yield is the response variable.  How can conduct an experiment to reduce scrap for 1000 parts per million?  At least 1000 trials are required to expect to find one defect.  EVOP is perfect in this situation because the initial trials are conducted with factors setting very close to current production levels.  This allows an experimental setting to be replicated for weeks if necessary because there is either an improvement in scrap or a slight degradation.

系統需求

OS:
Required Operating System: Windows XP or later

EVOPtimizer 進化操作法軟體

為什麼選擇 EVOP
首先,EVOP 比響應曲面、因子設計甚至田口方法等傳統實驗技術更容易應用。它也更準確。二階多項式是其他方法可以做出的響應曲面的最佳近似。這個假設限制了最終答案的準確性。傳統方法的另一個問題是因素數量的複雜性。下表顯示了作為因子數量的函數的響應曲面設計所需的最小試驗次數。


此外,通常需要多次響應曲面設計(每個都對區域的較小部分進行建模)才能獲得滿意的解決方案。

對於順序單純形優化,初始單純形中的試驗次數為k +1,其中k是實驗因子的數量。通過評估直接區域的斜率並沿最佳方向移動來實現優化。

EVOP 的另一個優點是對當前生產的影響。初始單純形的因子設置可以設置得非常接近當前生產設置。這允許在對廢料或停機時間沒有影響或影響有限的情況下運行初始實驗試驗。隨著時間的推移,額外的實驗性試驗提高了現有的產量。EVOP 是追求持續改進的好方法!

當產量是響應變量時,傳統的實驗技術很難使用。如何進行實驗以減少 1000 ppm 的廢料?至少需要 1000 次試驗才能找到一個缺陷。EVOP 在這種情況下是完美的,因為初始試驗是在非常接近當前生產水平的因素下進行的。這允許在必要時重複實驗設置數週,因為廢品率有所改善或略有下降。

 

Comprehensive Meta-Analysis 3 統計分析軟體

Meta分析是結合來自多項研究的數據統計程序。當治療效果(或效果大小)的一項研究是一致的,Meta分析可以用來識別當前普遍的影響,從一項研究中至下一項的效果變化,Meta分析可被用來確定變化的原因。 

特價0

Comprehensive Meta-Analysis 3 統計分析軟體

IBM SPSS Statistics Base Edition 統計分析軟體

IBM® SPSS® Statistics Base Edition 提供的功能能支援整個分析程序,包括資料準備、敘述統計、線性迴歸、視覺化繪圖及報告。 您可以存取多個資料格式,沒有任何資料處理大小限制。進階資料準備功能有助於免除勞力密集的人工作業。使用逾 30 個分析程序,例如雙變數統計資料程序、因素和叢集分析以及自助抽樣法。您也可以使用 R 或 Python 來擴充功能。

特價0

IBM SPSS Statistics Base Edition 統計分析軟體

Analyse-it 5.56 方法評估分析軟體

Analyse-it 是一個方法評估分析軟體,檢驗科或者那些需要檢驗、核實分析診斷數據方法的研究員使用

特價0

Analyse-it 5.56 方法評估分析軟體