القوافل التجارية اإلفريقية خالل القرن التاسع عشر امليالدي: قوافل منطقة وادي نون منوذجا

ملخـص: اضطلعت قافلة منطقة وادي نون بدور مهم كوسيط وصـلة وصـل بـين اللـمال والجنـوب، فوقوع هذا المجال الصحراوي وسـط المغـرب، جعلـن منفـذا يجاريـا لل اـاري اإلفريقيـة اآلييـة مـن جنوب الصحراء، والمتجهة نحو شمال المغرب وأوروبا والعكس صحيح، إال أن ذلك ال يعني عـدم يعرض القوافل التجارية لملاكل يحول دون نجاح مهمتها، التـي يتطلـا الـتمكن مـن منهجيـة عمـل محكمة وما وطة، يحقق أرباح ونتارج إيجابية، سواء على المستوى االجتمـاعي أو االقتصـادي أو الثقافي. الكلمات المفتاحية: وادي نون، التجارة الصحراوية، القوافل التجارية.

Monitoring the Performance of Shewhart and Memory Control Charts by Using Bayesian Techniques

Most popular statistical process control (SPC) technique is control charting and control charts are used for monitoring the process and detection the unusual variations in the process parameters. Now, control charts have also been developed under Bayesian perspectives. In this study, we have suggested some Bayesian memory control charts for efficient monitoring the process parameters under different loss functions. We have considered squared error loss function (SELF), linex loss function (LLF), precautionary loss function (PLF) and general entropy loss function (GELF) in this study. Exponentially weighted moving average (EWMA) control chart is used for monitoring the mean and dispersion of the process in separate chapters while double EWMA (DEWMA) control chart and cumulative sum (CUSUM) control chart are also used for monitoring the mean of the process. Using Bayesian inference, two types of priors (informative and non-informative) are used for the construction of control charts. Sensitivity analysis for hyperparameters has also performed. Monte Carlo simulations are used to compute ARL and SDRL as performance measures of the control charts. It is shown that the proposed control charts under square error loss function are more efficient for detection of smaller shifts in the process parameters than the proposed control charts under other loss functions considered in this study. This thesis, in general, will help quality practitioners to use Bayesian methods for the monitoring of process location and dispersion under different loss functions.