طراحي‌ آزمايشها درسي‌ است پيشرفته‌ در زمينة‌ آمار كاربردي‌ براي‌ دانشجويان‌ كارشناسي‌ ارشد و دكتراي‌ رشته‌هاي‌ مهندسي‌، علوم‌ مديريت‌، فيزيك‌، شيمي‌، تحقيق‌ در عمليات‌، مهندسي شيمي، كشاورزي‌ و ... ‌. در اين‌ درس‌ تأكيد براين‌ است‌ كه‌ مفاهيم‌ و تكنيكهاي‌ آماري‌ براي‌ طراحي، اجراء  و تحليل آزمايشهاي لازم براي رسيدن به نتايج معتبر علمي فراهم‌ شود.

فرض‌ بر اين‌ است‌ كه‌ دراين‌ درس‌ دانشجو با توزيعهاي‌ احتمال‌ نرمال‌،  t  ،  مربع کای ،  F  و همينطور با مفاهيم‌ فواصل‌ اطمينان‌ و آزمونهاي‌ فرض‌ آشنايي‌كافي‌ دارد. آشنايي‌ مختصر با تكنيكهاي‌ رگرسيون‌ و كمترين‌ مربعات‌ مفيد خواهد بود. مطالب‌ اين‌ درس‌ به‌ صورت‌ سريع‌ و با تأكيد بر كاربردهاي‌ آن‌ تدريس‌ خواهد شد.

منبع‌ اصلي‌ براي‌ تدريس‌ اين‌ درس‌ عبارت‌ است‌ از :

* Douglas C. Montgomery, Design & Analysis of Experiments, 4th ed., John Wiley & Sons, 1997

بعضي‌ از منابع‌ ديگر كه‌ براي‌ اين‌ درس‌ مفيدند عبارتند از:

  • Charles R. Hicks; Fundamental Concepts in the Design of Experiments; Second ed., Holt, Rinehart & Winston, 1973.
  • Douglas C. Montgomery & George C. Runger; Applied Statistics & Probability for Engineers; John Wiley & Sons, 1994.
  • John Neter, W. Wasserman, & M. H. Kutner; Applied Linear Statistical Models; Third ed., Irwin, 1990.

Scheduling is the process of allocating scarce resources to a set of tasks over time. In this course, we look at practical scheduling problems, solution techniques, and algorithms in both manufacturing and service industries. Specifically, we look at job shop scheduling, timetabling, project scheduling, supply chain scheduling, workforce scheduling, healthcare scheduling, and sports scheduling and discuss various solution procedures including heuristics, constraint programming, local search, and dispatching rules. We apply our knowledge to investigate one case study concerning real world scheduling problems and learn from one guest speaker who discusses interesting scheduling challenges and opportunities

Analytics is not a new invention, but rather a coming together of several technologies and fields of science including data warehousing and management, data mining, statistical modeling, forecasting, optimization, and most importantly management decision making under uncertainty.

 

In this course we first discuss predictive analytics that provides techniques to model the relationships between inputs and outcomes, and construct predictions about future outcomes. Then, we cover the prescriptive analytics that provides tools to optimize actions against a complex set of objectives to find best practices and design best policies under all circumstances. We also look at practical problems, solution techniques, and algorithms. Specifically, we look at examples in supply chains, service industries, healthcare systems, revenue management, inventory management, and sports. Finally, we apply our knowledge to investigate several case studies concerning real world problems and learn from a couple of guest speakers who discusses interesting challenges and opportunities that data analytics has presented.