Decision Optimization With Solvers

Date: 15-17 May 2017

Time: 8.30am - 5pm

Venue: Kompleks ЄUREKA, USM, Penang

Course Synopsys

Traditionally businesses will focus on efficiency to differentiate themselves from competitors. This includes waste reduction, faster throughput time, and better customer service. While efficiency is still important in today’s environment, it is no longer sufficient for differentiation. Today’s buzzwords include Big Data and Analytics. Actions include installing huge IT infrastructure for data collection and storage, acquiring skills in processing structured and unstructured data and in advanced statistical software like R, and implementing data visualization. Provided that data is available and usable, companies will have personnel and tools for reporting and prediction.

However, the management still has to make decisions based on gut feel. There is no actionable recommendation provided by the systems and reports, or what-if analyses to quantify what will happen if different course of actions are taken. There are 3 types of analytics: descriptive (what has happened), predictive (what will happen), and prescriptive (what should happen). This training is about prescriptive analytics (top third in the diagram above).

Prescriptive analytics recommends one or more courses of action and showing the likely outcome of each decision so that the business decision-maker can take this information and act. It uses advanced analytical methods from Operations Research (OR) / Management Science (MS) / Decision Science for quantitative decision making to help make better decisions. No more gut feel or crystal ball in decision making. OR/MS is not new as it started during World War II. Optimization problems need to be formulated as mathematical programming and solved to get the solutions/ recommendations.

Today’s computing capability has enabled very large and complex practical problems to be solved very quickly. Even MS Excel can be used. Unfortunately OR/MS applications are not widespread in Malaysia compared to USA, Europe, South Korea, Taiwan, and Singapore. This training is very practitioner-centric. While some basic theories are covered, the focus is on learning the fundamentals of mathematical optimization and how to apply it. Mathematical modelling is difficult to learn and initially hard to apply into real-world problems. With his industry experience, the trainer will teach how to apply mathematical optimization using real world examples and tools used by industry.


Optimization could be beneficial to your organization when:

  1. The management faces complex decision making

  2. The management is not sure what the main problem is

  3. The management is uncertain about potential outcomes

  4. The organization is having problems with decision making processes

  5. Management is troubled by risk

  6. The organization is not making the most of its data

  7. The organization needs to beat stiff competition.

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