This article introduces decision disorder and how operation research (OR) can help overcome it. It explains the historical background, applications, and main techniques of OR, and how it can be used to efficiently utilize limited resources to make optimal decisions.
Have you ever been told that you have ‘decision disorder’? It’s also known as ‘Hamlet syndrome’ and refers to the inability to make a decision when faced with multiple options. It’s a psychological problem that many people suffer from in the modern world, and it’s one that’s gaining increasing attention. For example, if you’re calling a restaurant to order food and you can’t decide whether to get a sandwich or a burger, you’re suffering from “decision disorder.” It’s not just limited to everyday decisions, but can also occur when you’re facing a major decision, such as choosing a career path, making a career change, or making a relationship decision. The difficulty we experience in making decisions, big and small, can sometimes affect our quality of life.
Decision disorder can be characterized as a lack of decisiveness or fear of making choices. Psychologically, it’s also linked to anxiety, stress, and perfectionism. For example, indecisive people are often afraid of “making the wrong decision,” or they tend to want to make the perfect choice from all the options. This can cause us to dwell on many choices for a long time, often resulting in unsatisfying decisions.
If you search for “decision disorder” on the internet, you’ll find a lot of information on how to overcome it. For example, articles and blog posts with titles like “11 Ways to Overcome Decision Disorder” show that there is a great deal of social interest in this problem. This interest has gradually led to various studies in the fields of psychology, personal development, and business. In addition to psychological approaches, there are also several analytical techniques and optimization methods to solve this problem. At this point, you might be wondering if there is a specific discipline that can help you overcome this “decision disorder” and make clear decisions.
As mentioned above, we face a lot of decision-making situations in our lives. Especially in today’s complex and fast-paced society, we have to make multiple choices every day. Even the process of waking up in the morning and getting to the classroom involves a lot of decisions. We decide whether to eat breakfast or forgo it based on the time we wake up, and we choose between transportation options such as bus, taxi, or subway to get from home to class. It also involves choosing a particular route based on arrival time, or weighing different alternatives by comparing transportation costs and efficiency. These seemingly simple everyday decisions involve complex analysis and evaluation, even when we don’t realize it.
Let’s take a closer look at the decision of how to get to school. We consider the time it takes to get to school, the cost, the convenience, and so on, and choose the method that we think is the most “right” for us. This process depends not only on our personal preferences, but also on external factors, such as traffic conditions or climate change. In other words, decision-making is the process of making the “best choice” among several options. One of the key analytical techniques that can help you make such decisions more systematically and rationally is Operations Research (OR).
Operations Research (OR) is an optimization technique for using limited resources as efficiently as possible. It studies how to find the maximum possible output from a given resource and how to achieve it. For example, finding the best way to travel to school with the limited resources of time and money is an example of how OR can be used. OR is widely used in various fields such as business management, industry, and military strategy, as well as in personal life, and contributes to minimizing the waste of resources and maximizing efficiency through optimized decision-making.
Operations Research first came to prominence in the late 1930s, during the height of World War II, when the United Kingdom was looking for ways to effectively defend against Nazi air raids, and the War Office was studying how to deploy and operate new radar technology. The focus of OR was not just on the development of radar, but on finding the most efficient way to use the resources that already existed. This research paved the way for OR to spread globally and was an important contribution to the postwar industry as a whole.
The adoption and expansion of OR has since spread to many different fields. Initially, it was mainly used for military strategy, but over time, it began to be applied in various fields such as economics, industry, and management. In the mid-20th century, especially in the 1960s and 70s, OR played an important role in helping companies improve productivity and solve resource management problems. As a result, OR is now widely used in policy making in public institutions, logistics optimization, and risk management in finance. For example, large-scale supply chain management or strategic decisions of global companies are made using OR to create mathematical models and make the most efficient decisions based on them.
However, OR wasn’t always successful. In the early days, there were a number of challenges. First of all, it was very difficult to collect the data needed to build the models. The lack of data or incomplete data made it difficult to solve optimization problems. Also, the hardware and software power of computers at the time was insufficient to handle complex mathematical models. For example, in the early days, OR problems were very slow to process, requiring 120 workers to work for a day to solve a problem with 77 variables. Over time, however, as information and communication technologies developed and data processing power increased dramatically, the scope of OR’s application expanded.
In particular, the development of information and communication technology (ICT) since the 1990s has been a catalyst for the further development of OR. During this period, technologies were developed to process large amounts of data more quickly and accurately, which allowed OR to be actively used in various industries. For example, in finance, OR is used to optimize investment portfolios, and in healthcare, OR techniques are used to manage resources in hospitals and optimize surgery schedules. In the logistics industry, OR is also playing an important role in reducing logistics costs by finding optimal routes.
Today, OR has evolved into a variety of techniques to solve more complex problems, including linear programming, conic programming, and mixed integer programming. Linear programming is an efficient method that can solve problems with hundreds of thousands of variables and constraints in minutes. Conic programming, on the other hand, is an extended version of linear programming and is often used for uncertain data or in finance. In addition, mixed-integer planning is used to solve complex problems such as power supply and production planning, and is already used in Korea’s electricity exchange.
As such, OR is constantly evolving, and it will become even more important in the future by combining with various technologies. In particular, combining OR with ‘Big Data’ is one of the most important directions that OR will take in the future, allowing us to analyze and utilize vast amounts of data in a sophisticated way. In the future, we can expect OR to play an increasing role in the decision-making process, which will give us the ability to make better choices.
