In this blog post, we explore why decision disorder occurs in everyday life and how Operations Research can systematically resolve it.
Have you ever heard the term ‘decision disorder’? Also known as ‘Hamlet Syndrome’, it describes the situation where one wavers indecisively when faced with multiple choices. This is a psychological issue experienced by many in modern society and is one phenomenon gaining increasing attention. For example, if you call a restaurant to order food and spend ages hesitating between a sandwich and a hamburger, you are experiencing ‘decision disorder’. Moreover, this isn’t limited to everyday choices; it can also surface when facing important decisions like career paths, career changes, or interpersonal relationships. The difficulties in making both big and small decisions experienced in daily life can sometimes impact quality of life.
‘Decision disorder’ can be said to stem from a lack of decisiveness or a fear of choice. Psychologically, it is also linked to anxiety, stress, and perfectionism. For example, people who struggle to make decisions often fear “making the wrong choice” or show a tendency to want to make the perfect choice from all available options.
Consequently, we often hesitate for extended periods at many crossroads of choice, frequently resulting in unsatisfactory decisions. Searching ‘decision disorder’ online yields diverse information, including numerous articles addressing ways to overcome it. For instance, articles or blog posts titled ‘11 Ways to Overcome Decision Disorder’ demonstrate significant social interest in this issue.
This interest is gradually leading to diverse research in psychology, self-development, and business fields. Beyond psychological approaches, methods utilizing various analytical techniques and optimization methods are also being proposed to solve this issue. At this point, wouldn’t you be curious if there existed a specific discipline that could help you overcome this ‘decision disorder’ and make clear decisions?
As mentioned above, we face countless decision-making situations throughout our lives. Especially in today’s complex and rapidly changing society, we must make multiple choices daily. Even the process of waking up in the morning and arriving at the classroom involves countless decisions. We decide whether to eat breakfast based on the time we wake up, and we choose one mode of transportation—like a bus, taxi, or subway—to get from home to the lecture hall. This also includes selecting a specific route considering arrival time or evaluating various alternatives by comparing transportation costs and efficiency. While these everyday decisions may seem simple, they involve complex analysis and evaluation happening unconsciously.
Let’s take a closer look at the decision of choosing how to get to school. We select the method we deem most ‘suitable’ by comprehensively considering factors like travel time, cost, and convenience. This process involves not only personal preferences but also external factors, such as traffic conditions or weather changes, acting as important variables. In other words, decision-making can be defined as the process of selecting the ‘best choice’ from a set of predefined candidates. One of the core analytical techniques that helps make such decisions more systematic and rational is ‘Operations Research’ (OR).
Operations Research (OR) is an optimization technique aimed at maximizing the efficient use of limited resources. It studies how to find and achieve the greatest possible output within the constraints of available resources. For example, finding the best possible method for commuting to school using limited resources like time and cost is a practical application of OR. OR is widely used not only in personal life but also across diverse fields such as corporate management, industry, and military strategy. It contributes to minimizing resource waste and maximizing efficiency through optimized decision-making.
‘Operations Research’ first gained significant attention in the late 1930s, during the height of World War II. At that time, Britain was seeking ways to effectively defend against Nazi air raids. To this end, the Ministry of State researched how to deploy and operate new radar technology. In this process, OR focused not merely on developing radar but on finding ways to use existing resources as efficiently as possible. This research became the catalyst for OR’s global spread and made significant contributions to post-war industry.
The introduction and expansion of OR subsequently spread into various fields. Initially used primarily for military strategy, over time it began to be applied in diverse fields such as economics, industry, and management. In the mid-20th century, particularly during the 1960s and 70s, OR played a crucial role in enhancing corporate productivity and solving resource management problems. Consequently, OR is now widely used in policy decision-making within public institutions, logistics optimization, and risk management in the financial sector. For instance, managing large-scale supply chains or making strategic decisions for global corporations involves creating mathematical models through OR and using them to determine the most efficient course of action.
However, OR was not successful from the outset. Several challenges existed in its early days. First, collecting the data required to build models was extremely difficult. Insufficient or incomplete data made solving optimization problems challenging. Additionally, the hardware and software capabilities of computers at the time were insufficient to handle complex mathematical models. For instance, early OR problems were processed extremely slowly; solving a problem with just 77 variables required 120 workers laboring for an entire day. However, as time passed, advancements in information and communication technology (ICT) and dramatic improvements in data processing capabilities significantly expanded the scope of OR applications.
Particularly since the 1990s, advancements in information and communication technology (ICT) became a catalyst for OR’s further development. During this period, technologies capable of processing large volumes of data more quickly and accurately were developed, enabling OR to be actively utilized across diverse industrial sectors. For instance, the financial sector employs OR for optimizing investment portfolios, while the medical field uses OR techniques for hospital resource management and optimizing surgery schedules. Furthermore, in the logistics industry, OR plays a crucial role in finding optimal routes to reduce logistics costs.
Today, OR has evolved with various techniques to solve increasingly complex problems. Among these, representative solution methods include linear programming, conic programming, and mixed-integer programming. Linear programming is an efficient method capable of solving problems with hundreds of thousands of variables and constraints within tens of minutes. Convex programming, an extended version of linear programming, is primarily used with uncertain data or in finance. Mixed-integer programming is employed to solve complex problems like power supply or production planning and is already utilized by Korea’s power exchange.
As such, OR is constantly evolving, and its importance will grow even more in the future as it combines with increasingly diverse technologies. In particular, integration with ‘Big Data’ represents one of the key directions for OR’s future advancement, enabling sophisticated analysis and utilization of vast datasets. In future society, the role of OR in decision-making processes is expected to expand significantly, providing us with the ability to make better choices.
