The modern industrial structure is a huge system with many different disciplines intertwined. To efficiently manage this complexity, industrial engineering was born, which focuses on maximizing the productivity of companies through system analysis and optimization, and deriving rational management strategies.
If you look at the structure of modern industry, you’ll see that it’s a huge system with many different disciplines intertwined. For example, conglomerates such as Samsung and LG in South Korea not only have various business fields, but also expand their business globally. This creates a demand for complex elements that connect each business area and enable seamless communication across organizational hierarchies. In addition, as business structures and products become increasingly diverse and complex, companies look to outsourcing for things they can’t do themselves. This has led to a growing need to streamline complex organizational management, logistics management, and distribution management. Companies are now exploring how to run them efficiently through multiple methodologies.
Industrial engineering is a discipline that was created to fulfill this need. Unlike other engineering majors that focus on the research and development of specific technologies, industrial engineering aims to innovate and systematize the work processes of companies by combining scientific principles and management strategies. It also deals with scientific theories and methodologies to derive rational methods and create comprehensive management strategies. The fields of industrial engineering can be divided into five main areas: Business-economic systems, systems analysis and optimization, operations management, product and service design, and information systems. Within each of these fields, there are several research areas.
Among them, system analysis and optimization is a field that describes industrial engineering. As industrialization has progressed and various industries have developed, the systems of companies have become complex and huge. As a result, inefficient processes have arisen here and there, and the task of solving them is system analysis and optimization. The foundation of industrial engineering is Taylor’s work management theory, which is the basis of ergonomics or human interface systems, but the original purpose of work management is to increase efficiency to improve productivity and design optimal systems. Designing optimal systems means ensuring that workers, processes, organizational operations, etc. are operating at maximum efficiency. This can be done through repair planning or optimization.
Optimization is a discipline for solving real-world problems. It was created by mathematicians and economists during World War II to help the military solve problems in planning, transportation, and more. The discipline provides methodologies for maximizing productivity in each industry. For example, optimization models and solutions are applied to routing KTX vehicles, finding economical cutting patterns for a paper company, effectively allocating resources in a wireless communication network, constructing investment portfolios that minimize risk, and classifying genome decoding. Current research trends in optimization focus on the application of optimization models and solutions to the design, operation, quality assurance, and pricing of telecommunications, which has the greatest economic potential and scale. As these few examples illustrate, optimization is now expanding its application across industries that drive social growth with much more sophisticated and mathematical methodologies. The potential scale of business using optimization is also growing in Korea. As the number of researchers who can utilize it grows, the demand for optimization will come from a wider range of fields based on the utility of optimization solutions.
Optimization solves real-world problems in a mathematical and scientific way, so modeling is essential to extract and logically reconstruct each element of the problem. At its core, optimization is the study of the structure and solution of mathematical models. It can be divided into several subfields based on mathematical models, the main ones being nonlinear planning, network flows, large-scale problem solving, stochastic process planning (stockchastic programming), integer planning, LCP, and practical polynomial solutions of linear plans. Most of these remain important research topics. For example, in logistics systems, we study finding the optimal travel route with the least cost. Factors such as cost, vehicles, and locations are optimized as variables, and optimal solutions are found using linear programming methods, simplex solutions, etc.
Recently, the field of optimization has been focusing on models with discrete structures because modern systems are becoming more and more discrete in their details. Most optimization problems with discrete structures are NP-hard to solve. By NP-hard, we mean that there is no way to get an exact answer to a problem other than to check all the numbers in all cases. Because of this, the study of the structure of related integer planning and combinatorial optimization problems and the theory behind their solutions has become one of the main topics in optimization today. In fact, the field of integer and combinatorial optimization solutions continues to evolve. Optimization is evolving as it deals directly with real-world problems. Today, thanks to faster computing technology, we can solve problems of sizes that would have been impossible just five or six years ago. Whereas mathematics used to be a theoretical discipline studied at a desk, optimization mathematics has transformed into a discipline that solves real-world problems. This real-world nature of optimization is maximized when it meets computer theory. It is well known that the development of computers was a decisive early factor in the evolution of optimization. In particular, the theory of the solution of problems with NP-hard properties has evolved into a theory of approximate solutions to optimization problems.
As such, the discipline of industrial engineering requires knowledge of the individual components of complex systems in order to adapt to changing environments. Industrial engineering utilizes each component efficiently to help make decisions throughout the system and ensure that it can effectively provide services when needed. Industrial engineering is a discipline that allows you to see both the trees and the forest.
