The new technologies brought to life by Industry 4.0 have led to the transformation of the digital world and thus supply chains. To continuously improve speed, accuracy, efficiency, and quality, it was necessary to drastically change the information flow and improve the optimization processes. The complex and complicated tasks of supply chains belong to the difficult-to-solve, np-hard complexity class. In many cases, metaheuristics are used to solve these problems, which can provide results much faster than exact methods, but in many cases, they fall short in terms of accuracy. According to the authors, the use of metaheuristics should be treated as part of some kind of trade-off. These algorithms are usually compared to each other, not to a different kind of optimization procedure, and they do not observe the point that can determine which method is the more favorable, more profitable procedure. The authors present the above topic in this chapter, which helps to reveal further research gaps and new research directions.
By the 21st century, logistics and various supply chains had become key units in the global market and corporate structures. Industry 4.0 has brought developments and implementations to life that have drastically changed and are still changing the practices used in certain areas of logistics. Many new technologies (advanced robotics, additive manufacturing, artificial intelligence (AI), blockchain, drones, Internet of Things (IoT)) have emerged in the digital world, which many companies are using to develop cyber-physical systems in order to increase efficiency, speed, accuracy and the ability to change and steer competition between companies around the world. Planning tasks at the strategic, tactical and operational levels are covered in the areas of production and logistics. The tasks presented here can be identified as extremely complex optimization problems that belong to the np-hard complexity class. These can be addressed in many cases with metaheuristics, and industry also often uses search strategies inspired by biological or physical processes. Metaheuristic algorithms simulate the behavior of a selected phenomenon in a given search area. Algorithms based on various principles can help optimize processes, such as: population-based algorithms, evolutionary methods, behavior-inspired procedures, swarm intelligence methods, etc. New technologies or metaheuristic procedures are also increasingly used in logistics due to the complexity of the tasks. This paper presents theoretical application possibilities of digital transformation, AI and IoT in the field of logistics. The paper provides a further brief overview of the problems surrounding metaheuristics, supported by examples. The article shows the impact of different Industry 4.0 technologies on logistics. There is a shortage of such comprehensive studies, so the article helps provide insight into innovative optimization opportunities in a larger area - the field of logistics. Within this one paper, the impact of new technologies on the field of logistics was collected. A brief description of these will help to identify further directions and deepen the applicability of the new methods in logistics.
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