Editorial: Artificial Intelligence and Operations Research in the Supply Chain

Abstract

Different authors during the development of computer science have dedicated appropriate words to define Artificial Intelligence (AI), almost all of them equally valid. In a wide sense, it has been defined considering its origin and the way that intelligence is coded and transferred to devices to perform operations or to optimize processes for decision-making. In this way, it is notable the relation with multiple areas, for example, Operations Research (OR), which makes use of techniques from mathematical modelling, statistical analysis and optimization for decision-making. As it can be seen, both AI and OR have an intersection in the area known as decision-making, which does not mean that this is the only aspect where they cross.

Even though many areas use AI to solve their problems, it is and will remain a branch of computer science. AI introduces efficient algorithms for computational applications to present an intelligent human-like behaviour while performing tasks with a lot more precision and speed; furthermore, AI aims to simulate human behaviours such as reasoning and even creativity, as in the case of artificial art. AI has made great progress in the last years, which has spread to many areas and also to many different places.  An area of interest where many domains, concepts, processes, etc. intersect is OR. For example, OR is usually identified as a sub-area of applied mathematics, however, it includes sundry techniques and methods to improve decision-making and the prized efficiency of a system; these include optimization, queuing theory and simulation.