<![CDATA[Scheduling and resource allocation are two crucial aspects in various fields, including manufacturing, transportation, education, and information systems. The complexity of decision making is often increased by integer constraints, such as the number of workers, machines, or indivisible working hours. Therefore, the Integer Linear Programming (ILP) approach is one of the methods widely used in solving optimization problems involving discrete variables. This literature study aims to review previous studies that apply ILP in the context of scheduling and resource allocation optimization. This study reviews model approaches, solution techniques such as the branch and bound method and cutting plane, and their implementation in various real cases. The results of the study show that ILP is able to provide optimal or near-optimal solutions in scenarios with complex constraints and integer variables. This study also identifies challenges in implementing ILP models, such as the scale of the problem and high computational requirements, as well as opportunities for further research that includes hybridizing the ILP method with a heuristic approach. Thus, ILP remains a very relevant and effective tool in supporting optimization-based decision making in various sectors.]]>
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