Application of Artificial Intelligent in Production Scheduling: a critical evaluation and comparison of key approaches

Production scheduling is a part of operational research which relies on combinational optimization solved by discrete methods. This wide area covers different variety of problems like; vehicle routing problem, bin packing problem and job priority. In order to solve these problems, operational research applies two main principles: exact methods which provide the absolute best solution but solve only small sized problems, and approximate methods which provide only good solution but solve near real life sized problem. The second category provides various methods divided into: problem dedicated methods called heuristics and general method called metaheuristics. Many of these metaheuristic methods are leading the literature of production scheduling for past two decade, like; Genetic Algorithm, Neural Network, and Fuzzy Logic which will be discuss in this paper. This review shows that there are only few research works which compare heuristic techniques on scheduling problem. There is a need for scholars to focus on evolutionary manufacturing systems, and hybrid models to face scheduling problem.