Application of genetic algorithm to industrial scheduling and problems of parameters evaluation - Šakurovas, Edgaras; Listopadskis, Narimantas (Lietuvos matematikos rinkinys, 2021)
Genetic algorithms are widely used in various mathematical and real world problems. They are approximate metaheuristic algorithms, commonly used for solving NP-hard problems in combinatorial optimisation. Industrial scheduling is one of the classical NP-hard problems. We analyze three classical industrial scheduling problems: job-shop, flow-shop and open-shop. Canonical genetic algorithm is applied for those problems varying its parameters. We analyze some aspects of parameters such as selecting optimal parameters of algorithm, influence on algorithm performance. Finally, three strategies of algorithm – combination of parameters and new conceptualmodel of genetic algorithm are proposed.
Keywords: scheduling problem; genetic algorithm; job shop; open shop; flow shop