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This paper aims to design the best and versatile solution to job-shop scheduling problem (JSP). For this purpose, the ant colonly algorithm (ACA) was integrated to the shortest route optimization of the JSP, and a strategy was developed to solve the shortest scheduling route with the improved ACA (IACA). The proposed strategy was verified through case analysis and simulation experiment. The results show that the ACA is suitable to optimize the scheduling route of real-world JSP. With the increase of the pheromone residual coefficient, the route length of the ACA first increased and then decreased. The IACA worked out a better solution than the genetic algorithm with fewer iterations. The IACA is more adaptable and versatile than the genetic algorithm in shortest route optimization, as well as the IACA’s relative advantage in the global optimization ability for JSP. The research findings shed new light on the optimization of dynamic JSP with multiple objectives.
Job-shop scheduling problem (JSP), shortest route optimization, ant colony algorithm (ACA), simulation, number of iterations
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