Abstract
In the contemporary industrial production, multiple resource constraints and uncertainty factors exist widely in the actual job shop. It is particularly important to make a reasonable scheduling scheme in workshop manufacturing. Traditional scheduling research focused on the one-time global optimization of production scheduling before the actual production. The dynamic scheduling problem of the workshop is getting more and more attention. This paper proposed a simulated annealing algorithm to solve the real-time scheduling problem of large variety and low-volume mixed model assembly line. This algorithm obtains three groups of optimal solutions and the optimal scheduling scheme of multiple products, with the shortest product completion time and the lowest cost. Finally, the feasibility and efficiency of the model are proved by the Matlab simulation.
Highlights
Mixed model assembly line is a flexible and cost-effective production system [1], but is always difficult for scheduling.e structure and process of productions are similar in the mixed model assembly line
We proposed an event-triggered simulated annealing (ETSA) method to deal with this issue and output the optimized changes of the scheduling plan
Complex, multiconstraint, and multiobjective characteristics of job-shop scheduling in multivariety and small-batch production enterprises, this paper proposes a dynamic job-shop scheduling model based on the simulated annealing algorithm, which can meet the characteristics of multivariety and small-batch production in enterprises
Summary
Mixed model assembly line is a flexible and cost-effective production system [1], but is always difficult for scheduling. To solve the scheduling problem of the hybrid model assembly line, different kinds of optimization algorithms have been proposed. Yow applied the genetic algorithm, for the first time, to solve production scheduling of the assembly line which overcomes the traditional optimization methods [7]. Dong and Kan proposed an improved particle swarm optimization algorithm to solve multiobjective mixed model assembly line scheduling problems. To overcome the infeasibility of above methods in real productions, various optimization algorithms have been studied, such as the fuzzy problem of shop scheduling, fast scheduling problem, multiobjective optimization problem of Mathematical Problems in Engineering assembly line production, and the robust scheduling of working time. Ye et al proposed an effective optimization method [10] to solve the flexible job shop scheduling problem with fuzzy processing time. An improved simulated annealing method was proposed for the fast scheduling problem of the hybrid assembly line [16]. We proposed an event-triggered simulated annealing (ETSA) method to deal with this issue and output the optimized changes of the scheduling plan
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