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Scheduling of machines and AGVs in Flexible Manufacturing Systems involves modeling and searching methodology in a wide solution space. In this work the search for scheduling occurs by genetic algorithm and simple AGV dispatching rules. Modeling occurs in Timed Petri nets in time of fitness evaluation, considering the input buffers of machines, AGVs, and flags also control the use of these buffers,...
Most of papers that deal with the production planning problems assume that the equipments are always available during the scheduling horizon, however, preventive maintenance activities cost time that could otherwise be used for production, but delaying preventive maintenance for production may increase the probability of equipment failure. In order to resolve the conflict between preventive maintenance...
Most of papers on integrating scheduling problems with preventive maintenance have focus on a signal production line or preventive maintenance that can restore a production line to a 'as-good-as-new' status, which is depart from the real production, limiting its use in practice. This paper deals with the problem of jointly scheduling and imperfect preventive maintenance which can not restore a production...
Considering the flexible job shop scheduling problem (FJSSP) more accorded with practice, a correspondent model is established and the adaptive genetic algorithm is used to solve it. According to the features of the model (machines are optional), three factors: the processing time, the completion time of previous operation and the idle time of current machine are synthetically considered for choosing...
In this paper, minimizing machine idle time and minimizing earliness-tardiness penalties are considered as two objectives in advanced planning and scheduling (APS). The APS problem is formulated as a mixed integer programming model. Constraints including precedence, alternative machine, capacity, and setup and transition times are taken into account. A preference-based adaptive genetic algorithm is...
The problem of production scheduling of manufacturing systems is characterized by the large number of possible solutions. Several researches have been using the Genetic Algorithms (GA) as a search method to solve this problem since these algorithms have the capacity of globally exploring the search space and find good solutions quickly. Since the performance of the GA is directly related to the choice...
Adaptive genetic algorithm for solving job-shop scheduling problems has the defects of the slow convergence speed on the early stage and it is easy to trap into local optimal solutions, this paper introduces a time operator depending on the time evolution to solve this problem. Its purpose is to overcome the defect of adaptive genetic algorithm whose crossover and mutation probability can not make...
The problem of production scheduling of manufacturing systems is a typical NP-hard optimization problem and several researchers have been using the genetic algorithms (GAs) as a search method, since these algorithms have the capacity of globally exploring the search space. However, it is reported that traditional GAs often suffers from the weaknesses of premature convergence as well as parameter and...
The problem for scheduling the manufacturing systems production involves the system modeling task and the application of a technique to solve it. There are several ways used to model the scheduling problem and search strategies have been applied on the models to find a solution. The solutions consider performance parameters like makespan. However, depending on the size and complexity of the system,...
In order to solve the feeble adaptability and the imbalance between random search and local search in the job-shop scheduling problem, a new adaptive genetic algorithm (AGA) was presented in this paper. The superiority of this algorithm was the adaptation achieved by adjusting the crossover rate and mutation rate. At the same time, the search property has been balanced by restricting crossover and...
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