Abstract
This study deals with the problem of sequencing feeding tasks of a single mobile robot which is able to provide parts for feeders of machines on production lines. The mobile robot has to be scheduled in order to stoppage from lack of parts in the production line. A method based on the characteristics of feeders and inspired by the (\(s,Q\)) inventory system, is thus applied to define time windows for the feeding tasks of the robot. The capacity of the robot is also taken into consideration. The performance criterion is to minimize total traveling time of the robot for a given planning horizon. A genetic algorithm-based heuristics is presented which results in a significant increase in the speed of finding near-optimal solutions. To evaluate the performance of the genetic algorithm-based heuristic, a mixed-integer programming model has been developed for the problem. A case study is implemented at an impeller production line in a real factory and computational experiments are also conducted to demonstrate the effectiveness of the proposed approach.
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This work has partly been supported by the European Commission under grant agreement number FP7-260026-TAPAS.
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Appendix
Appendix
The average and standard deviation of the objective value is the best when \(P_{c}\) is equal to 0.8 or 1.0. However, only slight improvement on the objective value is achieved with longer computation time when \(P_{c}\) increases from 0.8 to 1.0. The value of \(P_{c}\) is hence set to be 0.8.
There are improvements on the average of the objective value when \(P_{m}\) increases from 0.05 to 0.2. However, these improvements are minor in comparison with the increase of the computation time. Furthermore, the standard deviations of the objective value at \(P_{m}\) of 0.1 and 0.2 are better than the other values of \(P_{m}\). The value of \(P_{m}\) is hence set to be 0.1.
Similar to the case of \(N_{p}\), the value of \(G_{c}\) is set to be 100.
There are considerable improvements on the average of the objective value when \(CT_{m}\) increases from 15 to 60 s (the best average is achieved at \(CT_{m}\) of 60). Furthermore, the standard deviation of the objective value at \(CT_{m}\) of 60 is better than the other values of \(CT_{m}\) except the case of 30. Note that the value of \(CT_{m}\) is set based on the improvements on the objective value because the ANOVA of \(CT_{m}\) shows that this parameter strongly affects the objective value (\(F = 22.560 > 2.866\)). The value of \(CT_{m}\) is hence set to be 60 (a problem instance of larger size is randomly generated to test \(CT_{m}\)).
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Dang, QV., Nielsen, I., Steger-Jensen, K. et al. Scheduling a single mobile robot for part-feeding tasks of production lines. J Intell Manuf 25, 1271–1287 (2014). https://doi.org/10.1007/s10845-013-0729-y
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DOI: https://doi.org/10.1007/s10845-013-0729-y