In this paper, we present a constraint Programming (CP) based model for scheduling forage harvesters and transport vehicles during corn harvest. The key aspects are the synchronization of the two resource types and a ...
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ISBN:
(纸本)9783030876722;9783030876715
In this paper, we present a constraint Programming (CP) based model for scheduling forage harvesters and transport vehicles during corn harvest. The key aspects are the synchronization of the two resource types and a forage harvester utilization depending on the number of transport vehicles supporting the harvester. The process is modelled as a pre-emptive multi-mode resource-constraint project scheduling problem with fast-tracking, sequence-dependent time lags and synchronization. We use the specialized scheduling features of CP Optimizer for modelling and solving the harvest logistics problem. The results show the suitability of the CP-based approach for modelling the problem in terms of representability of its characteristics. In computational experiments, a solution is found for any of the test instances. Proving optimality, however, is found to be difficult, especially for larger instances. Further variants of the model without pre-emption and fast-tracking and with fewer modes per activity are introduced and tested, showing improvements in computation time and the number of optimal solutions found for the prior variant.
In this paper, a new optimisation method based on chaotic differential evolution (CDE) algorithm for solving resource-constraint project scheduling problem (RCPSP) is proposed. By using improved logistic chaotic map a...
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In this paper, a new optimisation method based on chaotic differential evolution (CDE) algorithm for solving resource-constraint project scheduling problem (RCPSP) is proposed. By using improved logistic chaotic map and penalty function, the objective function can be solved more perfectly. projectscheduling problem library (PSPLIB) has been taken into account so as to test the effect of novel hybrid method. Simulation results and comparisons show that the chaotic differential evolution algorithm is competitive and stable in performance with other optimisation approaches presented in literatures.
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