Precedence constrained scheduling to minimize sum of weighted completion times on a single machine
Discrete Applied Mathematics
Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks
Discrete Event Dynamic Systems
An integrated Petri net and GA based approach for scheduling of hybrid plants
Computers in Industry
A simulation model for capacity planning in sugarcane transport
Computers and Electronics in Agriculture
Resource assignment and scheduling based on a two-phase metaheuristic for cropping system
Computers and Electronics in Agriculture
An object-oriented model for simulating agricultural in-field machinery activities
Computers and Electronics in Agriculture
HPN modeling, optimization and control law extraction for continuous steel processing
Proceedings of the Winter Simulation Conference
A flow-shop problem formulation of biomass handling operations scheduling
Computers and Electronics in Agriculture
Scheduling for machinery fleets in biomass multiple-field operations
Computers and Electronics in Agriculture
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This paper introduces hybrid Petri nets into modeling for farm work flow in agricultural production. The main emphasis is on the construction of an adequate model for designing practical farm work planning for agriculture production corporations. Hybrid Petri nets conventionally comprise a continuous part and a discrete part. The continuous part mainly models the practical work in the farmland, and the discrete part mainly represents the status changes in resources such as machinery and labor. The proposed model also models the present status or undesirable breaks during the farming process. Moreover, in this paper, the approach of formulating the farm work planning problem based on the model is suggested. The simulated results reveal that the hybrid Petri nets model is promising for exactly describing the farming process and reallocating resources in the presence of uncertainties. The proposed model serves as a referential model for farm work planning and it promotes the development of a corresponding optimization algorithm under uncertain environments.