Job shop scheduling by simulated annealing
Operations Research
Precedence constrained scheduling to minimize sum of weighted completion times on a single machine
Discrete Applied Mathematics
Genetic Algorithms: Concepts and Designs with Disk
Genetic Algorithms: Concepts and Designs with Disk
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Discrete Event Dynamic Systems
Computers and Operations Research
An integrated Petri net and GA based approach for scheduling of hybrid plants
Computers in Industry
Hybrid Petri nets modeling for farm work flow
Computers and Electronics in Agriculture
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
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
Determination of the locations and capacities of sugar cane loading stations in Thailand
Computers and Industrial Engineering
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This paper proposes a resource assignment and scheduling based on a two-phase metaheuristic for a long-term cropping schedule. The two-phase metaheuristic performs the optimization of resources assignment and scheduling based on a simulated annealing (SA), a genetic algorithm (GA) and a hybrid Petri nets model. The initial and progressive states of farmlands and resources, moving sequence of machinery, cooperative work, and deadlock removal have been well handled in the proposed approach. In the computational experiment, the schemes of emphasizing the resource assignment optimization, initializing the population of the GA with chromosomes sorted by the waiting time, and inheriting the priority list from tasks in the previous resources assignment improved the evolution speed and solution quality. The simulated result indicated that the formulated schedule has a high ratio of resource utilization in sugarcane production. The proposed approach also contributes a referential scheme for applying the metaheuristic approach to other crop production scheduling.