Resource assignment and scheduling based on a two-phase metaheuristic for cropping system

Guan, Senlin; Nakamura, Morikazu; Shikanai, Takeshi; Okazaki, Takeo

doi:10.1016/j.compag.2009.01.011

Cited by 41 publications

(20 citation statements)

References 18 publications

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“…Thus, Guan et al (2009) developed a very interesting approach for resource assignments and scheduling based on a two-phase metaheuristic for a long-term cropping schedule. The two-phase metaheuristic performs the optimization of resource assignments and scheduling based on a simulated annealing and genetic algorithm and a hybrid Petri net model (Guan et al 2009). These last approaches do not guarantee the optimality of the obtained solutions but they are promising, especially when combining their respective performances.…”

Section: Approaches For More Elaborated Formulationsmentioning

confidence: 99%

Model-based design of integrated production systems: a review

Ould-Sidi¹,

Lescourret²

2011

Agronomy Sust. Developm.

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Pest resistance and water pollution are major issues caused by the excessive use of pesticides in intensive agriculture. The concept of integrated production system (IPS) has been thus designed to solve those issues and also to meet the need for better food quality and production. Methodologies such as agronomic diagnosis-based design, prototyping, and model-based design have been developed.Here we review the model-based design of IPS. We identify tools for the development of comprehensive models. Once comprehensive models have been developed, model-based design of IPS can be formulated as an optimization problem to be solved using different approaches. Thus, we review the choice of corresponding criteria, constraints, and mathematical formulations found in the literature. We analyze successful model-based design in transportation and drug development. We learn from these areas to overcome the inherent difficulties involved in the modelbased design of IPS. We recommend the following major points: IPS model-based design should use integrative modeling platforms, process-oriented modeling, and object-oriented techniques to improve the genericity, modularity, reuse of crop models, and the data sharing. The spatial dimension of IPS has to be accounted for by making crop models spatially explicit. The design evaluation criteria have to be standardized and carefully chosen by promoting stakeholders involvement. The design of IPS should be formulated as a multiobjective optimization problem, for which non-aggregative approaches should be preferred. Finally, it is necessary to establish a true dialogue between prototyping and model-based design practitioners in order to test, and if necessary to improve, the proposed systems.

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Section: Approaches For More Elaborated Formulationsmentioning

confidence: 99%

Model-based design of integrated production systems: a review

Ould-Sidi¹,

Lescourret²

2011

Agronomy Sust. Developm.

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show abstract

“…Recently more advanced meta-heuristics have been developed that can provide more optimal solutions and utilize other optimization functions. Long-term scheduling of agricultural field work has been optimized using a two-phase metaheuristic based on simulated annealing, genetic algorithms and hybrid Petri nets (Guan et al, 2009). Unfortunately, the most popular metaheuristics, such as neural networks or genetic algorithms, are not efficient at exploring the solution space posed by the VRP (Toth and Vigo, 2002).…”

Section: Introductionmentioning

confidence: 99%

Using the Vehicle Routing Problem to reduce field completion times with multiple machines

Seyyedhasani

Dvorak

2017

Computers and Electronics in Agriculture

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“…The model, which is a hybrid Petri nets first introduced into agricultural production (Guan et al, 2008), graphically formulates the farm work flow and simulates the overall status of the progress of farm work and the state of resources. The scheduling system performs the resource assignment and the computation for a long-term schedule (Guan et al, 2009). A part of contents and experiment data in this chapter are originally published in these two articles.…”

Section: Introductionmentioning

confidence: 99%