Cloud computing is a new paradigm that opens a flexible and financial attractive door for business
and consumers. In recent years, cloud services have reshaped the way business models are planned,
saving costs and improving efficiency. There is an exponential growth of cloud solutions available
in the public and private sector. This way, cloud services are based on a pay-per-use model,
offering resources and service through the Internet. Moreover, the cloud architectures provide many
advantages concerning scalability, maintainability and massive data processing. High Performance
Computing (HPC) decomposition methods and Operation Research (OR) models integration inside the
cloud brings the opportunity to improve the performance and the quality of the cloud services.
There are infinite opportunities and challenges for exploiting the capabilities of Decision Support
Systems (DSS) inside the cloud. The agribusiness sector can significantly be boost by the
consideration of these possibilities.
Queuing theory and nonlinear programming were combined to model cloud architectures and to develop
trade-off policies considering different metrics and ensuring a certain level of quality of service.
OR decomposition methods (Benders Decomposition and Lagrangian Decomposition) are improved using HPC
paradigms. Cloud services, OR methods and decision support systems are integrated to deliver
knowledge from academia to society. Two-stage sStochastic programming was selected to enhance
the decision-making process in the agribusiness field. Finally, two different agribusiness
applications were designed, deployed and tested on a real cloud platform..
This thesis explores methods, mathematical models and algorithms for managing cloud systems
cost-effectively. Furthermore, this thesis analyses how to export and deliver this knowledge to
society; in particular to the agribusiness field. The primary purpose is to assist decision-makers
in their daily activity. The main results of this thesis highlight the future guidelines to build
smart decision support systems combining HPC, OR and cloud platforms. The outcomes corroborate the
benefits of applying parallel computing to decomposition methods to deal with stochastic models.
Moreover, the results show the advantages of mixing cloud platforms and optimisation field offering
optimisation as a service to the overall community. Furthermore, the results demonstrate that
considering uncertainty and stochastic programming costs can be reduced in agribusiness supply
chains. Besides, the results show that cloud DSS's improve the decision-making process, overcoming
the current barriers. It presents a cloud application to assist pig farmers in the optimal
replacement of sows.
Several cloud models and policies were analysed and implemented using different methods and techniques.
The results yielded by this thesis provide strong evidence that parallel decomposition methods,
such as Lagrangian Decomposition and Benders Decomposition can beat commercial solvers such as CPLEX
when dealing with the optimal resolution of real-life stochastic instances. SPOS shows how to
orchestrate cloud platforms to deliver knowledge as a service while integrating mathematical solvers
in a cloud environment. In a more agroindustrial context, the stochastic model designed around the
Chilean dried apple supply chain proof a cost reduction by considering uncertain up to a 6.4%.
Finally, a cloud DSS was successfully developed and implemented to assist sow farmers. All the
results extracted from this thesis are an essential seed for modelling a much bigger service,
with great potential to become a reference in the design of cloud DSS offering all users the
possibility of solving any problem, even stochastic models, and also of obtaining detailed
and custom results.