L. M. Plà

A Markov decision sow model representing the productive lifespan of herd sows

Abstract A Markov decision sow model has been developed to represent the productive and reproductive lifespan of herd sows. This model precisely describes the herd structure at equilibrium based on actual farm data. Model outputs are the herd structure at equilibrium, and technical and economic indexes. Validation has been performed by comparing observed and simulated outputs from specific farm data. A complementary validation using a statistical χ 2 test based on Pearsons statistic is proposed to compare herd distributions at equilibrium. The model is intended to be used by farmers and runs on micro computers.

A perspective on operational research prospects for agriculture

This paper discusses the future of operational research (OR) for the agricultural industries in a broad sense, including horticulture and viticulture during a period of increased pressure on natural resources. The authors use their experience in the field along with published literature, to draw insights into new opportunities for OR, and how the OR community might adapt to realise these opportunities best. Trends in demand for food security and biofuels, the quest for sustainability, information technology (IT), and commercial power create new opportunities to support strategic investment and operations management within both primary production and the related supply chains. To realise such potential, the agricultural OR community needs to improve management of stakeholder relations, interdisciplinary synthesis, and the successful application of OR.

New opportunities in operations research to improve pork supply chain efficiency

The structure of the pork sector in world economy is changing. In many countries the number of pig farms is being reduced, while the herd size of the remaining ones is increasing. Pig production process is partitioned into different phases with specialized farms devoted to piglet production, rearing or fattening pigs are common instead of old farrowing-to-fattening farms. Pig farms have tended to be integrated and coordinate their operations into pork supply chains by using tighter vertical coordination linkages. This paper presents a description of the pork supply chain, stressing the role of pig farming as one of the key issues to improve pork supply chain efficiency. A survey of literature to support the decision making on the pork sector has revealed that most papers had only considered individual farm operations, while the pork supply chain management involves the coordination of sets of farm units at different stages of production. Thus, our contribution emphasizes the importance and complexity of new decision-making tasks regarding the modern organization of the pork sector. All these elements make it possible to envisage new opportunities for operations research methods to be successfully applied to the pork supply chain. Likewise, we have identified some existing gaps in the literature that we believe should be addressed in the near future.

A linear programming formulation of a semi-Markov model to design pig facilities

Housing facilities design represents the main strategic decision in pig farms. This paper introduces a linear programming formulation of a semi-Markov process to approach the facilities design. Thus, the Linear Programming formulation determines the optimum replacement policy and provides the equilibrium distribution of the herd along pig facilities. Then, the calculation of the associated needs of room for each sow facility is derived from sow herd distribution at equilibrium. Results show the flexibility of the model for designing pig facilities and computational advantages in the solving procedure compared to previous proposals. Furthermore, the robustness of the optimal solution is studied by means of sensitivity analysis.

Forecasting for some stochastic process models related to sow farm management

Abstract Sow farm management requires appropriate methods to forecast the sow population structure evolution. We describe two models for such purpose. The first is a semi-Markov process model, used for long-term predictions and strategic management. The second is a state-space model for continuous proportions, used for short-term predictions and operational management.

A Green Strategy for Federated and Heterogeneous Clouds with Communicating Workloads

Providers of cloud environments must tackle the challenge of configuring their system to provide maximal performance while minimizing the cost of resources used. However, at the same time, they must guarantee an SLA (service-level agreement) to the users. The SLA is usually associated with a certain level of QoS (quality of service). As response time is perhaps the most widely used QoS metric, it was also the one chosen in this work. This paper presents a green strategy (GS) model for heterogeneous cloud systems. We provide a solution for heterogeneous job-communicating tasks and heterogeneous VMs that make up the nodes of the cloud. In addition to guaranteeing the SLA, the main goal is to optimize energy savings. The solution results in an equation that must be solved by a solver with nonlinear capabilities. The results obtained from modelling the policies to be executed by a solver demonstrate the applicability of our proposal for saving energy and guaranteeing the SLA.

Proceedings of the Second Meeting of the EURO Working group on Operational Research (OR) in Agriculture and Forest Management

This working group, which is concerned with operational research methods and applications to agricultural science in its broad meaning (i.e. including Forest Management and Fisheries), was formed in 2003 within the European Association of Operational Research Societies (EURO). The first meeting of the group was held at the former Silsoe Research Institute two years ago. The next meeting will be held in 2007 within the XXII EURO Conference in Prague. The group intends to start regular meetings at approximately yearly intervals in association with the EURO Conferences. The second meeting of the working group, chaired by Dr. L. M. Pla of the University of Lleida and organized as a stream within the XXI EURO Conference, was held at the University of Iceland in Reykjavik from 3rd–5th July 2006 where the following papers were read.

A production planning model considering uncertain demand using two-stage stochastic programming in a fresh vegetable supply chain context

Abstract Production planning models are achieving more interest for being used in the primary sector of the economy. The proposed model relies on the formulation of a location model representing a set of farms susceptible of being selected by a grocery shop brand to supply local fresh products under seasonal contracts. The main aim is to minimize overall procurement costs and meet future demand. This kind of problem is rather common in fresh vegetable supply chains where producers are located in proximity either to processing plants or retailers. The proposed two-stage stochastic model determines which suppliers should be selected for production contracts to ensure high quality products and minimal time from farm-to-table. Moreover, Lagrangian relaxation and parallel computing algorithms are proposed to solve these instances efficiently in a reasonable computational time. The results obtained show computational gains from our algorithmic proposals in front of the usage of plain CPLEX solver. Furthermore, the results ensure the competitive advantages of using the proposed model by purchase managers in the fresh vegetables industry.