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This paper focuses on determining the raw milk collection routes with heterogeneous fleet aiming to minimize the average operational emissions of carbon dioxide. The problem is a particular case of the Vehicle Routing Problem (VRP) which considers the variation of emissions in terms of load and distance. Two types of the problem are studied, one in which the routes of the available fleet are optimized, and another which determines the configuration of the fleet and its routes. For solving the problems, an exact method based on a Branch and Bound formulation and a multi-start heuristic based on the Iterated Local Search (ILS) are implemented. Randomly generated instances were implemented inspired by the conditions of the Ubaté-Chiquinquirá dairy region in central Colombia, considered one of the five largest milk producers with the highest daily productivity. The instances have a combination of different factors which are: the location of the processing plant, the farms in clusters, and the number of farms. The analysis reveals that in terms of route design, an environmental approach can produce an increase in the distance traveled by vehicles (up to 60%) while reducing the overall emissions, and also the importance of replacing part of the large and polluting vehicles with smaller vehicles with lower emissions. The results may facilitate decision-making, provide insights for shifting to sustainable approaches in routing problems, and suggest future works. • Environmental criterion is considered in the design of routing schemes. • Routes change the fleet structure when considering environmental criteria. • Small vehicles are environmentally efficient but increase overall distances. • Real-life inspiration is considered for testing the solution approach. [ABSTRACT FROM AUTHOR]

• How and which quantitative methodologies based on Operations Research are implemented to solve problems in farms. • Response surface methodology is successfully used as optimization tool in decision making for agricultural production. • The lack of integration between the solution methods, the works only reach a real implementation near 17% of the cases. • The solutions are focused on planning problems in tactical and operational levels in farms. • Integrating lean to mathematical models for agricultural production contribute to the sustainability of food production. Due to the increase of global demand of agriculture and agro-industrial products, agriculture has undergone some changes in the stages of procurement, planning, planting, maintenance, harvesting, transport and distribution. The use of formal mathematical model has been employed to evaluate and implement improvement policies and strategies. In this paper, we present a Mixed Integer Nonlinear Programming Problem model to solve the planning of agricultural production systems in the stages of sowing, crop maintenance and harvesting. The objective function seeks to minimize the cost of resources and wastes generated in operations. The minimization is inspired by lean manufacturing wastes but newly applied in agricultural production. The proposed approach is validated using data for the planning of bananas farmers in the Caribbean region of Colombia. While a deterministic solution is obtained, sensitivity analyses are carried out to evaluate different scenarios for production based on surface response methodology. Model offers a reduction of cost near 59% of cost relationed with wastes of production, moreover, we can find an alternative for scheduling operations in agricultural production based in the most important resources and tasks required along of production in agriculture. [ABSTRACT FROM AUTHOR]

• An Intelligent IoT precision irrigation approach was implemented at farm and district scales. • The precision irrigation system was made up of the integration of intelligent agents, crop models, agent-based modeling and Internet of Things technologies. • The behavior of each irrigation agent was influenced by their own interests and neighbors. • The proposed approach is promising for the design of distributed systems in agriculture. The fourth industrial revolution in agriculture seeks the automation of traditional practices, using modern smart technologies. Advances in electronics, computation and the internet of things are integrated for improving field inputs management. The aim of this paper is to present the design and implementation of an intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems. The study site was the large-scale irrigation and drainage district of Chicamocha and Firavitoba (Usochicamocha) located in Boyacá - Colombia, where water is distributed from the Chicamocha riverbed. In the proposed system, irrigation is supervised and controlled in each field by an intelligent irrigation agent that autonomously prescribes and applies water amounts with agronomical criteria. The methodology was applied with real (cyber-physical) and virtual (simulated) intelligent agents and was extended to eleven pump stations that supply water to 5911 fields. Using a MQTT protocol, hundreds of irrigation intelligent agents report water prescriptions and crop characteristics to a master agent in each pump station, who creates a regional irrigation map to manage georeferenced field information and performs negotiation of water resources between agents according to supply availability. Field maps and intelligent irrigation agents can be visualized using devices with internet access. Results demonstrated that irrigation amounts were correctly applied on the fields, thus improving the water use efficiency. This technology is a novel support to decision-making in water resources management applications at field and district scales. [ABSTRACT FROM AUTHOR]