Search

The study of the interactions among the economic, environmental and social dimensions of sustainability in supply chain management research has received increased attention in recent years. However, for the specific case of the biodiesel supply chain, little attention has been given to the inclusion of social aspects in an integrated analysis of sustainability performance. This paper in turn proposes and develops a deterministic multi-objective linear programming model to analyze the relationship among the economic, environmental, and food security dimensions of the biodiesel supply chain (BSC) in Colombia. Considering four echelons from the supply chain (palm cultivation, oil extraction, biorefineries, and mixers), the model seeks to minimize total cost, impact on food security, and emissions of greenhouse gases, including emissions from direct and indirect land use change. The Epsilon-constraint method is used to solve the multi-objective model for the BSC. A Pareto set of optimal solutions helped to assess the trade-offs involving the three objectives. The relationship between costs and emissions is contingent upon impact on food security; this is, the design of the supply chain network involving substitution of palm for agricultural crops exhibits lower total cost and lower CO 2 emissions. The proposed generic mathematical model is applicable to any supply chain of agro-fuels and allows the calculation of production, inventory and distribution plans to better satisfy sustainability criteria. [ABSTRACT FROM AUTHOR]

Highlights • A stochastic optimization model for sugarcane supply chain planning is proposed. • The model integrates sowing, growing and harvesting operations. • Strategic and tactical decisions are simultaneously considered. • Financial feasibility for a new agro industrial project is assessed. Abstract The aim in new agricultural investment projects is to achieve a proper balance between customer value creation and investor economic benefits. Several authors have highlighted the importance of integrating agroindustrial supply chain operations, as a way to improve competitiveness. In the specific case of sugarcane, most research only integrates harvest and transport operations, lacking simultaneous analysis of sowing, growing and harvesting. Also, from the perspective of sugarcane supply chain planning, few contributions undertake tactical and strategic decisions. This paper proposes an optimization model for sugarcane supply chain planning, integrating several agricultural decisions from a strategic-tactical planning perspective. The uncertainty effects generated by weather conditions were also considered. As a main contribution, the model establishes a set of agricultural decisions that maximize cane yield, in order to optimize the Net Present Value (NPV) of expected profits. The proposed model is utilized to evaluate the feasibility of a new biofuel production plant in Colombia. Results allowed for the identification of critical variables to control, in order to reduce investment risk. [ABSTRACT FROM AUTHOR]

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]

• There is not enough literature on optimization in crop maintenance tasks. • The cost of waste in crop tasks can be reduced by optimization and lean principles. • This model is a first approach for integrating lean principles to agricultural tasks. • Response surface is used as tool for optimizing results of MINLP problem. The operational planning for agricultural production systems become an important tool for decision making for farmers. This paper shows a mathematical model as a decision support tool for crop maintenance planning problems. The model seeks the minimization of waste generated by the use of resources necessary for the task of crop maintenance. The model is inspired on the types of wastes from Lean Manufacturing (LM), which includes labor, use of machinery and operations in time windows that preserve the quality of harvest. The problem is modeled as a Mixed Integer Nonlinear Program (MINLP) in a case study on banana farms in Colombia. The model was tested with different scenarios, operations for crop maintaining, and number of workers. Then, we used the Response Surface Methodology (RSM) for sensitivity analysis, and obtain a significant reduction in cost, resources optimization, wastes, and schedule of crop maintenance activities: a reduction of 59% on the total cost was obtained, as well as a new approach for activities and task scheduling. The contributions of this paper to the body of knowledge in agricultural planning are at least twofold. Firstly, in contrast with traditional modeling approaches, this paper integrates the principles of lean manufacturing (LM) into the definition of parameters and constraints in a mathematical model for crop maintenance. Secondly, the paper couples nonlinear mathematical programming with response surface methodology (RSM) to carry out sensitivity analysis on the results obtained by the mathematical model. It is to note also that, although the modeling and solution methodology follow a case study research approach, it can be applied to other agricultural planning problem at operational level, being an economic option to minimize wastes and resources in others crops. [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]

• A cyber-physical intelligent agent was implemented for irrigation scheduling. • The irrigation agent was made up of the perception, actuation, reasoning and control systems. • Crop modelling was used for implementing the heuristic in the reasoning system. • The reasoning system estimated irrigation prescriptions with the best Water Use Efficiency. Irrigation is one of the most important activities in crop management. The aim of irrigation scheduling is to supply the correct amount of water to the crops and define appropriate irrigation times. The rational behavior of intelligent agents added to internet of things technologies could be a strategy for improving the performance of automatic irrigation systems. This paper presents the design and implementation of a cyber-physical crop irrigation system, based on the intelligent agent concept. The system allows the acquisition of field information through sensors, the application of water according to a decision system and the activation of solenoid valves. Communication capabilities and the performance measurement, the environment, the actuation and sensing systems (PEAS) are explained. Monitoring in the farm or remotely from other locations is achieved with a central station and its internet connectivity. The performance evaluation of the system was made using crop modelling software and an experimental crop in a field located in the large-scale irrigation and drainage district of Chicamocha and Firavitoba (Usochicamocha) in Boyacá – Colombia. The developed system allowed maintaining soil moisture close to field capacity with several irrigation strategies, and avoiding losses and excesses in the use of water resources. [ABSTRACT FROM AUTHOR]