An important contribution to the environmental impact of agro-food supply chains is related to the agricultural technology and practices used in the fields during raw material production. This problem can be framed from the point of view of the Focal Company (FC) as a raw material Green Supplier Selection Problem (GSSP). This paper describes an extension of the GSSP methodology that integrates life cycle assessment, environmental collaborations, and contract farming in order to gain social and environmental benefits. In this approach, risk and gains are shared by both parties, as well as information related to agricultural practices through which the FC can optimize global performance by deciding which suppliers to contract, capacity and which practices to use at each supplying field in order to optimize economic performance and environmental impact. The FC provides the knowledge and technology needed by the supplier to reach these objectives via a contract farming scheme. A case study is developed in order to illustrate and a step-by-step methodology is described. A multi-objective optimization strategy based on Genetic Algorithms linked to a MCDM approach to the solution selection step is proposed. Scenarios of optimization of the selection process are studied to demonstrate the potential improvement gains in performance.
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This study presents a methodology designed to optimize various parameters of each access point within a Multiple-Input Single-Output (MISO) Visible Light Communication (VLC) system. The primary objective is to enhance both power and spectral efficiencies. A MISO-VLC model is presented based on experimental evaluations and a problem formulation considering intermodulation distortions based on Orthogonal Frequency Division Multiplexing modulation. A Hybrid Multi-Objective Optimization (HMO) approach is proposed, combining the Non-Sorting Genetic Algorithm III (NSGA-III) and the Multi-objective Grey Wolf Optimization (MOGWO). The proposed HMO's success was validated by a 66 % reduction in transmitted power, maintaining the Error Vector Magnitude (EVM) performance metrics even at lower power transmission levels and minimizing the guard band to its lower bound.
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The the agriculture sector in developing countries has a large production share in the global fresh fruit market. Yet, in many cases, the land production yield indices at the orchard level are lower than the values related to more technologically developed countries. This situation leads to economic losses due to poor performance in productivity, efficiency and quality, which in turn is related to a technological and managerial gap. In this chapter, an operations management framework is proposed that tries to balance the market requirements (i.e. quality and quantity) with the capacity of the production system. This is performed through a multi-objective optimization approach that helps orchard managers synchronize the production yields with market demand and quality requirements. The model also allows the production managers to have a forecasting tool based on historical data. The model integrates the full supply chain through a set of sub-models for each stage of the production life cycle. The objective of the model is to minimize cost while maximizing sales. The optimization strategy involves a variant of the so-called NSGA II algorithm. The case study of an exporting lime packaging company is developed to illustrate the proposed framework and its possible impact on performance.
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