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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Airports and surrounding airspaces are limited in terms of capacity and represent the major bottlenecks of the air traffic management system. This paper addresses the problems of terminal airspace management and airport congestion management at the macroscopic level through the integrated control of arrivals and departures. Conflict detection and resolution methods are applied to a predefined terminal route structure. Different airside components are modeled using network abstraction. Speed, arrival and departure times, and runway assignment are managed by using an optimization method. An adapted simulated annealing heuristic combined with a time decomposition approach is proposed to solve the corresponding problem. Computational experiments performed on case studies of Paris Charles De-Gaulle airport show some potential improvements: First, when the airport capacity is decreased, until a certain threshold, the overload can be mitigated properly by adjusting the aircraft entry time in the Terminal Maneuvering Area and the pushback time. Second, landing and take-off runway assignments in peak hours with imbalanced runway throughputs can significantly reduce flight delays. A decrease of 37% arrival delays and 36% departure delays was reached compared to baseline case.
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Food security depends on a network of actors and elements working together to produce and deliver healthy, sustainable, varied, safe and plentiful food supply to society. The interactions between these actors and elements must be designed, managed and optimized to satisfy demand. In this chapter we introduce Food Supply Chain Optimization and Demand, providing a framework to understand and improve food security from an operational and strategic point of view.
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Presenting techniques, case-studies and methodologies that combine the use of simulation approaches with optimization techniques for facing problems in manufacturing, logistics, or aeronautical problems, this book provides solutions to common industrial problems in several fields, which range from manufacturing to aviation problems, where the common denominator is the combination of simulation’s flexibility with optimization techniques’ robustness.
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"We must know whether we want to change the world to experience it with the same sensorial system like the one we already possess, or whether we’d rather modify our body, the somatic filter through which it passes."
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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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Aircraft require significant quantities of fuel in order to generate the power required to sustain a flight. Burning this fuel causes the release of polluting particles to the atmosphere and constitutes a direct cost attributed to fuel consumption. The optimization of various aircraft operations in different flight phases such as cruise and descent, as well as terminal area movements, have been identified as a way to reduce fuel requirements, thus reducing pollution. The goal of this chapter is to briefly explain and apply different metaheuristic optimization algorithms to improve the cruise flight phase cost in terms of fuel burn. Another goal is to present an overview of the most popular commercial aircraft models. The algorithms implemented for different optimization strategies are genetic algorithms, the artificial bee colony, and the ant colony algorithm. The fuel burn aircraft model used here is in the form of a Performance Database. A methodology to create this model using a Level D aircraft research flight simulator is briefly explained. Weather plays an important role in flight optimization, and so this work explains a method for incorporating open source weather. The results obtained for the optimization algorithms show that every optimization algorithm was able to reduce the flight consumption, thereby reducing the pollution emissions and contributing to airlines’ profit margins.
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For long flights, the cruise is the longest phase and where the largest amount of fuel is consumed. An in-cruise optimization method has been implemented to calculate the optimal trajectory that reduces the flight cost. A three-dimensional grid has been created, coupling lateral navigation and vertical navigation profiles. With a dynamic analysis of the wind, the aircraft can perform a horizontal deviation or change altitudes via step climbs to reduce fuel consumption. As the number of waypoints and possible step climbs is increased, the number of flight trajectories increases exponentially; thus, a genetic algorithm has been implemented to reduce the total number of calculated trajectories compared to an exhaustive search. The aircraft’s model has been obtained from a performance database, which is currently used in the commercial flight management system studied in this paper. A 5% average flight cost reduction has been obtained.
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Background: Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships. Methods: Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship. Results: This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis. Conclusions: This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.
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The constant growth of air traffic, especially in Europe, is putting pressure on airports, which, in turn, are suffering congestion problems. The airspace surrounding airport, terminal manoeuvring area (TMA), is particularly congested, since it accommodates all the converging traffic to and from airports. Besides airspace, airport ground capacity is also facing congestion problems, as the inefficiencies coming from airspace operations are transferred to airport ground and vice versa. The main consequences of congestion at airport airspace and ground, is given by the amount of delay generated, which is, in turn, transferred to other airports within the network. Congestion problems affect also the workload of air traffic controllers that need to handle this big amount of traffic.This thesis deals with the optimization of the integrated airport operations, considering the airport from a holistic point of view, by including operations such as airspace and ground together. Unlike other studies in this field of research, this thesis contributes by supporting the decisions of air traffic controllers regarding aircraft sequencing and by mitigating congestion on the airport ground area. The airport ground operations and airspace operations can be tackled with two different levels of abstractions, macroscopic or microscopic, based on the time-frame for decision-making purposes. In this thesis, the airport operations are modeled at a macroscopic level.The problem is formulated as an optimization model by identifying an objective function that considers the amount of conflicts in the airspace and capacity overload on the airport ground; constraints given by regulations on separation minima between consecutive aircraft in the airspace and on the runway; decision variables related to aircraft entry time and entry speed in the airspace, landing runway and departing runway choice and pushback time. The optimization model is solved by implementing a sliding window approach and an adapted version of the metaheuristic simulated annealing. Uncertainty is included in the operations by developing a simulation model and by including stochastic variables that represent the most significant sources of uncertainty when considering operations at a macroscopic level, such as deviation from the entry time in the airspace, deviation in the average taxi time and deviation in the pushback time. In this thesis, optimization and simulation techniques are combined together by developing two methods that aim at improving the solution robustness and feasibility. The first method acts as a validation tool for the optimized solution, and it improves the robustness of solution by iteratively fine-tuning some of the optimization model input parameters. The second method embeds the optimization in a simulation environment by taking full advantage of the sliding window approach and creating a loop for a continuous improvement of the optimized solution at each window of the sliding window approach. Both methods prove to be effective by improving the performance, lowering the total amount of conflicts up to 23.33% for the first method and up to 11.2% for the second method, however, in contrast to the deterministic method, the two methods they are not able to achieve a conflict-free scenario due to the effect of uncertainty.In general, the research conducted in this thesis highlights that uncertainty is a factor that affects to a large extent the feasibility of optimized solution when applied to real-world instances, and it, moreover, confirms that using simulation together with optimization has the potentiality toivdeal with uncertainty. The framework developed can be potentially applied to similar problems and different optimization solving methods can be adapted to it.Keywords: Optimization, Simulation, Integrated airport operations, Uncertainty
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