Functional optimization of a Persian lime packing using TRIZ and multi-objective genetic algorithms

This article proposes a novel approach that uses a mathematical model optimized by Genetic Algorithms harmonized with the Russian theory of problem solving and invention (TRIZ) to design an export packing of Persian Lime. The mathematical model (with functional elements of non-spatial type) optimizes the spaces of the Persian Lime Packing, maximizes the Resistance to Vertical Compression and minimizes the Amount of Material Used, according to the operation restrictions of the packing during the transport of the merchandise. This approach is developed in four phases: the identification of the solution space; the optimization of the conceptual design; the application of TRIZ; and the generation of the final proposal solution. The results show the proposed packing (with 28% less cardboard) supports at least the same vertical load with respect to the nearest competitor packing. However, with the same number of packings per pallet and pallets per container, the space used by the packing assembled and deployed in the container is greater by 10% and 38% respectively. Besides, TRIZ includes innovative non-spatial elements such as the airflow and the friction of the product inside the packing. The contribution of this approach can be replicable for the packing design of other horticultural products of the agri-food chai

Ecodesign of photovoltaic grid-connected systems

Optimization approaches for PV grid-connected system (PVGCS) have focused on optimizing the technical and economic performances. The main objective of this study is thus to propose an integrated framework that manages simultaneously technical, economic and environmental criteria. Life Cycle Assessment (LCA) is applied for the evaluation of environmental impacts of PVGCS. The proposed framework involves a PVGCS sizing simulator involving the computation of solar irradiance coupled to an outer optimization loop, based on a Genetic Algorithm. The objective is to maximize the annual energy generated by the facility. The analysis was carried out for different types of solar panel technologies: monocrystalline silicon (m-Si), polycrystalline silicon (p-Si), amorphous silicon (a-Si), cadmium telluride (CdTe) and copper indium diselenide (CIS). The environmental impact assessment was achieved by use of the IMPACT 2002+ method embedded in the SimaPro software tool with Ecoinvent database. The other chosen criteria based on technical and economic aspects concern the payback time of investment (PBT) and energy payback time (EPBT). To select the best option among the five choices under study, a weighted evaluation is performed on all criteria in order to obtain a score for each technology. The technology with the lowest total score is the a-Si technology. A more relevant analysis is then performed taking into account the environmental impacts per kWh produced, as new criteria. In this case, the CIS PV module technology best meets the objectives

Multiple software product lines to configure applications of internet of things

Software product lines (SPL) emulate the industrial production lines that are capable of generating large volumes of products through reuse schemes and mass production. A multi product line (MPL) aims to reuse of several SPL. Feature models are often used to manage the existing resources of SPLs and define valid products through notations and relationships such as mandatory, optional, and alternative features. The main contribution of this study is a method to manage the variability of multiple SPL and generate a new portfolio of products for Internet of Things (IoT). For this, the problem of developing a universal feature model (FM) for an MPL from merging the FMs of the individual SPLs with a Search-Based Software Engineering (SBSE) technique is addressed. In addition, the authors propose a multi-objective optimisation model to maximise the reusability and compatibility between features and minimise the development cost. The model facilitates the design of an MPL-feature model. Authors' empirical results show that the proposed model solved by genetic algorithms allows to configure a variety of software products and to determine the scope of the MPL

An expert system for predicting orchard yield and fruit quality and its impact on the Persian lime supply chain

In recent years academics and industrials have shown an interest in agricultural systems and their complex and non-linear nature, aiming to improve production yield in the agricultural field. Innovative strategies and methodological frameworks are thus required to assist farmers in decision making for an efficient and effective resource management. In particular, this research concerns the structural problem of the Persian lime supply chain in Mexico, which still leads to low production yield over short time periods with heterogeneous fruit quality and also to the emergence of excessive middleman businesses arising from a fragmentation between orchard and exporting companies that constitute the first two links in the associated supply chain. Based on the Persian lime production cycle, an Expert System (ES) using Fuzzy Logic involving an inference engine with IF—THEN type rules is presented in this paper. A Mamdani model codifies the decision criteria related to agricultural practices for growing Persian lime in non-irrigated orchards. The ES allows the farmer to boost production in orchards by modeling application scenarios for agricultural practices. A case study based on an exporting company׳s fruit supply is discussed, in which the ES proves to be a useful tool to aid the decision making involved in the application of agricultural practices in the orchard. Results show an increase in production yield and fruit quality in the orchard, as well as a better synchronization between orchard and exporting companies, with a significant impact on inventory levels of fresh fruit in the link Persian lime exporting company

Environmental impact assessment of chicken meat production via an integrated methodology based on LCA, simulation and genetic algorithms

This study performed a Life Cycle Assessment (LCA) to evaluate the environmental impact of chicken meat production from a Mexican case study, with a “cradle-to-slaughterhouse gate” approach. To overcome the LCA's limitations and provide a more holistic picture of the system, simulation and artificial intelligence techniques were integrated. First, raw material/energy requirements were obtained from the case study and simulated using Process simulation (PS) and Monte Carlo (MC) simulation to estimate the emissions and quantify their uncertainty. Then, IMPACT 2002 + was used to calculate the overall impact using Ecoinvent and LCA Food databases. The results highlight that chicken farms are the main factors responsible for the environmental impacts assessed, where feed production (use of chemicals and energy requirements) and on-farm emissions (organic waste decomposition) are the main contributors. Concerning the slaughterhouse, the energy production (electricity and steam) and the cooling-related activities present a significant impact. Afterwards, three impact allocation procedures (mass method, neural networks, and stepwise regression) were tested, showing similar results. Finally, a multiobjective optimization model based on a Genetic Algorithm was applied looking to minimize the environmental impacts and maximize the economic benefits. The selected alternative achieved a reduction of 15.14% per functional unit at the environmental indicators. The results encourage the use of support techniques for LCA to perform a reliable assessment and an environmental/economic optimization of the system

Intelligent system based on a satellite image detection algorithm and a fuzzy model for evaluating sugarcane crop quality by predicting uncertain climatic parameters

The increase in uncertain weather affects agriculture, impacting crop yield and quality, mainly due to the interaction of climatic variables such as temperature, wind speed, and humidity. In addition, soil erosion and nutrient loss are regional problems aggravated by inadequate agricultural practices in developing sugarcane agriculture. The present research proposes an Intelligent System based on a detection algorithm and a fuzzy model to estimate the quality of the sugarcane crop and the probability of the presence of pests and diseases through the prediction of uncertain variables. Wind speed, cloudiness, humidity, and thermal amplitude were considered variables of interest because parameters out of control of these variables generate a state of thermal stress, triggering pests and diseases that reduce crop quality and sugar production. This research uses geospatial information to simplify the exchange of information through a detection algorithm using real-time satellite images and a fuzzy model to estimate crop quality and prevent climate change-related problems. The variables humidity and cloudiness determine sugarcane quality as they are related to crop phenology and the probability that the crop will develop pests and diseases. In contrast, the intelligent system showed a correlation of over 93% for predicting the variables of interest

Expert System for Competences Evaluation 360° Feedback Using Fuzzy Logic

Performance evaluation (PE) is a process that estimates the employee overall performance during a given period, and it is a common function carried out inside modern companies. PE is important because it is an instrument that encourages employees, organizational areas, and the whole company to have an appropriate behavior and continuous improvement. In addition, PE is useful in decision making about personnel allocation, productivity bonuses, incentives, promotions, disciplinary measures, and dismissals. There are many performance evaluation methods; however, none is universal and common to all companies. This paper proposes an expert performance evaluation system based on a fuzzy logic model, with competences 360° feedback oriented to human behavior. This model uses linguistic labels and adjustable numerical values to represent ambiguous concepts, such as imprecision and subjectivity. The model was validated in the administrative department of a real Mexican manufacturing company, where final results and conclusions show the fuzzy logic method advantages in comparison with traditional 360° performance evaluation methodologies

Functional Evaluation Using Fuzzy FMEA for a Non-Invasive Measurer for Methane and Carbone Dioxide

This paper combines the use of two tools: Failure Mode and Effect Analysis (FMEA) and Fuzzy Logic (FL), to evaluate the functionality of a quantifier prototype of Methane gas (CH4) and Carbon Dioxide (CO2), developed specifically to measure the emissions generated by cattle. Unlike previously reported models for the same purpose, this device reduces damage to the integrity of the animal and does not interfere with the activities of livestock in their development medium. FMEA and FL are used to validate the device’s functionality, which involves identifying possible failure modes that represent a more significant impact on the operation and prevent the prototype from fulfilling the function for which it was created. As a result, this document presents the development of an intelligent fuzzy system type Mamdani, supported in the Fuzzy Inference System Toolbox of MatLabR2018b®, for generating a risk priority index. A Fuzzy FMEA model was obtained to validate the prototype for measuring Methane and Carbon Dioxide emissions, which allows considering this prototype as a reliable alternative for the reliable measurement of these gases. This study was necessary as a complementary part in the validation of the design of the prototype quantifier of CH4 and CO2 emissions. The methods used (classic FMEA and Fuzzy FMEA) to evaluate the RPN show asymmetric graphs due to data disparity. Values in the classical method are mostly lower than the Mamdani model results due to the description of the criteria with which it is evaluated

An Agent-Based Model-Driven Decision Support System for Assessment of Agricultural Vulnerability of Sugarcane Facing Climatic Change

In recent years, there have been significant changes in weather patterns, mainly caused by sharp increases in temperature, increases in carbon dioxide, and fluctuations in precipitation levels, negatively impacting agricultural production. Agricultural systems are characterized by being vulnerable to the variation of biophysical and socioeconomic factors involved in the development of agricultural activities. Agent-based models (ABMs) enable the study, analysis, and management of ecosystems through their ability to represent networks and their spatial nature. In this research, an ABM is developed to evaluate the behavior and determine the vulnerability in the sugarcane agricultural system; allowing the capitalization of knowledge through characteristics such as social ability and autonomy of the modeled agents through fuzzy logic and system dynamics. The methodology used includes information networks for a dynamic assessment of agricultural risk modeled by time series, system dynamics, uncertain parameters, and experience; which are developed in three stages: vulnerability indicators, crop vulnerability, and total system vulnerability. The development of ABM, a greater impact on the environmental contingency is noted due to the increase in greenhouse gas emissions and the exponential increase in extreme meteorological phenomena threatening the cultivation of sugarcane, making the agricultural sector more vulnerable and reducing the yield of the harvest