Enhanced genetic algorithm-based fuzzy multiobjective strategy to multiproduct batch plant design

The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must form an integral part of the design problem. This work proposes an alternative treatment of the imprecision (demands) by using fuzzy concepts. In this study, we introduce a new approach to the design problem based on a multiobjective genetic algorithm, taking into account simultaneously maximization of the net present value NPV ~ and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The methodology provides a set of scenarios that are helpful to the decision’s maker and constitutes a very promising framework for taken imprecision into account in new product development stage. Besides, a hybrid selection method Pareto rank-tournament was proposed and showed a better performance than the classical Goldberg’s wheel, systematically leading to a higher number of non-dominated solutions

Enhanced genetic algorithm-based fuzzy multiobjective strategy to multiproduct batch plant design

This paper addresses the problem of the optimal design of batch plants with imprecise demands in product amounts. The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must constitute a basic part of the design problem. Rather than resorting to a traditional probabilistic approach for modeling the imprecision on product demands, this work proposes an alternative treatment by using fuzzy concepts. The design problem is tackled by introducing a new approach based on a multiobjective genetic algorithm, combined wit the fuzzy set theory for computing the objectives as fuzzy quantities. The problem takes into account simultaneous maximization of the fuzzy net present value and of two other performance criteria, i.e. the production delay/advance and a flexibility index. The delay/advance objective is computed by comparing the fuzzy production time for the products to a given fuzzy time horizon, and the flexibility index represents the additional fuzzy production that the plant would be able to produce. The multiobjective optimization provides the Pareto's front which is a set of scenarios that are helpful for guiding the decision's maker in its final choices. About the solution procedure, a genetic algorithm was implemented since it is particularly well-suited to take into account the arithmetic of fuzzy numbers. Furthermore because a genetic algorithm is working on populations of potential solutions, this type of procedure is well adapted for multiobjective optimization

Approche multicritère pour la conception d'ateliers discontinus dans un environnement incertain.

Les procédés discontinus représentent un mode idéal de fonctionnement pour synthétiser, en faibles quantités des produits à forte valeur ajoutée, à cycle de vie limité et exigeant un contrôle strict des conditions opératoires. De tels ateliers présentent l'avantage de pouvoir élaborer, par campagnes, plusieurs composés à partir d'équipements standard et de s'adapter à des variations de nature et de qualité des matières premières, ainsi qu'à des fluctuations fréquentes du marché, ce qui constitue un atout majeur du point de vue de la flexibilité. Ainsi, lors de l'étape de conception d'un atelier discontinu, il est presque impossible d'obtenir une information précise sur la future demande en produit. Une étude bibliographique a montré que les travaux antérieurs sont généralement basés sur des approches probabilistes qui représentent l'imprécision de la demande par des lois de distribution normale considérées comme indépendantes. Ces hypothèses simplificatrices ne représentent pas la réalité puisque beaucoup de paramètres sont, en pratique, dépendants les uns des autres et ne peuvent suivre des lois de distribution symétrique. L'objectif de cette thèse est de traiter de l'imprécision de la demande par des concepts flous en conception optimale d'ateliers; cette approche diffère principalement des modèles probabilistes en considérant la demande imprécise sous la forme d'ensembles de valeurs plus ou moins possibles et par sa fonction d'appartenance correspondante. Classiquement, la capacité de l'atelier doit satisfaire un équilibre entre la demande en produits et la marge de production dont dispose l'installation, de manière à satisfaire trois critères: la maximisation du bénéfice actualisé, une fonction représentant les retards ou les avances par rapport à échéance vis-à-vis de la synthèse de tous les produits et un indice de flexibilité. Les variables de décision sont la configuration de l'atelier, la taille et le nombre des équipements à chaque étape de traitement. Un modèle d'ateliers multiproduit est retenu comme support de l'étude. Un Algorithme Génétique multicritère, précédemment développé, a été adapté à la prise en compte de fonctions d'évaluations floues. Cette phase a nécessité l'utilisation d'opérations algébriques floues et un opérateur de comparaison de quantités floues. Par ailleurs, une méthode de sélection hybride tri de Pareto-tournoi a été proposée et a montré une meilleure performance que la méthode traditionnelle de la roulette de Goldberg, conduisant systématiquement à un nombre supérieur de solutions non dominées. La méthodologie multicritère est ensuite appliquée à un atelier multiproduit pour la production de quatre protéines, comportant huit étapes de traitement dont les temps opératoires des différentes étapes sont calculés par le biais de modèles d'opérations unitaires. L'approche retenue permet donc d'assister de manière efficace et robuste la mission du oncepteur, conduisant à un ensemble suffisamment large de solutions de compromis. ABSTRACT : Batch processes represent an ideal mode to synthesize in low volumes high value-added products characterized by short life cycle and requiring a strict control of operating conditions. Such plants are defined by their ability to elaborate various products from standard items, through a campaign operating mode. Furthermore, they are adaptable to variations of raw materials nature and quality as well as to market-driven fluctuations, which constitutes a major asset for flexibility. Consequently, at the design step of batch plants, it is almost impossible to get precise information about the future product demand. An overview of the state-of-the art showed that previous studies are generally based on probabilistic approaches, representing imprecision on demand through normal distribution laws, each one being considered independently. Yet, this simplifying assumption does not reflect real-world situations, since a lot of parameters are interdependent in practice and do not follow symmetrical distribution laws. The aim of this PhD work is to treat imprecision on demand using fuzzy concepts for batch plant design. This approach differs mainly from probabilistic models by considering the imprecise demands through sets of "more or less possible values" and by their corresponding membership function. Classically, the plant capacity must verify a balance between product demand and a production margin of the installation, in order to satisfy three criteria: (i) maximization of the Net Present Value, (ii) minimization of a function taking into account delays or advances with respect to a duedate for the synthesis of all products and (iii) maximization of a flexibility index. The decision variables are the plant configuration, i. e. size and number of items for each operating stage. A multiproduct batch model serves as a support of this study. A multicriteria Genetic Algorithm (GA), which was developed in previous works, was adapted to take into account fuzzy fitness functions. The GA required the implementation of algebraic fuzzy operations and of a comparison operator for fuzzy quantities. Besides, a hybrid selection method Pareto rank-tournament was proposed and showed a better performance than the classical Goldberg's wheel, systematically leading to a higher number of non-dominated solutions. The multicriteria methodology was then applied to a multiproduct plant for the production of four proteins, which eight processing stages. The processing time corresponding to each stage is computed by the use of process performance models. The proposed approach thus constitutes an efficient and robust support to assist the mission of the designer, leading to a quite large set of compromise solutions

A fuzzy multiobjective algorithm for multiproduct batch plant: Application to protein production

This paper addresses the problem of the optimal design of batch plants with imprecise demands and proposes an alternative treatment of the imprecision by using fuzzy concepts. For this purpose, we extended a multiobjective genetic algorithm (MOGA) developed in previousworks, taking into account simultaneously maximization of the net present value (NPV) and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The former is computed by comparing the fuzzy computed production time to a given fuzzy production time horizon and the latter is based on the additional fuzzy demand that the plant is able to produce. The methodology provides a set of scenarios that are helpful to the decision’s maker and constitutes a very promising framework for taken imprecision into account in new product development stage

Multiobjective Multiproduct Batch Plant Design Under Uncertainty

This paper addresses the problem of the optimal design of batch plants with imprecise demands and proposes an alternative treatment of the imprecision by using fuzzy concepts. For this purpose, we extended a multiobjective genetic algorithm developed in previous works, taking into account simultaneously maximization of the net present value (NPV) and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The former is computed by comparing the fuzzy computed production time to a given fuzzy production time horizon and the latter is based on the additional fuzzy demand that the plant is able to produce. The methodology provides a set of scenarios that are helpful to the decision’s maker and constitutes a very promising framework for taken imprecision into account in new product development stage

Optimization of a hydrogen supply chain network design under demand uncertainty by multi-objective genetic algorithms

Hydrogen is currently considered one of the most promising sustainable energy carriers for mobility ap- plications. A model of the hydrogen supply chain (HSC) based on MILP formulation (mixed integer linear programming) in a multi-objective, multi-period formulation, implemented via the ε-constraint method to generate the Pareto front, was conducted in a previous work and applied to the Occitania region of France. Three objective functions have been considered, i.e., the levelized hydrogen cost, the global warm- ing potential, and a safety risk index. However, the size of the problem mainly induced by the number of binary variables often leads to difficulties in problem solution. The first innovative part of this work explores the potential of genetic algorithms (GAs) via a variant of the non-dominated sorting genetic al- gorithm (NSGA-II) to manage multi-objective formulation to produce compromise solutions automatically. The values of the objective functions obtained by the GAs in the mono-objective formulation exhibit the same order of magnitude as those obtained with MILP, and the multi-objective GA yields a Pareto front of better quality with well-distributed compromise solutions. The differences observed between the GA and the MILP approaches can be explained by way of managing the constraints and their different logics. The second innovative contribution is the modelling of demand uncertainty using fuzzy concepts for HSC design. The solutions are compared with the original crisp models based on either MILP or GA, giving more robustness to the proposed approach

DESARROLLO DE UNA APLICACIÓN EN LENGUAJE JAVA UTILIZANDO LA METODOLOGÍA ANÁLISIS ESTRUCTURADO MODERNO

El objetivo de este trabajo fue implementar paso a paso la metodología de Edward Yourdon para el diseño de software; se aplicó a un caso práctico de desarrollo de software para determinar el nivel de riesgo ergonómico en puestos de trabajo utilizando el método REBA y lógica difusa. El software construido se probó en cuatro casos de estudio con un total de 16 posturas y permitió calcular de forma rápida el nivel de riesgo ergonómico para cada postura específica del trabajador.Palabras clave: Análisis estructurado, programación orientada a objetos, riesgo ergonómico, lógica difusa

A green supply chain network design framework for the processed food industry: Application to the orange juice agrofood cluster

Food production has put enormous strain on the environment. Supply chain network design provides a means to frame this issue in terms of strategic decision making. It has matured from a field that addressed only operational and economic concerns to one that comprehensively considers the broader environmental and social issues that face industrial organizations of today. Adding the term “green” to supply chain activities seeks to incorporate environmentally conscious thinking in all processes in the supply chain. The methodology is based on the use of Life Cycle Assessment, Multi-objective Optimization via Genetic Algorithms and Multiple-criteria Decision Making tools (TOPSIS type). The approach is illustrated and validated through the development and analysis of an Orange Juice Supply Chain case study modelled as a three echelon GrSC composed of the supplier, manufacturing and market levels that in turn are decomposed into more detailed subcomponents. Methodologically, the work has shown the development of the modelling and optimization GrSCM framework is useful in the context of eco-labelled agro food supply chain and feasible in particular for the orange juice cluster. The proposed framework can help decision makers handle the complexity that characterizes agro food supply chain design decision and that is brought on by the multi-objective nature of the problem as well as by the multiple stakeholders, thus preventing to make the decision in a segmented empirical manner. Experimentally, under the assumptions used in the case study, the work highlights that by focusing only on the “organic” eco-label to improve the agricultural aspect, low to no improvement on overall supply chain environmental performance is reached in relative terms. In contrast, the environmental criteria resulting from a full lifecycle approach is a better option for future public and private policies to reach more sustainable agro food supply chains

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

Social cost-benefit assessment as a post-optimal analysis for hydrogen supply chain design and deployment: Application to Occitania (France)

A lot of recent studies have concluded that hydrogen could gradually become a much more significant component of the European energy mix for mobility and stationary fuel cell system applications. Yet, the challenge of developing a future commercial hydrogen economy still remains through the deployment of a viable hydrogen supply chain and an increasing fuel cell vehicle market share, which allows to nar- row the existing cost difference regarding the conventional fossil fuel vehicle market. In this paper, the market penetration of hydrogen fuel cell vehicles, as substitutes for internal combustion engine vehicles has been evaluated from a social and a subsidy-policy perspective from 2020 to 2050. For this purpose, the best compromise hydrogen supply chain network configuration after the sequential application of an optimization strategy and a multi-criteria decision-making tool has been assessed through a Social Cost-Benefit Analysis (SCBA) to determine whether the hydrogen mobility deployment increases enough the social welfare. The scientific objective of this work is essentially based on the development of a method- ological framework to quantify potential societal benefits of hydrogen fuel cell vehicles. The case study of the Occitania Region in France supports the analysis. The externality costs involve the abatement cost of CO2 , noise and local pollution as well as platinum depletion. A subsidy policy scenario has also been im- plemented. For the case study considered, the results obtained that are not intended to be general, show that CO2 abatement dominates the externalities, platinum is the second largest externality, yet reduc- ing the benefits obtained by the CO2 abatement. The positive externalities from air pollution and noise abatement almost reach to compensate for the negative costs caused by platinum depletion. The exter- nalities have a positive effect from 2025. Using a societal cost accounting framework with externalities and subsidies, hydrogen transition timing is reduced by four years for the example considered