Multi-criteria Optimization Research Articles

Determining the library size for the optimal output plan in the RapidPlan knowledge-based planning system using multicriteria optimization.

The aim of this study was to determine the number of trade-off explored (TO) library plans required for building a RapidPlan (RP) library that would generate the optimal clinical treatment plan. We developed 2 RP models, 1 each for the 2 clinical sites, head and neck (HN) and cervix. The models were created using 100 plans and were validated using 70 plans (VP) for each site respectively. Each of the 2 libraries comprising 100 TO plans was divided into 5 different subsets of library plans comprising 20, 40, 60, 80, and 100 plans, leading to 5 different RP models for each site. For every validation patient, a TO plan (TO_VP) was created. For every patient, 5 RP plans were automatically generated using RP models. The dosimetric parameters of the 6 plans (TO_VP + 5 RP plans) were compared using Pearson correlation and Greenhouse-Geisser analysis. Planning target volume (PTV) dose volume parameters PTVD95% in 6 competing plans varied between 97.6 ± 0.7% and 98.1 ± 0.6% in HN cases and 98.8 ± 0.3% and 99.0 ± 0.4% in cervix cases. Overall, for both sites, the mean variations in organ at risk (OAR) doses or volumes were within 50 cGy, 0.5%, and 0.2 cc between library plans, and if TO_VP was included the variations deteriorated to 180 cGy, 0.4%, and 15 cc. All OARs in both sites, except D0.1 ccspine, showed a statistically insignificant variation between all plans. Dosimetric variation among various output plans generated from 5 RP libraries is minimal and clinically insignificant. The optimal output plan can be derived from the least-weighted library consisting of 20 plans. This article shows that, when the constituent plans are subjected to trade-off exploration, the number of constituent plans for a knowledge-based planning module is not relevant in terms of its dosimetric output.

Net-zero energy management through multi-criteria optimizations of a hybrid solar-hydrogen energy system for a laboratory in Toronto, Canada

Hydrogen production and storage in hybrid systems is a promising solution for sustainable energy transition, decoupling energy generation from demand and boosting the deployment of renewable energy. Nonetheless, the optimal and cost-effective design of hybrid hydrogen-based systems is crucial to tackle existing limitations in the diffusion of these systems. The present study explores net-zero energy management via a multi-objective optimization algorithm for an outdoor test facility equipped with a hydrogen-based hybrid energy production system. Aimed at enabling efficient integration of hydrogen fuel cell system, the proposed solution attempts to maximize the renewable factor (RF) and carbon mitigation in the hybrid system as well as to minimize the grid dependency and the life cycle cost (LCC) of the system. In this context, the techno-enviro-economic optimization of the hybrid system is conducted by employing a statistical approach to identify optimal design variables and conflictive objective functions. To examine interactions in components of the hybrid system, a series of dynamic simulations are carried out by developing a TRNSYS code coupled with the OpenStudio/EnergyPlus plugin. The obtained results indicate a striking disparity in the monthly RF values as well as the hydrogen production rate, and, therefore, in the level of grid dependency. It is shown that the difference in LCC between optimization scenarios suggested by the design of experiments could reach $15,780, corresponding to 57 % of the mean initial cost. The LCOE value yielded for optimum scenarios varies between 0.389 and 0.537 $/kWh. The scenario with net-zero target demonstrates the lowest LCOE value and the highest carbon mitigation, i.e., 828 kg CO2/yr, with respect to the grid supply case. However, the LCC in this scenario exceeds $57,370, which is the highest among all optimum scenarios. Furthermore, it was revealed that the lowest RF in optimal scenarios is equal to 66.2 % and belongs to the most economical solution.

Multi-criteria parametric optimization of the displacement and weight of a shell of minimal surface on a circular contour consisting of two inclined ellipses under thermal and power loading with consideration of geometric nonlinearity

Thin shells are well suited to optimal design problems, and the finite element method and gradient descent method make it possible to solve inverse problems in structural and applied mechanics. The calculation process takes into account geometric nonlinearity. In modern numerical studies, a formulation taking into account geometric nonlinearity in the finite element method is used, namely, a stepwise loading procedure. It becomes necessary to take into account the relations between displacement vectors and their derivatives and strain increments. These relations help to determine the stiffness matrix of the finite element at each loading step, which leads to a qualitative study of real displacements in the structure. The geometrically nonlinear calculation of a shell of minimum surface on a circular contour consisting of two inclined ellipses in a multi-criteria parametric optimization is performed by the finite element method. The finite element method is a universal variational method that is focused on solving the most complex problems of elasticity theory and applied and structural mechanics using separate calculation complexes. Stiffness matrix - for the whole body is formed on the basis of the finite element stiffness matrix. The system of solving equations of the finite element method is formed using the Lagrange's variational principle, according to which the total potential energy P of a finite element model of a body in a state of stability and equilibrium has a minimum value. As part of the sensitivity analysis, gradients of the design variables of the structure, displacements in the form of partial derivatives of these characteristics along the design variables, and shell thickness are calculated. The sensitivity information serves as the basis for building an optimal design algorithm using the gradient descent method of the objective function. Standard multi-criteria parametric optimization allows for an average of 10% steel savings, with geometric nonlinearity increasing to 20%. At this study site, 24.4% of sheet steel was saved, which is a significant relative saving. This methodology of the authors shows high results for innovative design and production of steel thin shells in Ukraine and around the world, and also makes it possible to apply several types of optimization to one research object.

Methodology for Selecting Optimal Routes for the Transportation of Dangerous Goods in Conditions of Risk Uncertainty

Abstract * A risk situation is a type of uncertainty situation where events are likely to occur. In other words, risk is an estimated probability, and uncertainty is something that cannot be quantified. The greater the uncertainty when making a decision, the greater the degree of risk. The investigation is devoted to the development of a unified methodology for selecting optimal (from the proposed alternatives) routes for transporting dangerous goods (any of the 9 standard classes) by road. A model for determining two groups of risk components for transporting dangerous goods on separate routes is proposed, namely: parameters of risks of probability of occurrence of emergency situations and parameters that affect the risks of complexity of eliminating their possible consequences. This opened up opportunities for correct selection of criteria when forming a multi-criteria optimization problem of transportation. Mathematical algorithms for a comprehensive solution of the multi-criteria optimization problem of routing dangerous goods transportation are developed using the classical criteria of Laplace, Wald, Savage, and Hurwitz. To confirm the legal capacity of the proposed approach and mathematical apparatus, the optimal route for transportation of cylinders with technical gases through the transport network of Lutsk was calculated. Possible alternative transportation routes have been identified and safety criteria have been formalized for two groups of risk components

Multi-criteria optimization of a combined power and freshwater system using modified NSGA-II and AHP-entropy-topsis

This paper proposed an innovative hybrid system to generate electricity and fresh water with higher energy efficiency. A subsystem is employed by integrating DCMD and heat pump to recover waste heat of PEMFC to produce fresh water. The thermodynamic and exergoeconomic models are established to evaluate the system comprehensive performance. Compared with the PEMFC alone, the results demonstrated that the proposed hybrid system achieves a 42.07 % increase in energy efficiency and provides 177.86 kg/h of fresh water. The components with high exergy destruction are PEMFC, Relief value, Inverter, Separator 2 and DCMD with the exergy destruction rate of PEMFC stack accounting for 54.55 %. The parametric analysis results indicate that the variation of the membrane filament number, feed and permeate flow rates all have evident impacts on the thermodynamic and economic performances of the hybrid system. The multi-objective optimization is conducted by adopting NSGA-II and three decision-making methods. The optimal solution obtained by AHP-entropy-TOPSIS approach for the net power out, exergy efficiency, WPC, and total exergy cost rate are 96059W, 0.5293, 0.2995 $/m3, and 14.27 $/h, respectively. In addition, the obtained results shown that the simulation times of NSGA-II are significantly reduced from 89.30 to 11.20 h by introducing BP-ANN to improve the simulation efficiency.

Development of decision-making technology for the provision of services in project implementation

The object of research is decision-making processes regarding the provision of services within the framework of cross-border projects. To achieve the aim of the research, an analysis of the service provision market was first conducted, its features have been revealed and problems arising in the processes of its functioning have been identified. The main problem is to find the optimal distribution of services between performers in the service management system. A mathematical model of the problem of single- and multi-criteria optimization has been developed, where the problem is decomposed into independent sub-problems. The problem is presented in the form of a linear programming problem. Various efficiency criteria of the found distributions are proposed. Depending on the number of criteria, the problem will be a single-criteria Boolean programming problem or a multi-criteria optimization problem. An iterative method for finding the optimal distribution of services has been created, and individual methods are laid out in the form of production rules, which is understandable and allows to gain new knowledge. Based on the obtained data, a decision-making technology has been developed regarding the distribution of service consumers between performers. At the same time, decision-making methods were used, which allow optimizing the processes of service provision. A systematic approach was used when designing information technology. This made it possible to create an effective and problem-relevant technology that helps in making informed decisions about the distribution of services between participants of cross-border projects. A structural and functional diagram of the decision support system has been developed. Its structural elements are detailed. The obtained results reflect a thorough analysis of the current state of the services market and the development of effective decision-making technology, which contributes to the optimization of work in the field of cross-border projects. This approach can be useful for various subjects involved in the implementation and coordination of international projects.

Application of The Aggregation and Multicriteria Optimization Method in Assessing the Level of the Knowledge Economy of World Regions in Industry 4.0

The article proposes a new methodology for determining the level of the knowledge economy of countries in terms of the possibilities of using Industry 4.0 technologies. The relevance of the study is due to the transition to a new technological structure – “Industry 4.0” in the world. The aim of the study is to analyze data on the main sub-indices of the knowledge economy associated with Industry 4.0 for countries around the world based on the author’s methodology. As a tool for this analysis, the k-means++ clustering method was used to classify countries according to their characteristics. The assessment of the potential of Industry 4.0 is determined on the basis of 5 criteria characterizing various aspects of the development of countries at the level of economic knowledge. In the calculation process, determining the extent to which the indicators used describe the level of the knowledge economy, and the use of the aggregation formula and multi-criteria optimization, constitutes the scientific novelty of the study. It was concluded that as a result of clustering the countries of the world using the formula Aggregation and Multicriteria Optimization, the number of clusters decreased and the distribution of countries between clusters began to more adequately reflect reality. The results of the study can be used in the process of assessing the competitiveness of countries during the transition to the Industry 4.0 economy, as well as in developing strategies and concepts for countries.

A multi-criterial optimization of low-carbon binders for a sustainable high-strength concrete using TOPSIS

The objective of this study is to focus on "low carbon binders," given the adverse environmental impacts resulting from the extensive emission of greenhouse gases. In the current context, the term "geopolymer concrete," often referred to as "low carbon binders," is gaining prominence as a means to develop an eco-friendly binding material. Furthermore, this study focuses on creating high-strength geopolymer binders using ambient curing conditions. Numerous factors are involved in determining the mix ratios for geopolymer concrete. Nonetheless, the reluctance to apply it in practical scenarios arises from an insufficient understanding of how to select the appropriate mix proportions based on these parameters. Therefore, utilizing Multi-Criteria Decision-Making presents the best choice for evaluating the geopolymer parameters. Among several MCDM methods, TOPSIS has been selected for this research. Geopolymer binders are produced using Ground Granulated Blast furnace Slag (GGBS), a by-product from the steel industry and metakaolin (MK), a product from the calcination of clay. A total of thirty-two combinations of geopolymer mortar mixes were created by varying the percentage of metakaolin, the ratio of alkaline activators (sodium silicate to sodium hydroxide, SS/SH), and the concentration of sodium hydroxide (NaOH). The assessment of these mixtures involves nine response factors, encompassing flow characteristics, initial and final setting time, compressive strength (7 and 28 days), initial drying shrinkage, energy consumption, CO2 emission, and financial expenses. A study of the microstructure is carried out to explore the impact of MK presence on geopolymers using GGBS as a base precursor. Hence, techniques for optimizing multiple responses were employed to attain the desired attributes while maintaining all measurable factors within an acceptable range. This approach to optimization holds significant importance, particularly when dealing with geopolymer concrete, as it can enhance its utilization compared to traditional cement-based concrete.

Optimal Temperature Calculation for Multicriteria Optimization of the Hydrogenation of Polycyclic Aromatic Hydrocarbons by NSGA-II Method

Introduction. Multicriteria optimization, taking into account contradicting criteria, is used to improve production efficiency, reduce costs, improve product quality and environmental safety of processes. The literature describes the application of multicriteria optimization for production purposes, including the selection of reaction conditions and improvement of technological processes. In the presented paper, the object of research is the process of hydrogenation of polycyclic aromatic hydrocarbons (PAH) in the production of high-density fuels. To determine the optimal conditions of the process, the problem of multicriteria optimization based on the kinetic model is solved. The criteria include maximizing the yield of targeted naphthenes and conversion of feedstock. The research objective is to create a program implementing the multicriteria optimization non-dominated sorting genetic algorithm-II (NSGA-II). Due to this, it is possible to calculate the optimal temperature for the PAH hydrogenation process on the basis of the kinetic model.Materials and Methods. The NSGA-II genetic algorithm was used to solve the multicriteria optimization problem. Modified parental and survival selection within the Pareto front was also used. If it was necessary to divide the front, solutions based on the Manhattan distance between them were selected. The program was implemented in Python.Results. In the system of ordinary nonlinear differential equations of chemical kinetics, the concentration was designated yi, the conditional contact time of the reaction mixture with the catalyst — τ. The system was solved for the hydrogenation reaction of polycyclic aromatic hydrocarbons. The calculations showed that at τ = 0 y1(0) = 0.025; y2(0) = 0.9; y6(0) = 0.067; y9(0) = 0.008; yi(0) = 0, i = 3–5, 7, 8, 10–20; Q(0) = 1. The process temperature was considered as a control parameter according to two optimality criteria: maximizing the yield of target naphthenes (f1) at the end of the reaction, and maximizing the conversion of feedstock (f2). Values f1 were in the range of 0.43–0.79; conversion — 0.01–0.03; temperature — 200–300 K. The growth of temperature was accompanied by an increase in the yield of target naphthenes and a decrease in the conversion of feedstock. Each solution obtained was not an unimprovable one. When modeling the process of hydrogenation of PAH, an algorithm was launched with a population size of 100 and a number of generations of 100. A program implementing the NSGA-II algorithm was developed. The optimal set of values of the PAH hydrogenation reaction temperature was calculated, which made it possible to obtain unimprovable values of the optimality criteria — maximizing the yield of target naphthenes and conversion of feedstock.Discussion and Conclusion. The NSGA-II algorithm is effective for solving the problem of non-dominance, and deriving the optimal solution for all criteria. Future research should be devoted to the selection of optimal algorithm parameters to increase the speed of the solution. Based on the obtained theoretical optimal conditions of the PAH hydrogenation reaction, it is possible to implement the process in industry

N-type-2-ARAS: An efficient hybrid multi-criteria optimization approach for end-of-life vehicle’s recycling facility location: A sustainable approach

End-of-life vehicle (ELV) management is currently one of the most pressing environmental issues. Waste must be recycled to ensure that ELV management is both environmentally and economically sustainable. Cairo boasts the greatest percentage of Egyptian citizens who own a car and the country's oldest automotive fleet. There is a great desire to open a new ELV recycling factory in this city. One of the most difficult strategic issues facing decision-makers is determining where to build new facilities. ELV recycling facility selection is a multi-criteria and highly uncertain challenge, and this research presents a novel approach to help with the process. An extension of the Additive Ratio Assessment (ARAS) approach in a neutrosophic type-2 environment ((N-type-2 ARAS)) is described for the first time. The new approach (N-type-2 ARAS) is used to choose an ELV recycling facility location. The proposed N-type-2 ARAS method's potential and applicability are illustrated in the context of a real-life case study in Egypt. This study used five main criteria, twenty sub-criteria, and four alternatives (locations) to select the best location. The results show that Luxor is the best location. This method has been proven reliable and consistent compared to the current state-of-the-art interval type-2 fuzzy set-based MCDM methods. This study's sensitivity analysis was conducted to show rank stability from the (N-type-2 ARAS) methodology.

Dry milling of stir-cast and heat-treated EV31A magnesium alloy: An experimental investigation based on the multi-criteria optimization method

The purpose of this study is to examine the impact of various milling parameters and sample preparation methods on the end-milling of EV31A Mg alloy under as-cast, T4, and T6 heat-treated conditions. The test specimens were prepared through stir-casting and heat-treated according to T4 and T6 conditions. The CNC vertical milling machine with the HSS tool was used for the end-milling process based on Taguchi's L9 orthogonal array. Three levels of variation are applied to the process parameters, including spindle speed (S), feed rate (f), depth of cut (d), and test specimen (as-cast, T4, and T6 EV31A). The parameters of surface roughness (Ra, Rz, Sa, Sz), the rate of material removal (MRR), and the cutting force (Fr) were similarly measured. For surface roughness, optical profilometry was used, while the weight loss method and dynamometer were employed, respectively, to assess MRR and Fr. In order to improve the process parameters, TOPSIS (techniques order preferences by similarity to ideal solution) is employed. This method reduces several responses to a single measurable response (the closeness coefficient). S = 1500 rpm, f = 150 mm/min, and aP = 1.5 mm are the ideal process parameters to achieve the maximum closeness coefficient (Ci) of 0. 917129 under as-cast circumstances. Furthermore, analysis of variance (ANOVA) verifies that sample preparation methods of 24.59 % and depth of cut of 38.19 % significantly impact response characteristics.

Analysis of scenarios for transforming the object "Ukryttya" into an environmentally safe system by the method of multicriteria optimization

Based on the criteria for comparative analysis of scenarios grouped into factors, the value of scenarios was determined using the multicriteria optimization methodology. The evaluation methodology includes indicators of weights of factor groups and weights of criteria for the relevant factors. Scenario value assessment is determined by calculating a generalized weighted additive function. It is used to rank the scenarios with the established parametric characteristics of the criteria. The results of the scenario assessment and their ranking are recommendations for making decisions on the sequence of scenario implementation.

Оценка эффективности системы управления крупномасштабными строительными проектами в течение их жизненного цикла

The relevance of the study is due to the increasing importance of large-scale construction management processes throughout the full life cycle, as well as the system-technical nature of the problems that arise in this case. To determine the effectiveness of the management system, the principles of a systematic approach can be applied, in which the subject of economic activity is considered as a single whole with an ordered structure and relationships. It has been established that currently there are many problem-oriented models (simulation, optimization, expert) containing various quantitative indicators and methods for determining effectiveness aimed at finding economically feasible management solutions and providing for their subsequent practical implementation. However, a unified methodological understanding on the issues of evaluating the effectiveness of large-scale construction management has not yet been achieved. The purpose of the study is a methodological justification of a holistic approach to evaluating the effectiveness of a large-scale construction management system. As a working hypothesis, the assumption of a direct inextricable link between the integrity of the management system and the effectiveness of its functioning is accepted. The conceptual framework of the theory of integrity, mathematical statistics, and set theory is used within the framework of a systematic approach. As a result of the conducted research, an understanding of the effectiveness of large-scale construction management has been developed as the integrity of the solutions embedded in it, expressed by a set of any assessments that adequately express the need and possibility of implementing these solutions. The economic aspect of the systemic efficiency of capital management of a construction enterprise is investigated and a variant of its cost (monetary) assessment is proposed. The result obtained in the course of the study can be used as the basis for the development of a wide class of tasks for multi-criteria optimization of the operation of any large-scale construction management system throughout the full life cycle.

Thermo-environmental multi- investigation and ANN-based optimization of a novel heat integration criteria system integrated with a marine engine generating liquefied hydrogen

Marine transportation is a significant contributor to overall energy consumption among transportation sectors and is responsible for producing a considerable amount of greenhouse gases. A potential solution for alternative applications involves implementing combined heat integration, thereby enabling the production of additional utilities and mitigating emissions. The current work introduces an innovative heat integration process for a marine engine, focusing on implementing an optimal thermal matching technique to minimize overall irreversibility in producing liquefied hydrogen and coolant. The process incorporates an organic flash-bi-evaporator cooling cycle, a humidification dehumidification desalination, a polymer electrolyte membrane water electrolysis process, and a Claude cycle. The generated freshwater is delivered to the electrolyzer to produce gaseous hydrogen. This product and the cooling for freezing are utilized in the Claude cycle for hydrogen liquefaction. The study utilizes an advanced thermo-environmental multi-criteria investigation and optimization, considering sensitivity analysis and optimization based on artificial intelligence method. The optimization process incorporates the training and testing of artificial neural networks, NSGA-II method, and TOPSIS decision-making. The primary objective functions include exergetic efficiency and carbon dioxide emission. The findings demonstrate that the specified objectives are computed to be 0.121 and 2.67 kg/MWh, correspondingly. Besides, this condition exhibits a liquefied hydrogen flow rate of 6.44 L/h and a cooling output of 43.61 kW, showing an energy efficiency of 0.1145. Also, the total exergy destruction rate associated with the arranged structure is 124.5 kW. Furthermore, the optimum state reveals an exergoenvironmental index of 0.840 and an exergetic stability factor of 0.869.

Optimization of Reinforced Concrete Structures in Terms of Environmental Impacts, Durability and Cost

The paper deals with the methods of software optimization of the concrete structure in terms of environmental issues, durability, and cost. It links to previously developed software tool that enables multi-criteria optimization of a 1 m2 one-way slab. A feasibility study focused on the optimization of larger structural units, and it analyses and compares methods of concrete structures optimization in terms of environmental impacts, durability, and life-cycle cost.

Search for Optimal Parameters in the Control Structure of a Surgical System for Soft Tissue Operations Based on In Vitro Experiments on Cardiovascular Tissue

The surgical robots currently used in cardiac surgery are equipped with a remote center of motion (RCM) mechanism that enables the required spherical workspace. The dynamics model of the surgical robot’s RCM mechanism presented in this work includes a direct current (DC) motor, an optimal proportional–integral–derivative (PID) controller, and a LuGre friction model that takes into account the Stribeck effect and surface deformation. A finite element method (FEM) analysis of transients was carried out using the energy hypothesis of von Mises with an optimal input signal from the mechatronic system with a PID controller obtained using the Runge–Kutta differentiation method in the Dormand–Prince ordinary differential equations variant (ODE45). Five criteria were adopted for the objective function: the safety factor related to the stress function in the time-varying strength problem, the first natural frequency related to stiffness and the resonance phenomenon, the buckling coefficient in the statics problem related to stability, the static factor of safety, and the displacement of the operating tip. The force inputs to the dynamics model were derived from in vitro force measurements on cardiovascular tissue using a force sensor. The normality of the statistical distribution of the experimental data was confirmed using the Kolmogorov–Smirnov statistical test. The problem of multi-criteria optimization was solved using the non-sorter genetic algorithm (NSGA-II), the finite element method, and the von Mises distortion energy hypothesis. Velocity input signals for the transient dynamics model were obtained from a second in vitro experiment on cardiovascular tissue using the minimally robotic invasive surgery (MIRS) technique. An experienced cardiac surgeon conducted the experiment in a modern method using the Robin Heart Vision surgical robot, and a system of four complementary metal–oxide–semiconductor (CMOS) optical sensors and ariel performance analysis system (APAS-XP 2002) software were used to obtain the endoscopic tool trajectory signal. The trajectory signal was accurate to ±2 [mm] in relation to the adopted standard, and it was smoothed using the Savitzky–Golay (SG) polynomial smoothing, whose parameters were optimally selected using the Durbin–Watson (DW) statistical test.

Modeling and solving Passenger ship evacuation arrangement problem

In emergency scenarios on large passenger ships, ensuring the safety and efficient evacuation of a significant number of passengers is of utmost importance. Well-designed and preplanned evacuation arrangements are essential for a smooth and timely evacuation process. However, current research and evacuation models often overlook crucial planning considerations, including the allocation framework, counterflow prevention, and plan feasibility. To bridge these gaps, this paper presents a novel multi-criteria optimization model that addresses these overlooked aspects. A unique heuristic solution is proposed, utilizing a two-stage simulated annealing algorithm, to effectively solve this complex evacuation problem. When applied to a full-scale 14-deck cruise ship with 4977 passengers, the presented approach results in an optimized evacuation plan that markedly outperforms the original plan from the design materials, achieving reduced evacuation time and counterflow elimination. This research offers a practical tool for emergency management on large passenger ships, empowering ship designers and safety engineers to make informed, data-driven decisions.

Adaptively exploring the feature space of flowsheets

AbstractSimulation and optimization of chemical flowsheets rely on the solution of a large number of nonlinear equations. Finding such solutions can be supported by constructing machine learning‐based surrogate models, relating features and outputs by simple, explicit functions. In order to generate training data for those surrogate models computationally efficiently, schemes to adaptively sample the feature space are mandatory. In this article, we present a novel family of utility functions to favor an adaptive, Bayesian exploration of the feature space in order to identify regions that are convergent and fulfill customized inequality constraints. Moreover, points close to the Pareto‐optimal domain with respect to conflicting objectives can be identified, serving as good start values for a multicriteria optimization of the flowsheet. The benefit is illustrated by small toy‐examples as well as by industrially relevant chemical flowsheets.

Multi-criteria structural optimization of a hood inner structure using machine learning technique

This article presents an efficient multi-criteria structural optimization process based on machine learning for shape and size optimization of the hood inner. For this purpose, a low dimensional parameterization based on two-dimensional mapping and dimensionality reduction is used to decrease the number of design variables. There are many design parameters to be considered for designing this structure. In this study, the effect of three important criteria consisting of head injury, eigenfrequency, and static load were analyzed. A full vehicle finite element model is prepared for a newly developed product because head injury analysis requires a full vehicle model due to contact with the inner components of the engine compartment. Then, an experimental study is performed based on the real test scenario for model verification. A new rating method for analyzing the head injury criteria evaluated in different points is proposed and different machine learning techniques are compared in the approximation of the objective function. Finally, the problem is solved as both single and multi-objective optimization problems. Results present considerable improvement in accuracy and efficiency in the identified structure while the method has a low computational cost and the implementation is not difficult.

Minimizing the environmental impacts of waste valorization systems using multi-criteria life cycle optimization

Unsustainable waste management practices impose severe environmental hazards, health risks, and financial burdens. Waste valorization systems have emerged to mitigate the negative impacts of landfilling while recovering finite materials and renewable energy. This study aims to develop a life cycle assessment (LCA)-based multi-criteria framework using linear programming to optimize municipal waste allocation to multiple valorization facilities. The developed optimization model minimizes the life cycle impacts of integrated waste management on several life cycle impact categories. A multi-criteria decision-making analysis is implemented to account for the relative importance of the examined environmental categories, and a sensitivity analysis is conducted to study the effect of varying the importance weights. The variation between different LCA methods, IMPACT, CML, and ReCiPe, is also investigated. The proposed framework is applied to Egypt based on local conditions and strategic priorities. The LCA findings reveal that processing recyclables in material recovery facilities enhances the environmental performance in all impact categories except ozone layer depletion. To achieve minimum environmental effects on the assessed impact categories, single objective models suggest the diversion of recyclables to material recovery facilities and non-recyclables to gasifiers. The Pareto front of IMPACT involves anaerobic digesters and incinerators, CML comprises composting facilities, and ReCiPe includes incinerators. Overall, this study provides a lifecycle-based decision-making guide for the planning and optimization of sustainable waste management systems, and highlights the potential variations in the outcomes of different LCA methods adopted.