Multi-criteria Optimization Research Articles

Encoding consumer interests into product snippets with a multi-criteria genetic optimization approach

As an essential product cue in consumer information foraging, textual snippets can convey valuable scents that attract consumers to further access products, paving the way for online sellers to seize a competitive advantage. Premised on shopping goals theory, this study proposes a novel approach to designing high-quality product snippets that are particularly enhanced with consumer interests. First, snippet encoding is formulated as a multi-criteria optimization problem in which information sources are incorporated to distill consumer-appealing keywords considering both driving search and attracting selection, as well as to integrate the perspectives of sellers and consumers. Subsequently, a constrained genetic solution algorithm is developed, which copes well with the evolutionary nature of the problem to optimize snippets in an effective and efficient manner. Extensive experiments are conducted to verify the validity and superiority of the proposed approach.

Inventory classification with artificial intelligence: Conceptual framework

In today's world where competition is increasing harshly, it is important to achieve sustainable profits and keep costs competitive. Under these conditions, the importance of supply chain elements is increasing day by day. Management of inventory costs, which constitute a large volume among cost items, affects the performance of companies. The first step to focus on to keep inventory costs under control and improve is inventory classification. Inventory classification, which is at the top of the supply chain elements and closely affects the subsequent phases, it is critical in determining supply chain performance. Thanks to inventory classification, material groups are determined and stock strategies for these groups are clarified. Incorrect inventory classification causes materials to be assigned to incorrect groups, which negatively affects inventory costs and subsequent phases of the supply chain, causing an increase in costs. The most used methods for inventory classification are ABC analysis, multi-criteria inventory classification and optimization. However, the increasing momentum in artificial intelligence studies in recent years has also closely affected inventory classification. The advantages brought by artificial intelligence methods have also created distinctive contributions to inventory classification studies. This study provides a conceptual framework that examines artificial intelligence methods in the field of inventory classification.

Practical Approach to Studying Evolutionary Methods for Setting Weight Coefficients of Artificial Neural Networks

The article describes the problems of developing neurocontrollers for controlling dynamic objects, including the complexity of forming training data sets. It is indicated that one of the known methods for training an artificial neural network controlling an object is the neuroevolutionary approach, which involves using a genetic algorithm to adjust the synaptic weighting coefficients of an artificial neural network. The idea of using a means of demonstrating the evolutionary approach to adjusting the weighting coefficients of an artificial neural network for practical training of students in the basics of the neuroevolutionary approach is proposed. Software has been developed to demonstrate the neuroevolutionary approach using the example of the evolution of an artificial neural network of a given structure intended to control a simplified computer model of an autonomous vehicle. A method for resolving the problem of stagnation when using the evolutionary approach to training an artificial neural network is described. Options for using the developed software in teaching students the basics of artificial intelligence technologies and evolutionary methods of multicriteria optimization are proposed.

Methods of multi-criteria optimization of technological objects: a systematic review of scientific publications for the period 2013–2023

The article presents a systematic review of scientific articles devoted to applications of multi-criteria optimization methods, published in open sources in 2013-2023, conducted in accordance with the PRISMA recommendations. The main optimization methods with respect to a set of conflicting objective functions, such as energy efficiency, accuracy of achieving final states, economic feasibility, reliability of operation, compliance with environmental standards, etc. are considered. Particular attention is paid to the theoretical foundations and practical applications of these methods aimed at increasing the efficiency of technological processes and improving the quality of decisions. The main objective of the review is to analyze the current state of research in this area, to identify trends and limitations in the use of multi-criteria methods for optimization of specific technological processes and objects. The study showed that, despite significant progress in the development of the theoretical foundations of multi-criteria optimization, the number of works aimed at applying these methods to real technological problems remains limited. The article emphasizes the importance of further research and development aimed at adapting optimization methods to the specific features of technological processes. This review aims to provide a deeper understanding of current trends and issues related to the application of multi-objective optimization methods to improve technological processes, thereby contributing to the development of research in this promising area.

Molecular modeling and simulation of organic electrolyte solutions for lithium ion batteries.

Multi-criteria optimization is used for developing molecular models for ethylene carbonate (EC) and propylene carbonate (PC), organic solvents commonly used in Li-ion batteries. The molecular geometry and partial charges of the solvents are obtained from quantum mechanical calculations. Using a novel optimization strategy that combines systematic variations of the Lennard-Jones parameters with a reduced units approach, the models are fitted to experimental data on the liquid density, vapor pressure, relative permittivity, and self-diffusion coefficient. Since no experimental data for the self-diffusion coefficient of pure EC were available in the literature, they are measured in this work using a gradient-based nuclear magnetic resonance technique. For all pure component properties, excellent agreement between experiment and simulation is obtained. Moreover, the predictive capabilities of the new solvent models are assessed by comparison to experimental data for the liquid density and relative permittivity of mixtures of EC and PC. In addition, molecular models for the anions PF6-, BF4-, and ClO4- in solutions of their lithium electrolytes in PC are developed using experimental data on the solution densities. Finally, the self-diffusion coefficients of LiPF6 in PC and in aqueous solution are predicted and compared, showing that diffusion is much slower in the organic solution due to the formation of larger solvent shells around the ions. Furthermore, an analysis of the radial distribution functions in these solutions suggests that the ions have much less impact on the structure of the solvent PC than on water.

ANN modeling and optimization of friction stir welding performance for AA6061 and AA5083 alloy joints

This research focused on the characteristics of the AA6061/AA5083 alloy joints which are fabricated by the friction stir welding (FSW) technique. The impact of FSW parameters such as tool tilt angle, pin depth, and tool geometry on the mechanical properties and fractographic characteristics were studied. Ultimate tensile strength (UTS), hardness (HBN), and specific wear rate (SWR) were examined for characterization while fractography analyses were done using scanning electron microscopy. Taguchi-Grey relational analysis (GRA) was employed to execute multicriteria optimization and recognize the best grouping of parameters. The optimized values attained from this analysis were 259.2 MPa for UTS, 94.9 HV for HBN, and 0.0819 m³/Nm for SWR. The analysis of variance (ANOVA) results derived from the GRA revealed that tool geometry exerted the most significant influence (39.79%) on output factors, followed by pin depth (24.90%) and tilt angle (24.26%). To enhance the predictive accuracy of the output responses, an artificial neural network (ANN) model was developed utilizing the Levenberg-Marquardt (LM) algorithm. The optimized ANN architecture, configured as (3–10–3), exhibited robust regression analysis outcomes, showcasing correlation coefficients ( R) of 0.99999, 0.99967, and 0.99994 for the training, validation, and test datasets. The overall R-value, computed at 0.99958, affirmed high conformity between experimental and predicted values.

Multi-objective rescheduling of job shop scheduling problems in manufacturing using Quantum Annealing

Unforeseen events in manufacturing necessitate rescheduling to minimize lead times and maintain continuous production. Reducing rescheduling effort is also crucial otherwise additional setup times are incurred. This paper extends a dynamic rescheduling workflow, previously demonstrating success with Quantum Annealing through a multicriterial optimization. It introduces a second objective function considering rescheduling effort alongside makespan. The study addresses job arrivals and machine failures, employing a binary quadratic model for optimization. Results highlight the superiority of Quantum Annealing for larger problems compared to classical metaheuristics. Nevertheless, parameter selection complexities urge further exploration such as exploring deep learning techniques to tackle this challenge.

DEVELOPMENT OF METHODOLOGY AND STUDY OF THE INFLUENCE OF TECHNICAL, ORGANIZATIONAL AND TECHNOLOGICAL FACTORS ON THE DURATION OF THE PROCESS OF LIQUIDATION AND LOCALIZATION OF THE DEVELOPMENT OF DESTRUCTIONS

Analysis of the influence of technical, organizational and technological factors on the duration of the process of liquidation and localization of the development of destruction is an important stage of research into the process of liquidation of the consequences of accidental destruction. In order to compare options for liquidation and localization of the development of destruction of an emergency building, three options for reinforcement systems were analyzed: metal typical, wooden individual and combined. The work uses the method of analysis of hierarchies - a mathematical tool of a systematic approach to complex decision-making problems. The method does not impose the "correct" option, but allows you to interactively find an alternative (rational reinforcement option) that best matches the requirements for solving optimization tasks. To reduce the subjectivity of making a justified optimization decision in the conditions of complex multi-criteria optimization problems, a solution to the local problem of finding the optimal alternative according to the duration criterion, or a group of alternatives, taking into account the ratio of advantages based on the vector criterion, is proposed. To indicate the influence of advantages in quantitative expression, a pairwise comparison scale was used, which consists in a qualitative and quantitative assessment of relative advantage: equal in importance - 1; weak advantage – 2; average degree of preference ‒ 3; preference above the average degree ‒ 4; moderately stronger advantage ‒ 5; strong advantage ‒ 6; much stronger advantage ‒ 7; the most significant advantage is 8; absolute advantage ‒ 9. Initial data for choosing a temporary reinforcement option were obtained by comparing the estimated duration of dismantling and restoration works and optimizing the sequence of their execution for three options using wooden individual, metal typical and combined temporary reinforcement systems. It was established that it is impossible to ensure complete unification of reinforcement elements using only typical elements, therefore it is effective to use combined reinforcement systems using typical metal and individual wooden elements.

Methods of Multi-Criteria Optimization of Technological Processes in a Fuzzy Environment Based on the Simplex Method and the Theory of Fuzzy Sets

Many modern technological objects in practice are characterized by the uncertainty of the initial information necessary for their management. Recently, one of the pressing scientific and practical problems is the development of new optimization methods for controlling the operating modes of such objects in a fuzzy environment. In this regard, the objective of this study is to develop methods of multi-criteria optimization in a fuzzy environment by modifying the simplex method and various optimality principles based on fuzzy mathematics methods. The methodology of the proposed study is based on a hybrid approach, which consists of the integrated use and modification of simplex methods and optimization methods with various optimality principles for working in a fuzzy environment. The main results are as follows: a simplex method of multi-criteria optimization of immeasurable criteria (here, we are talking about the impossibility of physical measurements of criteria, the values of which are estimated by decision maker); a theorem on the convergence of the solution sequence obtained using the proposed method to the minimum value of the criteria; a heuristic method based on a modification for fuzziness and a combination of the maximin and Pareto optimality principles, which allows effectively solving multi-criteria optimization problems in a fuzzy environment. The heuristic method proposed will be used to solve a real production problem—optimization of the technological process of benzene production.

Investigation of sustainable operation oriented- economic, process and environment based multi-criteria optimization of large scale methanol production plant

Methanol is one of the main raw materials and a building block for many essential chemicals in the industry, and it is widely produced from the synthesis gas. It is used as a fuel, solvent, inhibitor, antifreeze agent, and in producing a variety of chemicals including olefins and paraffin. The global demand for methanol is 70 million tons per year and it is estimated to grow by a factor of 6–8% per year so it is necessary to develop a methanol production process, which is economical, and environment friendly. The multi-objective optimization (MOO) approach is used to optimize the large scale Lurgi two-step methanol synthesis process using the non-dominated sorting genetic algorithm-II (NSGA-II) to find the Pareto front solutions involving the process, economic, and environmental-based objectives. Methanol production rate, total energy consumption, total annual CO2 emissions, and profit are considered as objectives. The five decision variables include syngas molar flow, reactor feed pressure, reactor 1 temperature, reactor 2 temperature, and by-product mass flow in the methanol distillation column. Two constraints are set on ethanol mole fraction in the methanol stream, and methanol mole flow in water to the treatment stream. Three optimization cases (two bi-objective and one tri-objective) are developed. In the Case 1 study, CO2 emissions decreased by 65.83%, the methanol production rate increased by 10.11%, and total energy was reduced by 57.55%. The Pareto ranking is carried out using the Preference Ranking On the Basis of Ideal-average Distance (PROBID) method and the best optimal solution is reported for all cases.

Nature's Load-Bearing Design Principles and Their Application in Engineering: A Review.

Biological structures optimized through natural selection provide valuable insights for engineering load-bearing components. This paper reviews six key strategies evolved in nature for efficient mechanical load handling: hierarchically structured composites, cellular structures, functional gradients, hard shell-soft core architectures, form follows function, and robust geometric shapes. The paper also discusses recent research that applies these strategies to engineering design, demonstrating their effectiveness in advancing technical solutions. The challenges of translating nature's designs into engineering applications are addressed, with a focus on how advancements in computational methods, particularly artificial intelligence, are accelerating this process. The need for further development in innovative material characterization techniques, efficient modeling approaches for heterogeneous media, multi-criteria structural optimization methods, and advanced manufacturing techniques capable of achieving enhanced control across multiple scales is underscored. By highlighting nature's holistic approach to designing functional components, this paper advocates for adopting a similarly comprehensive methodology in engineering practices to shape the next generation of load-bearing technical components.

Lifecycle Assessment and Multi-Parameter Optimization of Lightweight Cement Mortar with Nano Additives.

With the growing global concerns regarding sustainable development in the building and construction industries, concentration only on the engineering properties of building materials can no longer meet the requirements. Although some studies have been implemented based on the lifecycle assessment of lightweight cement-based materials, very few attempts have been made pertaining to multi-criteria optimization, especially when fly ash cenospheres are used as lightweight aggregates and nano additives are incorporated as modifying admixtures. This investigation utilized cenospheres as fine aggregates to produce green, sustainable, lightweight cement mortar. Multi-walled carbon nanotubes at 0.05, 0.15, and 0.45% were binarily added, together with 0.2, 0.6, and 1.0% of nano silica to improve the mechanical performance. Strength tests were conducted to measure the flexural and compressive behaviors, combined with a cradle-to-gate lifecycle assessment and direct cost analysis to assess the environmental and economic viability. Integrated indexes and the TOPSIS method were adopted to systematically evaluate the mortar mixes and determine the optimal mix. The outcomes show that nano additives worked synergically to enhance the mechanical properties of the mortars. The utilization of cenospheres effectively reduced environmental impacts and improved economic feasibility. Nano additives significantly affected the sustainability and economic viability; in particular, the utilization of multi-walled carbon nanotubes increased the material costs. To minimize the impact of the price of multi-walled carbon nanotubes, it is proposed to binarily use less expensive nano silica. In the multi-parameter optimization, the mix with 0.05% multi-walled carbon nanotubes and 0.02% nano silica was recommended to be the optimal mix.

Optimisation of machining parameters in high-speed end milling of hardened AISI H13 die steel by integrating RSM and GA

ABSTRACT This study explores the potential of high-speed hard-end milling as a cost-effective alternative to the time-consuming grinding process for Chromium-molybdenum steel (AISI H13), widely used in cold and hot work tooling. The research aims to optimize machining parameters for achieving desired surface roughness, comparable to grinding and polishing. Cutting depth (0.4 - 0.8 mm), feed rate (1 - 4 μm per tooth), and cutting speed (80 - 140 m/min) were varied, and soybean oil replaced conventional lubricants. Employing Titanium Aluminum Nitride (TiAlN) coated end mills (3 mm diameter, 2 μm coating thickness) and response surface method (RSM) with Design Expert software, the study aimed at reducing tool wear and enhancing surface quality through multi-criteria optimization. Surface roughness analysis led to a streamlined surface by lowering mass flow rate and increasing cutting speed. The optimization approach predicted an optimal surface roughness value of 0.217475 μm at a cutting speed of 110 m/min, a feed rate of 1 μm/tooth, and a cut depth of 0.4 mm using genetic algorithm (GA), closely aligned with RSM’s ideal value of 0.215 μm. Both values converge around the 0.4 mm cut thickness.

Multi-criteria optimisation of subcritical wet oxidation for sludge treatment

Subcritical wet oxidation (SWO) is an environmentally-friendly solution for sewage sludge volume reduction. However, little study has comprehensively optimised SWO conditions across various aspects. This study developed a multi-objective model using genetic algorithms (GAs) to optimise SWO conditions, considering sludge deconstruction, emissions, energy balance, and resource recovery. The multi-criteria optimisation approach highlights the significant environmental benefits of SWO, including substantial sludge volume reduction and effective pollutant removal. An in-depth analysis of temperature, reaction time, and severity factor revealed their critical roles in enhancing sludge deconstruction and resource recovery efficiency. GAs predicted optimal conditions at 271 ± 2 °C and 51 ± 1 min, with confirmation experiments showing only 12% discrepancy between predicted and actual outcomes. This study provides practical insights for efficient sewage sludge treatment and sustainable wastewater management.

910: Comparison of Eclipse and RayStation in Multi-Criteria Optimization in Prostate Cancer Radiotherapy

910: Comparison of Eclipse and RayStation in Multi-Criteria Optimization in Prostate Cancer Radiotherapy

Renewable Energy Technology Selection for Hotel Buildings: A Systematic Approach Based on AHP and VIKOR Methods

This study provides an assessment of renewable energy technology utilization in hotel buildings, which are significant structures in terms of energy consumption. The aim of the study is to determine suitable renewable energy technologies (RETs) for hotel buildings by defining criteria for evaluating RETs, assessing the relative importance of these criteria, and proposing a multi-criteria decision-making framework to solve the problem of selecting the most appropriate RETs during the design stage. The alternatives for RETs and the criteria for their evaluation are gathered through a literature review and expert consultations. Eight fundamental RETs used in hotel buildings (such as heat pumps, solar panels, biomass boilers, etc.) are examined, and nine selection criteria are analyzed. According to the weights determined by the Analytic Hierarchy Process (AHP) method, the initial investment cost is the most influential decision criterion, with a weight of 0.314. As a result of applying the AHP and VIKOR (Multi-Criteria Optimization and Compromise Solution) methods for technology selection, photovoltaic panels emerge as the top-ranked choice. This comprehensive evaluation provides stakeholders in the building production process of hotel buildings with detailed analyses and multi-criteria decision-making methods for selecting RETs.

Multi-criteria Optimization for Wear and Friction Properties of Self-Lubricating Al6082-T6/GNP/TiB2 Hybrid Composites

Multi-criteria Optimization for Wear and Friction Properties of Self-Lubricating Al6082-T6/GNP/TiB2 Hybrid Composites

An experimental investigation on copper square electrode wear in electric discharge machining of Hastelloy B2

Hastelloy B2 is a high-performance nickel-molybdenum alloy and has been used in micro reactor, pump, pipe, and corrosion-resistant equipment. Square holes are required in the above application components. Although square holes are easily produced by spending more electrodes wear in Electrical Discharge Machining (EDM). Moreover, the square hole with small electrode wear achieved is a challenging task in thermal machining. Hence, in this work, the effects of EDM process parameters on electrode taper angle, electrode radius, electrode wear length and cutting time of Hastelloy B2 are studied. Taguchi based VišekriterijumskoKompromisnoRangiranje (VIKOR) method is used to find the optimum process parameters and compared with Taguchi based Grey Relational Analysis (GRA) method for validation. The results showed that current and pulse duration greatly affects the electrode wear out surface and edges. The square electrodes with sharp edges and surfaces are fully transformed into hemispherical shapes at electrode ends due to an unstable spark with diffusion of heat. Also, the VIKOR method provides an alternative multi-criteria optimization method that enhances the process performance over the grey relational analysis due to comparability, uniqueness, assessment and prediction.

Design and multiobjective optimization of a two‐point contact ladder‐climbing robot using a genetic algorithm

AbstractThis paper presents the design and optimization of a climbing robot. The design of a ladder‐climbing robot is done with fundamental mathematical considerations. The designed robot is robust enough to manage all environmental calamities, and at the same time, it is optimized for lightweight to reduce the actuator's cost and ease of transportation. An analytical evaluation is carried out for both static and dynamic conditions to determine strength and motion characteristics. The multiobjective optimization of the design parameters of a ladder‐climbing robot is done to obtain optimized values of design parameters. The formulation of an optimization problem that considers the minimization of weight and natural frequency is performed. Using an evolutionary genetic algorithm (GA) for the multicriteria optimization problem is solved, and a Pareto front solution is obtained. The optimal values of the parameters are decided based on the knee selection technique. As both objective functions are contradictory, the optimum results significantly improve the robot's performance. Controlling the proportional–integral–derivative (PID) parameters is crucial as the robot climbs with a two‐point contact gait pattern. The controlling parameters impart stability to the robot. PID parameters like proportional, integral and derivative gain are tunned using the GA. Finally, the developed prototype is tested on the ladders of the tower, and satisfactory climbing motion is achieved.

Rapid Surrogate-Aided Multicriterial Optimization of Compact Microwave Passives Employing Machine Learning and ANNs

Rapid Surrogate-Aided Multicriterial Optimization of Compact Microwave Passives Employing Machine Learning and ANNs