Objective Function Research Articles

Seismic retrofitting optimization model using fiber-reinforced polymer jacketing and NSGA-III

Fiber-reinforced polymer (FRP) is widely used for retrofitting structural elements due to its easy application. However, establishing a retrofit strategy is challenging due to conflicting objectives, such as cost and performance level, requiring optimization for effective decision-making. This study proposes a many-objective optimization model for seismic retrofitting using FRP and the Non-dominated Sorting Genetic Algorithm (NSGA)-III. The model's efficacy was demonstrated through two numerical examples: a reinforced concrete building retrofitted with FRP jackets and a masonry-infilled reinforced concrete building retrofitted with FRP bracings. Each example included three objective functions and multiple constraints. Nonlinear static pushover analysis provided optimal strategies to enhance base shear and energy dissipation while minimizing costs and retrofitting locations. Among the Pareto-optimal solutions, the optimal solution with the minimum Euclidean distance was selected. NSGA-III offered a wider distribution and more Pareto-optimal solutions compared to NSGA-II, demonstrating its potential in addressing many-objective problems related to retrofit decision-making.

A methodology to determine target gas supply reliability of natural gas pipeline system based on cost-benefit analysis

In this study, a methodology to determine the target gas supply reliability of the natural gas pipeline system based on cost-benefit analysis is proposed. In the methodology, an optimization model is developed firstly, and the objective function is constructed to maximize the difference between the benefits and investment when improving system reliability. Secondly, the investment and the benefits of reduced unreliability cost due to the implementation of the reliability improvement measures are considered in the optimization model. Thirdly, the virtual age model is employed to quantify the impact of reliability improvement measures on the unit's reliability, and the gas supply reliability evaluation method of the natural gas pipeline system is developed. Finally, the optimization model used to determine the target gas supply reliability is solved by a heuristic genetic algorithm. Furthermore, a real natural gas pipeline system located in China is applied to confirm the feasibility of the methodology, and the target gas supply reliability is determined, with a value of 0.998608. At last, the effect of system operational age on the target gas supply reliability is investigated through sensitivity analysis. The results show that the target gas supply reliability decreases with the increase of the operational age, which is consistent with the engineering practice.

Optimal Operation of an Industrial Microgrid within a Renewable Energy Community: A Case Study of a Greentech Company

The integration of renewable energy sources in the European power system is one of the main goals set by the European Union. In order to ease this integration, in recent years, Renewable Energy Communities (RECs) have been introduced that aim to increase the exploitation of renewable energy at the local level. This paper presents an Energy Management System (EMS) for an industrial microgrid owned and operated by a greentech company located in the north of Italy. The company is a member of an REC. The microgrid is made of interconnected busbars, integrating photovoltaic power plants, a fleet of electric vehicles, including company cars and delivery trucks supporting Vehicle-to-Grid (V2G), dedicated charging stations, and a centralized battery energy storage system. The industrial site includes two warehouses, an office building, and a connection to the external medium-voltage network. The EMS is designed to optimize the operation of the microgrid and minimize the operating costs related to the sale and purchase of energy from the external network. Furthermore, as the company is a member of an REC, the EMS must try to follow a desired power exchange profile with the grid, suggested by the REC manager, with the purpose of maximizing the energy that is shared within the community and incentivized. The results demonstrate that, when minimizing only costs, local self-consumption is favored, leading to a Self-Sufficiency Rate (SSR) of 65.37%. On the other hand, when only the adherence to the REC manager’s desired power exchange profile is considered in the objective function, the SSR decreases to 56.43%, net operating costs increase, and the energy shared within the REC is maximized.

Research on the application of a dynamic programming method based on geodesic distance for path planning in Robot-Assisted laser scanning measurement of Free-Form surfaces

The use of robots equipped with 3D laser line scanners is currently one of the mainstream geometric measurement methods for free-form surfaces. The key to this technology is to plan an effective measurement path based on the geometric features of the surface. In order to improve the measurement efficiency, a new path planning method for free-form surface measurement is proposed. First, an algorithm is developed to adaptively generate sample points of the surface to be measured based on the geodesic distance. Then, the geometric length of the scanning measurement path and the amplitude variation of the Euler angle are used as the objective function, and the optimal robot scanning measurement path is generated by minimising the objective function based on a dynamic programming algorithm. Finally, experiments show that the scanning measurement path generated based on the proposed method can effectively improve the measurement efficiency compared with existing research methods.

Comparative assessment of simulation-based and surrogate-based approaches to flowsheet optimization using dimensionality reduction

This work proposes a framework for simulation-based and surrogate-based reduced space Bayesian optimization of process flowsheets. The framework uses global sensitivity analysis for dimensionality reduction via the identification of critical process variables that contribute significantly to the variability of the objective function (e.g. productivity and operating costs). Both simulation- and surrogate-based algorithms are applied to a biopharmaceutical and a chemical process simulator for the production of plasmid DNA and dimethyl ether (DME), respectively. Their capabilities are assessed in terms of the trade-off between computational effectiveness and solution accuracy. Results indicate that simulation-based Bayesian optimization achieves better objective function values, while surrogate-based Bayesian optimization is more computationally effective.

Research on extreme obstacle–crossing performance and multi–objective optimization of tracked mobile robot

Stability of obstacle–crossing and structural optimization are important issues in the research of tracked mobile robots. In this paper, in order to fully understand the obstacle–surmounting ability of the robot, the relationship between the position of the center of gravity and the posture of the front and rear swing arms is analyzed. Based on the motion mechanism of the robot crossing obstacles, the geometric model and the dynamic model are established for the key states in the obstacle crossing process. Based on these models, a multi-objective optimization problem for the maximum obstacle–crossing height and minimum driving torque is established during the obstacle crossing process of the robot, which must meet geometric, slip, and stability constraints. To effectively handle the optimization problem of tracked mobile robots, an improved non–dominated sorting genetic algorithm with elite strategy version II based on adaptive genetic strategy (NSGA-II-AGS) is proposed in this paper. Some meaningful relationships between the objective function and the design variables are obtained through sensitivity analysis. Finally, the robot's obstacle-crossing ability was verified through virtual simulation and prototype experiments. These excellent performances enable the proposed NSGA-II-AGS to be qualified for dealing with the multi-objective optimization problem.

Shelter location–allocation problem for disaster evacuation planning: A simulation optimization approach

Immediately after a major earthquake strikes, a segment of the people in the disaster region must evacuate to relief shelters. If the evacuation proceeds in an efficient and safe manner, many lives can potentially be saved as the effects of secondary factors, e.g., secondary collapses, stampedes, and aftershocks, can largely be mitigated, while at the same time the people taking refuge can quickly gain access to vital relief goods. Formulating the best possible protocols for post-earthquake evacuation is a crucial, yet highly challenging problem considering the profound uncertainty which is present in the post-disaster environment, including the location and degree of infrastructural damage, the number and location of people who need to evacuate, the behavior and psychology of evacuees, etc. In this research, we formulate a two-stage stochastic evacuation simulation model capable of credibly accounting for these uncertainties. We develop a solution methodology to solve the shelter location–allocation problem represented by the two-stage model, finding the optimal location of shelters to be open as the first stage solution and the optimal assignment of evacuees to shelters as the second stage solution to minimize the total expected cost. The cost-based objective function consists of the cost of opening shelters, a deprivation cost component, the cost of trapped evacuees, and the cost of shelters exceeding capacity. In developing this framework, we collaborate with the National Science and Technology Center for Disaster Reduction (NCDR) in utilizing GIS-based probabilistic models of road damage. We apply the proposed framework to run an extensive numerical study and present various valuable observations and insights which can be gained.

Automated hybrid distillation sequence synthesis framework of mixed azeotropes and nonazeotropes

Distillation technology is a crucial, energy-intensive component of energy-supply systems, thereby motivating the improved design of distillation systems. This study investigated an automated framework for synthesizing hybrid distillation sequences, providing a systematic approach for the design and optimization of distillation sequences involving azeotropes. The proposed method separated mixed azeotropes and nonazeotropes by replacing the simple columns in the superstructure of a conventional distillation sequence with extractive distillation columns. The automated calculation of the superstructures was implemented by calling the external interface of the process simulator. The results of these calculations were used to formulate a mixed-integer linear programming problem that was optimized with the objective function of the total annualized cost (TAC). Finally, the optimal solution and all feasible alternatives were obtained. Moreover, the reliability and efficiency of the proposed framework were demonstrated through two case studies. The results show that the separation position of the azeotrope in the sequence and the presence or absence of heat integration significantly impact the TAC of the system. However, using an existing component versus an external component as an entrainer makes very little difference in the TAC for the optimal sequences.

A practical optimisation method for friction tuned mass dampers in multi-storey buildings subjected to earthquake excitations

To reduce lateral drifts, the parameters of friction tuned mass dampers (FTMDs) need to be “tuned up” during the design process, which can be a challenging task for multi-storey buildings subjected to real ground motions. To address this issue, this article proposes a practical particle swarm optimisation (PSO) algorithm to optimise simultaneously the parameters (frequency ratio and friction ratio) of FTMDs fitted on multi-degree of freedom (MDOF) systems and frames subjected to real seismic records. First, elastic MDOF systems with FTMDs were modelled in OpenSees software considering different number of storeys (5, 10 or 20), periods and stiffness distributions. Optimised frequency ratios and friction coefficients of FTMDs and the main structure response were then derived using an objective function that minimised the structures’ maximum average roof displacement. FTMDs with optimised parameters proved very effective at reducing average roof displacements of the examined MDOF structures by up to 39 %, but the magnitude of the reduction depended on the period of the structure. Subsequently, the effectiveness of FTMDs at controlling the response of inelastic steel moment-resisting frames (MRFs) was examined. For the examined structures and seismic records, the use of optimised TMDs led to marginally smaller (2 %) roof displacements when compared to FTMDs. Therefore, FTMDs can be as effective as TMDs devices but with added advantages of less susceptibility to aging and temperature variations. It is also shown that higher mode effects experienced by real MRFs can be captured accurately by equivalent MDOF systems (difference in responses <4 %), which can therefore simplify the design.

Green flexible production and intelligent factory building structure design based on improved ant colony algorithm

Traditional factory production has limitations such as energy waste, emission pollution, etc., and there are also certain unreasonable structural aspects. Therefore, this article proposes a method that can make the factory building structure more green, flexible, and intelligent. It is hoped that by improving the ant colony algorithm and applying it to factory design and production, the goal of reducing energy consumption, maximizing resource utilization, and reducing environmental impact can be achieved. Transforming the design problem of factory building structure into an optimization problem using ant colony algorithm, that is, finding the optimal path and layout. In this process, the ant colony algorithm simulates the behavior of ants by continuously iterating and updating the concentration of pheromones, in order to achieve the global optimal solution. In order to achieve the combination of green design principles and flexible production needs, corresponding constraint conditions and objective functions were introduced, taking into account the flexibility and adjustability within the factory to meet different production needs. Ant colony algorithm can provide the best structural design solution for factory buildings by optimizing search, combining green design principles and flexible production requirements, and promoting the development of green and sustainable factory buildings.

Monitoring of cementitious materials hydration using 2–2 cement-based piezoelectric transducers based on electromechanical impedance method

Monitoring cementitious materials hydration enhances construction efficiency by providing crucial information regarding the optimal timing for shoring removal and pre-stress transferring. In recent years, many scholars have utilized the lead zirconate titanate (PZT)-based electromechanical impedance method (EMI) to monitor the cementitious materials hydration process. However, conventional piezoelectric transducers are less compatible with cementitious materials. In addition, the currently commonly used statistical-based metrics lack a clear physical meaning. The 2–2 cement-based transducer (2−2), composed of multiple PZT elements, demonstrates excellent electromechanical performance and good compatibility with cementitious materials. In this study, the 2–2 was employed to monitor the cementitious materials hydration, and an electromechanical impedance model for describing the interaction between the 2–2 and the structure was presented. Taking into account both resonance frequency and peak value of the conductance signatures, an objective function was defined. Genetic algorithm (GA) was employed to minimize the disparity between theoretical and experimental results, thus extracting equivalent stiffness and damping. The empirical relationship between equivalent stiffness, damping, and compressive strength was established to predict the compressive strength. The experimental results show that for the same monitoring process, the normalized equivalent stiffness and damping extracted from different 2–2 sensors are in some agreement. It is more sensitive to hydration processes than statistical indicators. More importantly, the equivalent parameters have a clear physical meaning and can better portray the hydration process of the cementitious materials.

Fuzzy C-Means Algorithm Modification Based on Distance Measurement for River Water Quality

River water quality could be determined by understanding the capacity of pollutants in a water body. Fuzzy C-Means (FCM) is one of the fuzzy clustering methods for determining river water quality by measuring water quality parameters, that is, dissolved oxygen (DO) and total dissolved solids (TDS). The FCM algorithm is an effective fuzzy clustering algorithm for grouping data but often produces local and inconsistent optimal solutions due to the partition matrix's random initialisation process. Therefore, this study proposes to modify the FCM algorithm to be precise in the partition matrix initialisation process using several distance concepts. The purpose of the proposed algorithm modification is to get more consistent FCM clustering results and minimise stop iterations. The validation process for the clustering results uses the FCM algorithm, and the FCM modification algorithm uses three parameters, namely the Partition Coefficient Index (PCI), Partition Entropy Index (PEI) and Silhouette Score (SS). The experiments were conducted with three replications and using various distance concepts. The results showed that the number of iterations stopped in the FCM algorithm has different values for PCI, PEI, SS, and stop iterations and objective functions in each trial. On the contrary, the FCM modification algorithm has consistent PCI, PEI, and SS values, and the number of iterations stops with fewer iterations. Therefore, the modified algorithm for initialising the partition matrix can be used in the fuzzy C-means clustering algorithm.

Second-order necessary and sufficient optimality conditions for multiobjective interval-valued nonlinear programming

In this article, the second-order optimality conditions for nonlinear programming with multiple interval-valued objective functions (in brief, NPMIOF) are studied. In the first part, the second- and first-order necessary optimality conditions for some types of efficient solutions of NPMIOF are established under of Abadie second- and first-order constraint qualifications. In the next part, we investigate both primal and dual second-order sufficient optimality conditions. Our second-order necessary conditions enhance the previous results. The second-order sufficient optimality conditions are new.

An OGSM-based multi-objective optimization model for partner selection in fresh produce supply chain considering carbon emissions

Incorporating partner selection with environmental commitment is recognized as a crucial aspect of green development in fresh produce supply chains. This paper presents a multi-objective programming (MOP) model for partner selection considering carbon emissions based on the ‘Objectives, Goals, Strategies, Measures’ (OGSM) framework. The OGSM serves as a strategic scheme to identify the long-term green development vision of a supply chain, ensuring that the activities of supply chain partners are aligned with common goals. Driven by the OGSM design, the MOP model is formulated with three conflicting objective functions to maximize profit, minimize carbon emission, and maximize the quality of fresh produce. The model helps decision-makers address decisions on partner selection with determined logistics volumes, transportation modes and packaging types under carbon emission constraints. To tackle the NP-hard problem, we develop an effective hybrid meta-heuristic algorithm called ɛ-NSGA, which complements the advantages of the ɛ-constraint method and NSGA-II (Non-dominated Sorting Genetic Algorithm-II). The experimental results based on parameter calibration of the Taguchi method and the Kruskal–Wallis test demonstrate that the proposed ɛ-NSGA outperforms other four heuristic algorithms in terms of the performance indices of GD (Generational Distance), spacing, IGD (Inverted Generational Distance), and HV (Hyper Volume). The implementation of the OGSM-MOP together with ɛ-NSGA in a real-world apple supply chain illustrates the effectiveness and practicality of the model. Finally, managerial insights into the utilization of the model are discussed and suggestions for decision-making to fulfill corporate environmental obligations are provided.

Static Task Allocation Method for Multi-Machines in Cooperative Operations Combining OGFR-GA and MLW-Prim

Aiming at the operational efficiency of small farm machinery groups in cooperative operations in hilly areas, this study proposes a static task allocation model. This method combines the optimal gene fragment retention method based on a genetic algorithm (OGFR-GA) and the method for generating multi-loop weighted connected graphs based on Prim’s algorithm (MLW-Prim). The collaborative objective function for the harvester group was established by considering factors such as operation time, fuel consumption, and distance. The OGFR-GA was designed and applied multiple times to obtain several optimal gene fragments corresponding to the number of farm machines. These fragments were used as critical paths in the weighted connected graph generated based on farm machinery performance parameters and task parameters. The MLW-Prim method was proposed to construct this weighted connected graph and realize the static task allocation model for multi-machine cooperative operations. Simulation experiments showed that the model combining OGFR-GA and MLW-Prim achieved optimal values with fewer iterations, and reduced both group cost and cost variance compared to traditional algorithms. This method meets the static task allocation needs for multi-machine cooperative operations in agricultural production and can be integrated with intelligent operations in mountainous and hilly regions, laying a theoretical foundation for improving efficiency.

An automated optimized design of practical post-tensioned slabs

In this paper, an efficient methodology that could be implemented within any analysis software is developed to automate the design process of post-tensioned slabs. Optimization techniques are used to compute the necessary number of strands. The present methodology is based on computing an innovative influence matrix that contains the effect of each strand on stresses at a set of control points (points of peak stresses). This matrix will be used in the optimization avoiding reanalysis in each optimization iteration. The sum of the squares of the difference between the allowable stress and the computed stresses at all control points is considered an objective function. The optimization is then carried out under the constraints that the stresses at each control point should not exceed the allowable limit and to ensure that the number of strands is an integer value. Alternatively, the proposed methodology could be used as a value-engineering tool. Practical examples are presented to demonstrate the efficiency of the developed methodology.

When should lockdown be implemented? Devising cost-effective strategies for managing epidemics amid vaccine uncertainty.

During an infectious disease outbreak, public health policy makers are tasked with strategically implementing interventions whilst balancing competing objectives. To provide a quantitative framework that can be used to guide these decisions, it is helpful to devise a clear and specific objective function that can be evaluated to determine the optimal outbreak response. In this study, we have developed a mathematical modelling framework representing outbreaks of a novel emerging pathogen for which non-pharmaceutical interventions (NPIs) are imposed or removed based on thresholds for hospital occupancy. These thresholds are set at different levels to define four unique strategies for disease control. We illustrate that the optimal intervention strategy is contingent on the choice of objective function. Specifically, the optimal strategy depends on the extent to which policy makers prioritise reducing health costs due to infection over the costs associated with maintaining interventions. Motivated by the scenario early in the COVID-19 pandemic, we incorporate the development of a vaccine into our modelling framework and demonstrate that a policy maker's belief about when a vaccine will become available in future, and its eventual coverage (and/or effectiveness), affects the optimal strategy to adopt early in the outbreak. Furthermore, we show how uncertainty in these quantities can be accounted for when deciding which interventions to introduce. This research highlights the benefits of policy makers being explicit about the precise objectives of introducing interventions.

FOREIGN ECONOMIC ACTIVITY IN THE SYSTEM OF ACCOUNTING AND ANALYTICAL MANAGEMENT SUPPORT

The article decomposes the foreign economic activity of enterprise through the prism of accounting and analytical support of management. It has been determined that current trends in the development of the domestic and world economy, specificity and peculiarities of activities of individual business entities in the field of foreign economic relations, the need for further development of methodology and methods of accounting and analysis of foreign economic activity, taking into account the objectively arising needs for the development of information support for management, determine the relevance of decomposition of foreign economic activity in the system of accounting and analytical support for management. It is substantiated that the basis for the decomposition of foreign economic activity in the system of accounting and analytical support of management is the concept of the type of activity. The organisation of accounting of foreign economic activity at a particular enterprise is largely determined by the type of foreign economic activity inherent in this enterprise. In the process of production and economic activity, enterprises, as a rule, face several types of foreign economic operations simultaneously. The accounting and analytical aspects of management of foreign economic activity of enterprise are systematised. A model of decomposition of the foreign economic activity of enterprise has been developed. It is proposed that the model of decomposition of foreign economic activity of enterprise should include types of foreign economic activity and accounting and analytical aspects, grouped into blocks: key aspects; characteristics of types of foreign economic activity, organisational and legal conditions, purpose, directions and results of functioning, accounting approaches and basic analytical criteria, performance indicators, structural construction and functioning, etc. The developed model provides for the reflection and determination of performance results by types, subtypes, segments and other classification features of foreign economic activity in order to obtain analytical, detailed, transparent and relevant information based on financial, management and strategic accounting data. Formation of an integrated system of accounting and analysis of foreign economic activity is based on consideration of regulatory, contractual, structural, accounting, analytical, control and software and hardware support of foreign economic activity.

Skin Lesion Prediction and Classification Using Innovative Modified Long Short-Term Memory-Based Hybrid Optimization Algorithm

Identification of pigmented skin lesions is necessary for the detection of severe diseases associated with the skin organ, notably malignancy. Accurate skin cancer diagnosis can be improved with the use of image detection approaches and computer classification skills. Therefore, this research work plans to perform skin lesion prediction and classification using a novel deep learning methodology. Initially, the data related to the skin lesion are gathered from the ISIC dataset. After collecting the images, the pre-processing is performed using hair removal and filtering hair removed images via median filtering. These pre-processed images undergo segmentation process accomplished using the U-Net method. Next, the features are extracted from these segmented images with the help of color features, and texture features by GLCM and RGB histogram features. These extracted features undergo the prediction phase that is accomplished using the MLSTM model, in which the parameter optimization is done by the nature inspired novel hybrid metaheuristic algorithm referred as SC-STBO algorithm with the consideration of accuracy maximization and RMSE minimization as the major fitness for the objective function. If the predicted output is returned as the presence of skin lesion, the same novel MLSTM model classifies the final skin lesion output into seven types, such as Vascular Lesions, Melanocytic Nevi, Melanoma, Dermatofibroma, Benign Keratosis-like Lesions, BCC, and Actinic Keratoses, respectively. Seven groups of skin diseases can be identified early thanks to the suggested effort, which can then be tested and properly handled by medical professionals. With an accuracy of 0.9931, the recommended methodology clearly outperforms traditional techniques. Similarly, the suggested methodology clearly beats the conventional methods, with a recall of 0.9825.

Optimization of air refractive index based on dispersive interferometry

This study discusses the limitations of the multi-color method for air refractive index compensation and introduces the nonlinear objective refractivity optimization (NORO) to address these shortcomings. Utilizing a nonlinear objective function and the Davidon-Fletcher-Powel (DFP) optimization method, NORO provides precise, self-corrected geometric distance without the need for extensive environmental sensing or broad spectral coverage. Compared to the multi-color method, the NORO method reduces the minimum usable spectral range from 600 nm to 40 nm, achieving consistency with the empirical formula within 2.5 ppm using a 90 nm spectral range, significantly decreasing the dependence of algorithm accuracy on the spectral range. During a 4.5-hour long-term compensation, the relative residual compared to the empirical formula remains within 3 ppm, with a standard deviation of σ E = 9.4 × 10−7. Additionally, in long-distance measurements compared with the empirical formula, the NORO method demonstrates an agreement within 1.89 × 10−7 m for distances up to 12 m, without requiring environmental parameter sensing.