Selection Problem Research Articles

A heuristic application of systems reliability optimization in supplier selection problem of a make-to-order supply chain

A heuristic application of systems reliability optimization in supplier selection problem of a make-to-order supply chain

Dynamic Supplier Selection and Its Optimal Strategy Considering Full Truck Load and Fuzzy Demand using Fuzzy Expected Value-Based Programming

This article proposes a linear integer optimization model incorporating fuzzy parameters to find the optimal solution for a dynamic supplier selection problem with uncertain demand. The uncertain demand value is represented using a fuzzy variable. A fuzzy expected value-based linear optimization solver is used to address the optimization problem, to minimize the total cost under fuzzy demand values. Several computational experiments were conducted to evaluate and analyze the model. The results show that the proposed model effectively identifies the optimal suppliers for each product. Additionally, the model determines the optimal purchase volumes for each product type from the selected suppliers, leading to the minimal total expected cost.

Study on Spatial Scale Selection Problem: Taking Port Spatial Expression as Example

Spatial scale is a key factor, which affects the accuracy of spatial expression and further influences the spatial planning of a research area. In order to help improve the efficiency and accuracy of optimal scale selection for all sizes of research areas, a universal two-layer theoretical framework for optimal scale selection was proposed in this paper. Port area was taken as an example to systematically clarify the application of the proposed framework, and the scale selection model for port spatial expression was established. Least-squares-based mean change point analysis was introduced into the model, and the concept of a comprehensive change point was proposed to form the criterion for optimal port scale selection. First, an appropriate scale domain was preliminarily determined by the upper scale selection model. Then, the lower scale selection model determined the final appropriate scale domain and took the corresponding scale of a minimum comprehensive change point as the final optimal scale for port spatial expression. Finally, a port area in Qingdao in eastern China was taken to verify the feasibility of the proposed model, and the optimal scale was suggested to be 14 m. The proposed framework in this paper helps ensure the accuracy of spatial expression and reduce spatial data redundancy, and it can provide the methodological references for planners to better spatialize a research area, which will guarantee the subsequent spatial planning work.

Comprehensive evaluation of water quality grades based on improved CRITIC and multidimensional connection cloud combination

AbstractThis study proposes a comprehensive water quality assessment method for nine major plateau lakes in Yunnan Province, based on an improved CRITIC method combined with a multidimensional connectivity cloud model. Key water quality monitoring indicators (NH3‐N, COD, TP, TN, permanganate index, DO, pH) were selected, and the weights were determined using the improved CRITIC method, highlighting the impact of NH3‐N, COD, and TP. These weights were then integrated with a multidimensional connectivity cloud model to classify lake water quality levels. The results indicated the following water quality grades for the lakes: Dianchi (III), Fuxian Lake (I), Erhai (II), Xingyun Lake (I), Qilu Lake (II), Yilong Lake (II), Lugu Lake (I), Yangzonghai (II), and Chenghai (II). Compared to five conventional methods, the proposed approach better addresses the issues of fuzziness, randomness, and discreteness in water quality indicators, avoiding the boundary selection problems inherent in traditional methods. By combining the improved CRITIC method with a multidimensional connectivity cloud model, the study achieves more precise and reliable evaluations through objective weighting and comprehensive consideration of multiple indicators. This method offers a more accurate reflection of lake water quality conditions and provides a scientific basis for water quality management and decision‐making, demonstrating significant potential for application in the field of environmental science.

Artificial rabbits optimization algorithm with automatically DBSCAN clustering algorithm to similarity agent update for features selection problems

Artificial rabbits optimization algorithm with automatically DBSCAN clustering algorithm to similarity agent update for features selection problems

Fermatean fuzzy distance and Sugeno–Weber operators-based SPC-MARCOS approach for sustainable supplier evaluation in the healthcare supply chain

The present work proposes a new decision support tool for assessing the sustainable suppliers in the healthcare supply chain. For this purpose, the classical Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) model is integrated with the Sugeno–Weber weighted averaging operators, modified symmetry point of criterion (SPC) model, rank sum (RS) tool and Fermatean fuzzy sets (FFSs), and named as the ‘FF-SPC-RS-MARCOS’ framework. The developed model firstly determines the decision experts’ weights through RS model. Second, novel Sugeno–Weber weighted operators are introduced to combine the experts’ opinions. Third, a unified weighting procedure is presented based on the combination of modified SPC approach for objective weight and RS method for subjective weight of attributes. To this aim, a novel distance measure is introduced for FFSs and further applied to compute the distance between aggregated Fermatean fuzzy numbers and symmetry point value of an attribute in the modified SPC approach. Further, a hybrid FF-SPC-RS-MARCOS approach is proposed to tackle the decision-making problems on FFSs setting. To elucidate the efficacy of the developed method, it is applied to a case study of sustainable supplier selection problem in the healthcare supply chain. The paper further conducts sensitivity investigation and comparison with existent approaches to test the stability and robustness of the ranking outcomes. This study shows how the proposed MARCOS method in combination with SPC and RS models can be used to prioritize the alternative suppliers in the healthcare supply chain. The introduced work provides a new methodology, which can help the practitioners and academics to evaluate suppliers with uncertain information and can also be employed to other areas facing similar types of decision-making problems.

Assessing cognitive impairment in chronic pain: a cross-sectional study with healthy controls

Purpose The aim of this study is to clarify inconsistencies in the literature regarding the neuropsychological impact of chronic pain and determine how pain catastrophizing and pain intensity may explain cognitive impairment. Methods This cross-sectional study involved 30 participants with chronic primary musculoskeletal pain and 30 healthy controls without pain. The instruments used were the Pain Catastrophizing Scale, the Visual Analogue Scale, the Beck Anxiety Inventory, the Beck Depression Inventory–II, the Wisconsin Card Sorting Test, the Stroop Test, and the Working Memory Index of the Wechsler Adult Intelligence Scale. Results Chronic pain patients showed slight cognitive impairments in selective attention, sustained attention, working memory, problem solving, planning, abstract reasoning, inhibition, and resistance to interference. The data also indicate that higher levels of pain catastrophizing and pain intensity were independently associated with greater cognitive impairment in patients with pain, specifically in attention and executive functioning. Additionally, the interaction between these pain-related variables predicted further cognitive impairment. Conclusions This research has contributed to establishing the neuropsychological profile of patients with chronic primary musculoskeletal pain and reinforces evidence of the impact of chronic pain on cognition. These findings may help guide the design of programs aimed at improving cognitive performance.

A Novel Snow Leopard Optimization for High-Dimensional Feature Selection Problems

To address the limitations of traditional optimization methods in achieving high accuracy in high-dimensional problems, this paper introduces the snow leopard optimization (SLO) algorithm. SLO is a novel meta-heuristic approach inspired by the territorial behaviors of snow leopards. By emulating strategies such as territory delineation, neighborhood relocation, and dispute mechanisms, SLO achieves a balance between exploration and exploitation, to navigate vast and complex search spaces. The algorithm’s performance was evaluated using the CEC2017 benchmark and high-dimensional genetic data feature selection tasks, demonstrating SLO’s competitive advantage in solving high-dimensional optimization problems. In the CEC2017 experiments, SLO ranked first in the Friedman test, outperforming several well-known algorithms, including ETBBPSO, ARBBPSO, HCOA, AVOA, WOA, SSA, and HHO. The effective application of SLO in high-dimensional genetic data feature selection further highlights its adaptability and practical utility, marking significant progress in the field of high-dimensional optimization and feature selection.

A Heterogeneous Multi-Satellite Dynamic Mission Planning Method Based on Metaheuristic Algorithms

The increasing demand for satellite communication necessitates efficient resource management, especially as next-generation high-throughput satellites (HTSs) face challenges in optimizing user connections. This paper presents a novel method that integrates a discretely improved Particle Swarm Optimization (PSO) algorithm with a dynamic task management framework to enhance satellite resource allocation efficiency. The PSO algorithm is adapted for discrete selection problems to maximize a fitness function based on user priority, user preference, and capacity satisfaction, thus ensuring accurate user–satellite matching. A main loop function iteratively updates user data and connection statuses, thus achieving continuous optimization of satellite connections. Through simulation studies, we validated the effectiveness of this method under dynamically changing user demands, demonstrating that the Discrete PSO algorithm significantly outperforms Simulated Annealing (SA) and the Genetic Algorithm (GA) in user satisfaction, maintaining levels above 0.996 even under high demand. Additionally, it effectively prioritizes high-demand users, ensuring their satisfaction remains above 0.95. Overall, our method enhances the management of daily communication tasks, significantly improving service quality and user satisfaction.

Isotonic subgroup selection

Abstract Given a sample of covariate–response pairs, we consider the subgroup selection problem of identifying a subset of the covariate domain where the regression function exceeds a predetermined threshold. We introduce a computationally feasible approach for subgroup selection in the context of multivariate isotonic regression based on martingale tests and multiple testing procedures for logically structured hypotheses. Our proposed procedure satisfies a non-asymptotic, uniform Type I error rate guarantee with power that attains the minimax optimal rate up to poly-logarithmic factors. Extensions cover classification, isotonic quantile regression, and heterogeneous treatment effect settings. Numerical studies on both simulated and real data confirm the practical effectiveness of our proposal, which is implemented in the R package ISS.

The Efficiency Assessment of the Blockchain Networks Adaptive Algorithm as Part of a Multi-Consensus System on Communication Networks

The relevance of the approaches and solutions proposed in the work is due to the desire for distributed computing and decentralization of system modules using blockchain technology in order to increase the autonomy, security and independence of system components. The implementation of blockchain technology for decentralization is hampered by the lack of flexibility in terms of the algorithm for making a single decision in the asynchronous system ‒ consensus, which affects the amount of generated network traffic and the requirements for the hardware component. Due to the changing nature of the transmitted traffic, the load on the communication channel, the time of day of traffic transmission, the equipment, and the network protocols used, the development of a single universal consensus algorithm is not possible, since it depends on a very wide range of variable and specific parameters characteristic of different tasks. The introduction of adaptation mechanisms to current network conditions and a change in the consensus algorithm without losing information and with a satisfactory level of delays would provide sufficient flexibility for further implementation in existing telecommunication systems. The purpose of this paper is to evaluate the effectiveness of the proposed adaptive algorithm for choosing a consensus for blockchain networks.The essence of the presented algorithm is to change the consensus algorithm on a section of the blockchain network upon reaching certain network conditions, which allows regulating the amount of generated load on the communication network to reduce the loss of transaction blocks and the subsequent delay in their processing. The proposed efficiency assessment model is based on mathematical modeling methods, differential equation analysis, queueing theory and network graphs. The analysis of the results showed the efficiency of the proposed model when comparing the results of the analytical calculation and the experiment. The scientific novelty of the proposed approach is to developing an adaptive consensus selection algorithm, as opposed to developing a universal consensus algorithm, and the efficiency assessment model of the proposed algorithm on the communication network, taking into account a number of parameters characterizing the devices and the section of the communication network.The theoretical significance is in the universality of the proposed efficiency assessment model for calculating the synchronization speed of all nodes of the blockchain network with specified network parameters.The practical significance of the proposed algorithm and efficiency assessment method lies in the formation of new approaches and opportunities for integrating blockchain technology into modern communication networks, abstracting from the problem of consensus selection in changing conditions of the communication network section.

Modified Metaheuristics Optimization for Cyberbullying Detection on Online Data Science Platform

Online harassment detection faces significant challenges due to its expansive reach and anonymity. Addressing this issue demands effective detection mechanisms capable of processing vast data streams and adapting to evolving online language. Leveraging advancements in artificial intelligence, we propose a novel approach grounded in natural language processing (NLP) and metaheuristic algorithms. Our methodology integrates term frequency-inverse document frequency (TF-IDF) encoding and the AdaBoost algorithm for classification. To tackle the NP-hard problem of hyperparameter selection, we introduce a modified crayfish optimization algorithm (COA), termed GI-COA. This paper represents a pioneering effort in utilizing metaheuristic algorithms for hyperparameter selection in harassment detection models. Through experimentation, we demonstrate the efficacy of our approach in fostering a safer online environment. The best performing optimize models demonstrate and accuracy exceeding 77%.

Leveraging enhanced egret swarm optimization algorithm and artificial intelligence-driven prompt strategies for portfolio selection

In the financial field, constructing efficient investment portfolios is a focal point of research, encompassing asset selection and optimization of asset allocation. With the advancements in Large Language Models (LLMs), generative Artificial Intelligence (AI) tools have showcased capabilities never seen before. However, the black-box nature of these tools renders their outputs difficult to interpret directly, often necessitating iterative fine-tuning to align with users’ expected outcomes. This study presents a structured prompt framework specifically designed for stock selection, aiming to provide direct and interpretable stock-selecting tools for investors of various levels. By creating representative scenarios and combining them into different cases for experimentation, we can explore how the construction of prompts influences the responses generated by generative AI tools. Additionally, this paper proposes a novel algorithm that combines the Nonlinear-Activated Beetle Antennae Search strategy with the Egret Swarm Optimization Algorithm (NBESOA) to address the Mean-Variance Portfolio Selection problem with Transaction Costs and Cardinality Constraints (MVPS-TCCC), utilizing real stock market data to construct portfolios based on generative AI tools recommendations. Simulation results indicate that, compared to other algorithms, NBESOA prefers optimizing portfolio configurations to achieve the highest Sharpe Ratio with the strictest constraints, bringing the outcomes closer to the portfolio’s efficient frontier.

A novel importance-guided particle swarm optimization based on MLP for solving large-scale feature selection problems

Feature selection is a crucial data preprocessing technique that effectively reduces the dataset size and enhances the performance of machine learning models. Evolutionary computation (EC) based feature selection has become one of the most important parts of feature selection methods. However, the performance of existing EC methods significantly decrease when dealing with datasets with thousands of dimensions. To address this issue, this paper proposes a novel method called importance-guided particle swarm optimization based on MLP (IGPSO) for feature selection. IGPSO utilizes a two stage trained neural network to learn a feature importance vector, which is then used as a guiding factor for population initialization and evolution. In the two stage of learning, the positive samples are used to learn the importance of useful features while the negative samples are used to identify the invalid features. Then the importance vector is generated combining the two category information. Finally, it is used to replace the acceleration factors and inertia weight in original binary PSO, which makes the individual acceleration factor and social acceleration factor are positively correlated with the importance values, while the inertia weight is negatively correlated with the importance value. Further more, IGPSO uses the flip probability to update the individuals. Experimental results on 24 datasets demonstrate that compared to other state-of-the-art algorithms, IGPSO can significantly reduce the number of features while maintaining satisfactory classification accuracy, thus achieving high-quality feature selection effects. In particular, compared with other state-of-the-art algorithms, there is an average reduction of 0.1 in the fitness value and an average increase of 6.7% in classification accuracy on large-scale datasets.

A new probabilistic linguistic decision-making process based on PL-BWM and improved three-way TODIM methods

Probabilistic linguistic term sets (PLTSs) provide a flexible tool to express linguistic preferences, which allow decision-makers to label linguistic information with different probabilities. In this paper, a method based on a PLTS is proposed to address multi-criteria decision-making problems (MCDM). We develop the theory of PLTSs and put forward a novel best–worst method (BWM), termed PL-BWM, based on PLTS. Our method fully reflects the preference information of decision-makers and accurately provides the importance level of the criteria. The combined weight of the criteria is obtained by merging PL-BWM-based subjective weights and similarity minimization-based objective weights. Upon introducing a three-way decision system to improve the TODIM method, a novel three-way TODIM method is proposed and showcased on an optimal new energy vehicle selection problem. The effectiveness and accuracy of the proposed method are verified by sensitivity analysis and comparative analysis. Our approach paves the way for new developments in solving MCDM problems and for novel applications in otherwise difficult ranking problems.

Comparative Sensitivity Analysis in Composite Material Selection: Evaluating OAT and COMSAM Methods in Multi-criteria Decision-making

The decision-making process is critical across various fields, influencing essential aspects like performance, safety, and sustainability. In engineering and materials science, selecting suitable materials is a complex process involving multiple interdependent criteria, which impact the reliability and lifecycle of a design. Traditional sensitivity analysis methods, such as the one-at-a-time (OAT) approach, assess the effects of individual parameter changes but often fail to capture the cumulative impact of simultaneous modifications. It can limit the effectiveness of decision tools in real-world scenarios where multiple factors vary concurrently. This study utilizes the Comprehensive Sensitivity Analysis Method (COMSAM) to address limitations in traditional sensitivity analysis by modeling simultaneous parameter adjustments across multiple criteria. Specifically, the study aims to validate COMSAM’s effectiveness in the practical problem of composite material selection, comparing it with the traditional OAT approach within a multi-criteria decision-making (MCDM) framework. By integrating both sensitivity analysis techniques with the Characteristic Objects Method (COMET), this research ensures robust evaluation and mitigates vulnerabilities like the rank reversal paradox. The findings demonstrate that COMSAM provides enhanced insights into the impact of parameter interdependencies, offering a more resilient foundation for decision-making in high-stakes environments where material properties and performance are paramount.

Multi-objective model for electric vehicle charging station location selection problem for a sustainable transportation infrastructure

The transportation industry mostly depends on conventional vehicles, leading to significant adverse effects on the environment. The widespread usage of electric vehicles can be seen as a relief for this problem. However, the success of electric vehicles largely depends on the availability and proper deployment of charging station infrastructure. It is crucial for cities to strategically select suitable locations for charging stations with adequate capacity levels to promote sustainable and environmentally-friendly transportation options. Hence, in this study, a multi-objective model is proposed for the electric vehicle charging station location selection and capacity allocation problem. The model aims to maximize customer satisfaction, minimize total risk, and minimize costs as key objective functions. To manage the demand effectively, the region of interest is divided into grids. The proposed multi-objective model is applied to the European side of Istanbul and solved by using AUGMECON2 technique. Finally, computational analyses are presented based on scenarios including different demand values. These analyses provide valuable insights into the effectiveness of the proposed model and its implications for achieving sustainable transportation in Istanbul.

Improved sparrow algorithm to optimize kernel extreme learning machines for lithium battery status of health estimation on incremental capacity curve

Abstract The advancement of new energy vehicles and energy storage devices has increasingly demanded a more accurate state of health estimation for lithium batteries. Data-driven models are gradually widely used but still have the problems of long iteration time, low accuracy, and unreliable selection of feature factors. Thus, to promote the application of data-driven models in estimating the health state of lithium batteries, this paper proposes a multi-method improved sparrow algorithm optimized kernel extreme learning machine framework for lithium battery health state estimation. To improve the speed and accuracy issues of the data-driven model, this paper selects the kernel extreme learning machine as the network model and the improved sparrow algorithm using chaotic mapping and self-designed exponential weight factors as the optimization algorithm for automatic parameter search to achieve high accuracy and speed. Secondly, to strengthen the reliability of data feature factor selection, multiple feature factors are selected in the incremental capacity curve to improve the stability of feature factors. Finally, the rapidity and reliability of the proposed model on the SOH prediction of lithium batteries are verified by experiments, which provides an effective way to manage lithium batteries healthily.

Metode Simple Additive Weighting (SAW) pada Pemilihan Jenis Lampu Penerang Jalan Berbasis Photovoltaic

The simple additive weighting method is a decision support system method for various decision selection problems. Many research topics use the SAW method as a reference method in a decision support system. The street lighting control system is very useful for residents of the village of Karanganyar, Paiton, Probolinggo. This lighting is the main spotlight for residents' activities at the village office. Such as August competitions, routine monthly recitations, competitions to commemorate Isra' Mi'raj and the Prophet's birthday. There are many types of lamps in stores. The criteria for lamps are that they have high light illumination, long-lasting light, large bulb size, so that they can help village residents carry out activities at night. This lamp also uses photovoltaic as an additional source of electrical energy so it can save electricity costs to PLN. The solar panel measures 550 x 360 x 25 mm, 20 volts and has a peak power of 20 watts

A simheuristic for project portfolio optimization combining individual project risk, scheduling effects, interruptions, and project risk correlations

This paper introduces a simheuristic method to the Project Portfolio Selection Problem, designed to maximize the net present value of the portfolio while considering uncertain costs, schedules, interruptions, and inter-project risk correlations. The novel approach combines techniques from Monte Carlo simulation, critical path analysis, queuing theory, and optimization, integrating baseline schedules, project-level uncertainties, budgetary constraints, and risk correlations in a single model. A computational experiment is conducted on a realistic set of ten candidate projects and validated respect to the deterministic version of the problem, demonstrating its ability to select near optimal portfolio proposals with varying combinations of risk and net present value. The findings highlight the significant impact of factors such as contingency reserve allocation policies, operational interruptions, and project risk correlations on portfolio decisions, constituting a helpful framework for the decision-makers at portfolio level.