Decision Method Research Articles

Enhancing the usability range of acetylene in CI engines through split injection of high-reactive fuel under RCCI operation: Optimized operability using an MCDM approach

Reactivity Controlled Compression Ignition (RCCI) has emerged as a promising solution to address the challenges of advanced combustion techniques in diesel engines, balancing combustion control and emission reduction. This study investigates the impact of split-injection high-reactivity fuel (HRF) dosing in RCCI to enhance charge homogeneity at lower injection timing advancements. It systematically explores progressive advancements in HRF injection timing, distinguishing between main and pilot injections. The main injection varied from 20° bTDC to 70° bTDC, while the pilot injection, relative to the main injection, spans 25°-40° CA ahead with a 5° progression. As an LRF, Port-injected Acetylene supplements the process with variable premixed ratios. Further, using a multi-criteria decision-making method (AHP), the study optimizes operating conditions, proposing a main injection at 20° bTDC, a pilot injection 40° CA ahead, and 70 % LRF participation. This optimized condition yields significant benefits such as 32.99 % higher brake thermal efficiency (BTE), 160 % longer premixed duration, 18.65 % lower maximum temperature, 89.41 % lower NO emissions, 91.83 % lower hydrocarbon (HC) emissions, 91.24 % lower CO emissions and 97.20 % lower smoke opacity compared to conventional diesel combustion (CDC) operation. The results highlight the advantages of multiple fuel injections in RCCI, improving blending characteristics at lower injection angles.

Supplier performance assessment: Empirical evidence in Indonesian freight forwarding companies

This research aimed to assess the results of two vendors used by the company in the shipping process of export goods. Two leading suppliers for one similar activity had caused more difficulties in the monitoring and controlling activities of DHL Global Forwarding Indonesia. This research used qualitative and quantitative methods, with the Analytic Hierarchy Process decision-making method using 36 internal staff members as the sample. Through a qualitative calculation method by distributing questionnaires to the existing suppliers, namely Monang Sianipar Kargo and Andima Transportindo, it was found that the weighted score for Monang Sianipar Kargo was 22.84 and for Andima Transportindo was 10.66. Subcriteria and indicators should be prioritized in the criteria of Price and service, significantly to improve the performance of problematic suppliers. This research recommended using the Analytic Hierarchy Process for assessment since it facilitated the research development by the opinion of the company’s experts. Such a finding implied that a policy from the management was needed in the assessment of suppliers. As an implication, it was necessary to assess all suppliers cooperating with DHL Global Forwarding Indonesia by using actual data from the current month.

A behavior three-way decision approach under interval-valued triangular fuzzy numbers with application to the selection of additive manufacturing composites

Additive manufacturing composites, also recognized as three-dimensional (3D) printing composites, are highly anticipated for their potential to replace industrial materials due to the availability of multiple printing processes and optional materials. However, research gaps exist in cognitive deficiencies and psychological behaviors of decision-makers, as well as experimental error effects caused by material testing, resulting in material selection as a challenging issue. Therefore, this study proposes a novel behavior three-way decision model under the interval-valued triangular fuzzy number (IVTFN) to settle the selection issue of 3D printing composites. The research contributions are summarized as follows. First, the IVTFN is presented to account for the impacts of cognitive deficiency and experimental errors, based on which the concepts of information entropy and fuzzy measure are further developed to conduct the criterion weights. In addition, by integrating the prospect theory and regret theory, a framework for constructing the behavioral decision matrix is presented. Moreover, a novel behavior three-way decision model with the perspectives of objective and preference is proposed to classify the decision region. This study presents a comprehensive methodology integrating the three-way decision model and multi-criteria decision-making method to achieve both alternative ranking and alternative classifying. Finally, a research case of 3D printing composites reinforced by continuous hybrid fibers is adopted to illustrate the validity of the methodology. Comparative analysis and sensitivity analysis are also performed. This study offers valuable insights and tools for systematically tackling the 3D printing composite material selection issues.

Locating Emergency Stations Using Multi-Criteria Decision-Making (MCDM) Methods: Application of Ankara Province

An earthquake ranks first among natural disasters in Turkey, with a rate of 70%. Minimizing the effects of a disaster after it occurs is possible by establishing effective disaster management. Especially the earthquakes that occurred on February 6, 2023, showed how important it is to deliver aid to the disaster area. In this study, the problem of determining the location of emergency stations to be established in order to deliver aid to the disaster area in case of a disaster was examined. There are studies in the literature to determine the locations of emergency stations to be established in provincial centers. In this study, the locations of emergency stations to be established in the non-central districts of Ankara were tried to be determined using multi-criteria decision-making (MCDM) methods. In the study, firstly, the criteria that would be effective in the location selection of emergency stations were determined, and the weights of these criteria were calculated with the Pythagorean Fuzzy Analytical Hierarchy Process (PFAHP) method. In the second stage of the study, 16 districts outside the center of Ankara were compared with the Pythagorean Fuzzy Technique for Order Preference by Similarity To An Ideal Solution (PFTOPSIS) method, and the districts where emergency stations should be opened first were evaluated.

An Integrated Quantitative Method of Risk Analysis and Decision Making for Safety Manipulation of a Forest Firefighting Aircraft by Using Physical Models of Multi-Hazard Factors

Although firefighting aircrafts are effective for fighting forest fires, they are affected by a variety of risks related to forest fires during flight operations such as thermal radiation, temperature rise, decrease in air density, and changes in updraft aerodynamics. However, quantifying these influences poses a challenge. Our research reveals that safety standards predominantly hinge on technical indicators like flight height, speed, and angle of attack, which makes it possible to quantify the above effects. A general method framework for analysing firefighting flight safety criteria was constructed in this thesis. Using relevant physical models, such as heat radiation, temperature, air density, and updraft in a forest fire environment, safety criteria with an analytical form were established. The corresponding safety constraints of each criterion were calculated quantitatively, and the most stringent constraint was determined based on multiple factors to obtain a comprehensive safety constraint boundary composed of the critical altitude, stall speed, and critical angle of attack. Finally, the effectiveness of the method is verified in a simulation case. The research results show that the constrained boundary calculation method proposed in this study can provide a scientific basis for the formulation of technical specifications for forest aviation fire safety and aviation fire task planning.

FIXEL: TOPSIS-Driven Mobile Application for Roadside Assistance Provider Decision Making

Many drivers have faced the struggle of getting help when their vehicle breaks down on the road. This difficult situation is extremely stressful because there are limitations to seeking help, such as the service provider being too far away or the service provider's contact number being unavailable. The complication of deciding to choose a service provider is also occurring because of the eagerness to fulfil different kinds of aspects, such as the lowest price of the services and the shortest distance from the service provider. From the service provider’s perspective, it has become a challenge to get new customers when they are unfamiliar with their surroundings. Usually, the drivers will choose the most popular service provider, which may be far away from them. These issues sparked the motivation for this study, which aims to assist drivers and service providers through the mobile platform. One of the multi-criteria decision-making methods was employed to enhance the quality of choosing the right service provider based on the criteria involved, which are service availability, prices, distances and ratings. The result of this study would benefit drivers and service providers by improving decision-making, enhancing the customer experience, optimizing resource utilization and reducing cost.

Intelligent decision-making for TBM tunnelling control parameters using multi-objective optimization

In tunnel construction, tunnel boring machine (TBM) tunnelling typically relies on manual experience with sub-optimal control parameters, which can easily lead to inefficiency and high costs. This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multi-objective optimization (MOO). First, the effective TBM operation dataset is obtained through data preprocessing of the Songhua River (YS) tunnel project in China. Next, the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters (i.e. total thrust and cutterhead torque), rock mass classification, and hazard risks (i.e. tunnel collapse and shield jamming). Then, considering three optimal objectives, (i.e., penetration rate, rock-breaking energy consumption, and cutterhead hob wear), the MOO framework and corresponding mathematical expression are established. The Pareto optimal front is solved using DE-NSGA-II algorithm. Finally, the optimal control parameters (i.e., advance rate and cutterhead rotation speed) are obtained by the satisfactory solution determination criterion, which can balance construction safety and efficiency with satisfaction. Furthermore, the proposed method is validated through 50 cases of TBM tunnelling, showing promising potential of application.

Design, optimisation and evaluation of the S-CO2 Brayton cycle for marine low-speed engine flue gas

Five configurations of the S-CO2 Brayton cycle (SCBC) were constructed for flue gas waste heat recovery (WHR) of the marine low-speed engine HHM-6EX340EF on the EBSILON platform using the bench test data to save energy, reduce emissions and the efficient operation of ocean-going vessels. Experimental data from Sandia National Laboratories (SNL) was utilised to validate the accuracy of the model and the effect of Brayton cycle parameters on flue gas WHR for different configurations was analysed. The parameters of the SCRBC (S-CO2 recompression Brayton cycle) were optimised by combining the Neural Network Fitting (NNF) model, Multi-objective optimisation algorithms, and multi-criteria decision-making methods. Finally, the thermodynamic analysis of the low-speed engine was comprehensively evaluated by comparing five configurations of SCSBC (S-CO2 simple Brayton cycle), SCCBC (S-CO2 recuperative Brayton cycle), SCHBC (S-CO2 reheating Brayton cycle), SCIBC (S-CO2 intercooling Brayton cycle), and SCRBC. The optimised SCIBC configuration had the highest net recuperated work of 195.76 kW and the highest Brayton cycle efficiency of 20.13 %. The engine efficiency was improved by 0.82 %, 1.45 %, 1.78 %, 1.86 % and 1.68 %, the BSFC was decreased by 3.20 g/kW·h, 5.56 g/kW·h, 6.82 g/kW·h, 7.04 g/kW·h, and 6.43 g/kW·h, and the output power increased by 86.90 kW, 153.33 kW, 195.76 kW, 189.36 kW, and 178.14 kW, respectively. The maximum exergy loss of the cooler was 162.67 kW with an exergy loss efficiency of 14.54 % in the SCSBC configuration. Optimising the flue gas heat exchanger and cooler will further enhance the efficiency and reduce emissions. Research on the parameter and configuration optimisation of the SCBC for marine low-speed diesel engines can be extended to other low-speed engines.

Short-term high-efficiency decision-making method for tertiary voltage control considering short-term power fluctuations

Abstract With the continuous increase in grid-connected new energy sources and new types of intermittent loads, the short-term fluctuation amplitude of source-load power is continuously increasing, which increasingly affects the effectiveness of tertiary voltage control decisions. To address this issue, this paper proposes a short-term efficient decision-making method for tertiary voltage control considering short-term power fluctuations. Firstly, the decision-making cycle of tertiary voltage control is shortened, effectively enhancing its adaptability to short-term source-load power fluctuations. Secondly, to address the computational burden brought by the shortened decision cycle, this paper fully considers the temporal characteristics of short-term load fluctuations in practical power grid nodes and proposes two linearization methods for tertiary voltage control. Specifically, these methods include a three-point linear estimation method for small fluctuation periods and a sensitivity linearization optimization method for large fluctuation periods. By shortening the decision cycle and linearizing decision calculations, the efficiency of tertiary voltage control decision-making is greatly improved. Based on actual data from a provincial-level power grid in the northwest region of China, the effectiveness of the proposed methods is verified through simulation analysis.

Urban Competitiveness Assessment Using the Integration of the Global Power City Index and Multicriteria Decision-Making Methods

Urban Competitiveness Assessment Using the Integration of the Global Power City Index and Multicriteria Decision-Making Methods

Convolutional Neural Network-Based Lane-Change Strategy via Motion Image Representation for Automated and Connected Vehicles.

The lane-change decision-making module of automated and connected vehicles (ACVs) is one of the most crucial and challenging issues to be addressed. Motivated by human beings' underlying driving paradigm and the convolutional neural network's (CNN) dramatic capability of extracting features and learning strategies, this article proposes a CNN-based lane-change decision-making method via the dynamic motion image representation. Human drivers take proper driving maneuvers after they subconsciously construct the dynamic traffic scene representation in their brains, so this study first proposes the dynamic motion image representation method to reveal informative traffic situations in the motion-sensitive area (MSA), which provides a full view of surrounding cars. Then, this article develops a CNN model to extract the underlying features and learn driving policies from labeled datasets of MSA motion images. Besides, a safety-constrained layer is added to avoid vehicle collisions. We build a simulation platform based on the simulation of urban mobility (SUMO) to collect traffic datasets and test our proposed method. In addition, real-world traffic datasets are also involved to further investigate the proposed method's performance. The rule-based strategy and reinforcement learning (RL)-based method are used to compare with our approach. All results demonstrate that the proposed method performs lane-change decision-making much better than prevailing methods, which suggests our scheme has huge potential to accelerate the deployment of ACVs and is worth further study.

Evaluation of Community Regeneration Planning and Resilience Based on Fuzzy Group Decision-Making Method

Evaluation of Community Regeneration Planning and Resilience Based on Fuzzy Group Decision-Making Method

Verifying Welding Processes Selection for a Case Study Using an Analytic Hierarchical Process

This paper aims to develop a framework for differentiating the selection of welding processes using specialized knowledge-based expert systems, which are particularly useful in dealing with issues that require complex decision-making. A program was created and programmed using the process information maps (PRIMAs) matrix in Visual Basic Access for selecting welding processes in a study. The program is in two stages. In the first stage, it excludes non-candidate operations according to several criteria, the most important of which is the type of metal and its thickness. The second stage is arranging operations using multi-criteria methods for decision-making through research to resolve complicated problems involving multiple factors. The hierarchical analysis process (AHP) was used. It is considered one of the most frequently used multi-criteria decision-making methods. The selection of analytic hierarchical process. AHP criteria depend on experience and knowledge rather than specific data for selecting alternatives and determining weights. The methodology is straightforward, easy to comprehend, and applicable to various domains needing intricate decision-making. The program results are compared with those of a previously published case to choose the best welding processes for a study case, which is a home radiator, and results matching the candidate processes were discovered.

Multi-objective optimization of series-parallel system with mixed subsystems failure dependencies using NSGA-II and MOHH

In complex systems, failure dependencies play a crucial role in determining their overall performance. This paper explores the multi-objective optimization of series-parallel systems with mixed failure dependencies. By optimizing system cost and availability, the study aims to identify the most efficient redundancy and repair strategies. Two optimization algorithms, the non-dominated sorting genetic algorithm II (NSGA-II) and a novel multi-objective algorithm named the multi-objective hoopoe heuristic (MOHH), are utilized alongside constraint handling techniques to produce Pareto fronts. These fronts illustrate the trade-offs between cost and availability. Additionally, a fuzzy decision method is utilized to determine the best compromise solutions from each optimization technique. Comparing the results, NSGA-II consistently outperforms MOHH in providing better compromise solutions across five independent runs. However, MOHH demonstrates a better standard deviation in its performance.

An intelligent decision method of quality risk in electromagnet assembly process based on digital twin and q-rung dual hesitant fuzzy number

In order to solve the problem of online risk analysis of assembly process, an intelligent decision-making method of electromagnet assembly risk based on Digital Twin (DT) was proposed. Firstly, a DT model of electromagnet assembly process is constructed, and the potential failure modes are collected in real time at the physical layer. Secondly, in the virtual layer, real-time potential failure modes are obtained through information synchronization and transmitted to the application layer. Then, in the application layer, the optimized Failure Mode and Effects Analysis (FMEA) method is used to analyze the risk of failure mode, and the risk value of failure mode is fed back to the virtual layer. In the improved FMEA method, the q-Rung Dual Hesitant Fuzzy Number (q-RDHFN) is used as the evaluation language. The weight of experts is calculated by Bidirectional Projection Method (BPM), and the comprehensive weight of risk factors is calculated by Best and Worst Method (BWM) method, entropy weight method and Criteria Importance Through Intercriteria Correlation (CRITIC) method. Finally, the physical layer is adjusted to reduce the probability and risk of failure mode occurrence. An example of risk intelligent decision making is given to verify the objectivity and practicability of the new method.

Transformer-Based Sequence Modeling Short Answer Assessment Framework

Automated subjective assessment presents a significant challenge due to the complex nature of human language and reasoning characterized by semantic variability, subjectivity, language ambiguity, and judgment levels. Unlike objective exams, subjective assessments involve diverse answers, posing difficulties in automated scoring. The paper proposes a novel approach that integrates advanced natural language processing (NLP) techniques with principled grading methods to address this challenge. Combining Transformer-based Sequence Language Modeling with sophisticated grading mechanisms aims to develop more accurate and efficient automatic grading systems for subjective assessments in education. The proposed approach consists of three main phases: Content Summarization: Relevant sentences are extracted using self-attention mechanisms, enabling the system to effectively summarize the content of the responses. Key Term Identification and Comparison: Key terms are identified within the responses and treated as overt tags. These tags are then compared to reference keys using cross-attention mechanisms, allowing for a nuanced evaluation of the response content. Grading Process: Responses are graded using a weighted multi-criteria decision method, which assesses various quality aspects and assigns partial scores accordingly. Experimental results on the SQUAD dataset demonstrate the approach’s effectiveness, achieving an impressive F-score of 86%. Furthermore, significant improvements in metrics like ROUGE, BLEU, and METEOR scores were observed, validating the efficacy of the proposed approach in automating subjective assessment tasks. Doi: 10.28991/HIJ-2024-05-03-06 Full Text: PDF

An optimization framework for achieving optimal hydrocyclone's performance aligning with decision-makers' preferences

Previous hydrocyclone optimization often neglected interactions among key performance objectives, which limits hydrocyclone wide applications to meet increasingly diverse industry demands. This study proposes an optimization framework to identify the most suitable hydrocyclone design and operating conditions with conflicting key performance objectives. An advanced multi-objective evolutionary algorithm (PICEA-g) is employed to generate Pareto-optimal solutions that capture trade-offs among multiple conflicting objectives. A novel data-driven predictive algorithm, INFO-ELM, is introduced to establish nonlinear relationships between key variables and performance objectives, thereby accelerating the search for Pareto-optimal solutions by PICEA-g. Furthermore, a multi-criteria decision-making method (TOPSIS) is utilized to determine the optimal solution based on decision-makers' preferences, ensuring consistency between preference information and decision outcomes. The framework's effectiveness is validated across various separation scenarios using two decision-making strategies. This study offers a comprehensive approach to address trade-offs in hydrocyclone optimization, widening its application in diverse separation scenarios.

Multi-position two-phase person-position matching decision-making with intermediary participation

With the rapid development in society, intermediaries play an increasingly important role in person-position matching decision-making. To obtain reasonable enterprise-intermediary matching and enterprise-candidate matching schemes under multiple positions, preferences of enterprises, headhunting companies and candidates should be fully considered from a two-phase perspective. Thus, a two-sided matching decision-making method under the probabilistic linguistic environment is proposed to solve the multi-position two-phase person-position matching problem. First, related theories of probabilistic linguistic term sets and two-sided matching are given. On this basis, the problem of multi-position two-phase person-position matching under the probabilistic linguistic environment is described. The decision-making process is divided into the following two phases: In the first phase, satisfactions of enterprises and headhunting companies are calculated considering subject expectations based on prospect theory. Then, considering the maximization of satisfactions of two-sided subjects, the many-to-many two-sided matching model between enterprises and headhunting companies is established and solved to obtain the optimal many-to-many matching scheme. In the second phase, according to evaluation matrices and expectation matrices, satisfactions of enterprises and candidates under multiple positions are calculated considering intermediary evaluations. Then, considering the maximization of satisfactions of enterprises and candidates, one-to-many matching models and one-to-one stable matching models under multiple positions are developed and solved to obtain the optimal person-position matching schemes under multiple positions. Finally, an example of the person-position matching with intermediary participation is used to illustrate the feasibility and effectiveness of the proposed method.

Sub-watershed Prioritization Based on Watershed Hydrological Security Using Different Multi-Criteria Decision-Making Methods

Sub-watershed Prioritization Based on Watershed Hydrological Security Using Different Multi-Criteria Decision-Making Methods

Analysis of risk influential factors of marine pilots during the embarkation and disembarkation

Due to the complex geographical conditions within the port waters, it is necessary to take pilotage operations. The embarkation and disembarkation (E&D) of marine pilots is the riskiest phase of pilotage work, with accidents occurring frequently. In order to examine the key Risk Influential Factors (RIFs) of E&D accident of marine pilots, in this study, a novel complex network model of RIFs of marine pilots during the E&D process is proposed. Firstly, based on the E&D accident investigation report of marine pilots, the causal chain of E&D accidents is extracted, and the RIFs leading to E&D accidents are identified. Secondly, a complex network model of RIFs of marine pilots during the E&D is constructed based on the complex networks theory, and the network reliability is verified. Finally, by comprehensive use of topology characteristics analysis, robustness analysis and module analysis, the key RIFs of pilot E&D accidents are identified. The research results show that pilot and management factors have important impact on pilot safety, ‘insufficient pilot ladder strength' is the most critical RIF. It should be focused on RIFs with high connectivity between modules and within modules, and the risk evolution can be blocked through the control of those RIFs. Abbreviations: BNs: Bayesian Networks; CDM: Critical Decision Method; DEMATEL: Decision-making Trial and Evaluation Laboratory; E&D: Embarkation and Disembarkation; FSA: Formal Safety Assessment; RIFs: Risk Influential Factors; RPD: Recognition-Primed Decision; SHERPA: Systematic Human Error Reduction and Prediction Approach; SOLAS: International Convention for the Safety of Life at Sea; VTS: Vessel Traffic Services; TECHR: Technique for Early Consideration of Human Reliability.