Decision Method Research Articles

A City-Level Integrated Case Base Design for Systemic Disaster Risk Management

AbstractUrban disaster risks show multi-stage evolution and interconnected coupling features. Under time pressure, case-based reasoning (CBR) has emerged as a critical method for risk management decision making. Case-based reasoning tackles target case problems by leveraging solutions from similar historical cases. However, the current case base is inadequate for storing systemic risk cases, thus impeding CBR efficacy. This article presents a city-level integrated case base with a nested cross structure to facilitate the use of CBR in systemic risk management. It comprises a multi-layer vertical dimension and a multi-scale horizontal dimension. The vertical dimension is optimized to a four-layer (environment-hazard-object-aftermath) risk scenario classification system with taxonomy and fuzzy clustering analysis. The horizontal dimension is improved to a three-scale (network-chain-pair) risk association mode using event chain theory and association analysis. Hazard acts as the pivotal link between the two dimensions. An illustrative example displays the use process of the proposed case base, along with a discussion of its CBR-supported applications. Through the digital transformation, the suggested case base can serve government decision making with CBR, enhancing the city’s capability to reduce systemic risk.

REDUCE—A Tool Supporting Inconsistencies Reduction in the Decision-Making Process

This paper presents REDUCE, a free online tool designed to support decision-making processes by addressing inconsistency in multiplicative pairwise comparison (PC) matrices, a key element of many multi-criteria decision-making (MCDM) methods, including the analytic hierarchy process (AHP). AHP relies on pairwise comparisons to assign weights to decision criteria or alternatives, but human-generated PC matrices often exhibit inconsistencies. Consistency is evaluated using Saaty’s consistency ratio ( CR ), where a value below 0.10 is considered acceptable. Higher inconsistency levels necessitate matrix corrections, which are challenging if the original expert is unavailable or revision constraints exist. REDUCE autonomously reduces inconsistency in PC matrices using two different algorithms that require no expert intervention. The tool accommodates different PC matrices, enabling users to specify the desired CR threshold (e.g., CR≤0.10) and select the algorithm for adjustment. It ensures the resulting matrix is consistent while preserving the original preference structure to the greatest extent possible. Additionally, REDUCE calculates weights for the compared entities, making it a valuable tool for applications of AHP and related methodologies. Quantitative evaluations demonstrate that REDUCE can improve matrices with high inconsistency (e.g., CR=0.25) to acceptable levels (e.g., CR=0.08) while retaining up to 95% of the original preference integrity, depending on the chosen algorithm. By addressing the accessibility gap for small and medium enterprises (SMEs) that lack resources for costly decision-making software or expert consultants, REDUCE facilitates broader adoption of MCDM tools. This work highlights the potential of REDUCE to enhance decision-making reliability and accessibility in resource-constrained environments.

A Two-Stage Multi-attribute Group Decision-Making Model Based on Regret Theory and Three-Way Decision Method with R-Numbers

A Two-Stage Multi-attribute Group Decision-Making Model Based on Regret Theory and Three-Way Decision Method with R-Numbers

A Hesitation-Associated Multi-Attribute Decision-Making Method Based on Generalized Interval-Valued Hesitation Fuzzy Weighted Heronian Averaging Operator

In multi-attribute decision making (MADM), complex situations often arise where decision attributes are interval-valued hesitant fuzzy numbers (IVHFNs) and the attributes are interrelated. Traditional decision-making methods may be ineffective in handling such cases, highlighting the practical importance of seeking more effective approaches. Therefore, finding a more effective decision-making approach has important practical significance. By combining the theories of Archimedean S-norms and T-norms, we innovatively propose a multi-attribute decision-making method based on the generalized interval-valued hesitant fuzzy weighted Heronian mean (GIVHFWHM) operator to address the aforementioned issues. Initially, based on the operational laws of IVHFNs and the Heronian mean (HM) operator, we introduce the generalized interval-valued hesitant fuzzy Heronian mean (GIVHFHM) operator and the GIVHFWHM operator. We then examine properties of the GIVHFHM operator, including permutation invariance, idempotency, monotonicity, boundedness, and parameter symmetry. A multi-attribute decision-making model is constructed based on the GIVHFWHM operator. Finally, we validate the proposed model through numerical experiments in MADM. The results demonstrate that the new decision-making method, based on the GIVHFWHM operator, is feasible and effective in handling multi-attribute decision problems involving IVHFNs with interdependent attributes. This approach provides a novel perspective and method for solving MADM problems under interval-valued hesitant fuzzy conditions with interdependent attributes. It enriches the theoretical framework of multi-attribute hesitant decision models and expands the application of the Heronian mean operator within interval-valued hesitant fuzzy environments. This methodology assists decision makers in making more accurate decisions within complex decision-making contexts, enhancing both the scientific rigor and reliability of decision-making processes.

Group decision-making method based on Pythagorean fuzzy rough numbers

Group decision-making method based on Pythagorean fuzzy rough numbers

Topology Optimization, Part Orientation, and Symmetry Operations as Elements of a Framework for Design and Production Planning Process in Additive Manufacturing L-PBF Technology

This paper investigates the possibility of the application of different optimization techniques in the design and production planning phase in the metal additive manufacturing process, specifically laser powder bed fusion (L-PBF) additive technology. This technology has a significant market share and belongs to the group of mature additive technology for the production of end-use metal parts. In the application of this technology, there is a space for additional cost/time reduction by simultaneously optimizing topology structure and part orientations. Simultaneous optimization reduces the production time and, indirectly, the cost of parts production, which is the goal of effective process planning. The novelty in this paper is the comparison of the part orientation solutions defined by the software algorithm and the experienced operator, where the optimal result was selected from the aspect of time and production costs. A feature recognition method together with symmetry operations in the part orientation process were also examined. A framework for the optimal additive manufacturing planning process has been proposed. This framework consists of design and production planning phases, within which there are several other activities: the redesign of the part, topological optimization, the creation of alternative build orientations (ABOs), and, as a final step, the selection of the optimal build orientation (OBO) using the multi-criteria decision method (MCDM). The results obtained after the MCDM hybrid method application clearly indicated that simultaneous topology optimization and part orientation has significant influence on the cost and time of the additive manufacturing process. The paper also proposed a further research direction that should take into consideration the mechanical as well as geometric, dimensioning and tolerances (GDT) characteristics of the part during the process of ABOs and OBO, as well as the uses of symmetry in these fields.

Using TOPSIS Method for Solving Multi-Objective Optimization of a Two-stage Helical Gearbox with First Stage Double Gear-sets

In order to build a two-stage helical gearbox (THG) with first stage double gear-sets (FSDG), the multi-criteria decision-making (MCDM) method is introduced in this research as a new approach to solving the multi-objective optimization problem (MOOP). The study's objective is to deter-mine the best primary design factors that simultaneously satisfy two goals: maximal gearbox ef-ficiency and minimal gearbox mass. To that end, the first stage's gear ratio and the first and sec-ond stages' CWFW were chosen as the three main design elements. Furthermore, two distinct goals were analyzed: the lowest gearbox mass and the highest gearbox efficiency. Additionally, the MOOP is carried out in two steps: phase 1 solves the single-objective optimization problem to close the gap between variable levels, and phase 2 solves the MOOP to determine the optimal primary design factors. Furthermore, the TOPSIS approach was selected to address the MOOP problem. For the first time, an MCDM technique is used to solve the MOOP of a helical gearbox with FSDG. Additionally, this is the first investigation of the total gearbox efficiency, which includes the power losses during idle. When designing the gearbox, the optimal values for three crucial design parameters were ascertained using the study's results.

Neutrosophic fuzzy decision-making framework for site selection

These days, the area of interest based on canteen location selection in the university campus is a prominent topic. This research work directly affects students' convenience, accessibility and the total campus experience. An ideal canteen site not only provides good food to the students and staff but also takes care of their health by providing a healthy environment. In this paper, we choose various criteria and sub-criteria to calculate the best site for a canteen in the university campus. Location, space and layout, infrastructure and Utilities, safety and compliance and environmental factors as criteria with their corresponding sub-criteria are chosen for this work. Here, the data sets used are provided by decision makers in linguistic words and then converted into crisp numbers. The criteria and sub-criteria weights are considered by a popular multi criteria decision-making (MCDM) method named Criteria Importance Through Intercriteria Correlation, i.e., CRITIC method. Therefore, the ideal location for the canteen in the university campus was determined by applying the complex proportional assessment method (COPRAS), which is basically a ranking method of MCDM. And finally, sensitivity analysis was conducted to make sure the outcomes were flexible and stable.

A novel methodology for assessing resources and environmental carrying capacity with emphasis on ecosystem services: A multi-disciplinary approach to sustainable urban planning

Due to increased human activities, urban areas face challenges in maintaining their environment and natural resources in support of development. As a result, it is necessary to plan according to the availability of resources and the state of the environment to achieve sustainable development. For this purpose, Carrying capacity (CC) can be used to assess the sustainability of cities as complex Social-Ecological Systems (SES). By emphasizing Ecosystem Services (ESs) and regional thresholds, this paper proposes a novel methodology to evaluate Urban Resources and Environmental Carrying Capacity (URECC). At first, an integrated simulation model is developed by combining an ecosystem services simulation model, a surface and ground water simulation model, a priority-based water allocating model, and a demands (i.e., water, food, and energy) estimation model. To evaluate URECC, various measurement indicators are developed and combined with a multi-stage decision-making (MSDM) method. Also, the decision-making sequences of management strategies and climate change scenarios are used to evaluate changes in the future. Based on the results, in the two base stages (2013–2016) and (2017–2020), without applying strategies, the average value of pressure-support, distance, and composite indices changed by +1.5%, −75.6%, and −66.7%, respectively, indicating a decrease in the CC. The future period (i.e., 2021–2040) was divided into four-year decision-making stages. According to the results, with the implementation of top sequences of strategies, the average values of pressure-support, distance, and composite indices would surpass those of maintaining the current trend by 49.4%, 123.5%, and 63.7%, respectively. In conclusion, management actions such as the development of semi-centralized and decentralized wastewater treatment networks, the simultaneous expansion of green space and built-up land, the implementation of artificial recharge, the improvement of irrigation efficiency, the management of urban water consumption, and the use of vegetation with low to medium water demands are effective for strengthening the URECC.

A Mobility Handover Decision Method Based on Multi-Topology

With the emergence of new applications in mobile networks, users demand higher network stability and lower data transmission delays. When the network address of a mobile user changes, the data transmission path in the wired network may need to be switched to maintain service continuity. Traditional mobility support methods typically rely on a single switching path for all mobile data flows. However, if this path cannot meet the requirements of all the flows, it may lead to network congestion or a decline in user experience. To overcome this limitation, this paper proposes a mobility handover decision method based on multi-topology. It enables the dynamic allocation of mobile data flows across different switching paths within multiple logical topologies. The method models a multi-topology selection problem aimed at minimizing average packet transmission delay and packet loss rate, while considering network conditions and the Quality of Service (QoS) requirements for each flow. By solving the dual problem of the original optimization, a near-optimal solution is achieved. The proposed scheme and algorithm were implemented and tested using the Mininet network simulator. Results show that the proposed approach achieves low average packet transmission delay, low average packet loss rate, and high throughput, compared to traditional single-path switching methods and existing multipath routing methods.

An Ancillary Decision-Making Method for Hydropower Station Failure Handling Based on Case-Based Reasoning and Knowledge Graph

This paper proposes an ancillary decision-making method for hydropower station failure handling based on knowledge graph and case-based reasoning. The proposed method assists the power station dispatcher to carry out accurate and timely failure handling after an accident. First, the main steps of case-based reasoning are introduced. The main difficulties and their corresponding solutions when applying case-based reasoning to hydropower station failure handling are discussed. Then, an ancillary decision-making method for hydropower station failure handling is proposed. Key steps such as case construction, case retrieval, and case revision are designed. In the proposed method, each case is represented in the form of multiple knowledge graphs, i.e., a system topology knowledge graph, a dispatching regulation knowledge graph, and an accident case knowledge graph. The flexibility of case knowledge extraction, management, and retrieval is greatly enhanced. Finally, the simulation analysis is carried out on a large-scale cascade hydropower station in China. The simulation results show that the proposed method can provide reasonable and reliable ancillary decision-making for the power station dispatcher in the failure handling process, and greatly improve the intelligence level of emergency management at a hydropower station.

Research on Determining the Weights of Key Influencing Factors Based on Multi-Grained Binary Semantics

To effectively address the complexity of the environment, information uncertainty, and variability among decision-makers in the event of an enterprise emergency, a multi-granularity binary semantic-based emergency decision-making method is proposed. Decision-makers use preferred multi-granularity non-uniform linguistic scales combined with binary semantics to represent the evaluation information of key influencing factors. Secondly, the weights were determined based on the proposed method. Finally, the proposed method’s effectiveness is validated using a case study of a fire incident in a chemical company.

Expansion Direction Selection of the Coal-to-Olefin Industrial Chain in Inner Mongolia from the Value Chain Perspective

Inner Mongolia is a key region for China’s clean energy production and a demonstration base for modern coal chemical industry production. However, modern coal chemical industry development in this region faces many problems, such as complex downstream product structures, low value-added products, and severe environmental pollution. With limited future coal allowance in the coal chemical industry, the scarce available coal will likely be allocated to fields with strong product competitiveness to enhance industrial chain value. Given this, the Inner Mongolia coal-to-olefin industrial chain is selected to explore its strategic expansion directions. An improved value chain accounting method is proposed to account for the value of each segment of the coal-to-olefin industrial chain in Inner Mongolia from 2011 to 2022. The improved value chain accounting method is then combined with system dynamics to construct a model for selecting the expansion directions of the coal-to-olefin industrial chain based on the value chain perspective. Finally, the differences in the value of each future expansion direction are analyzed through scenario simulation and comparison. The most important result is that the higher the extension of the coal-to-olefin industry chain in Inner Mongolia, the higher the value of the industry chain. Carbon punishment is the most important factor affecting the value of the industry chain, and enterprises and governments should increase investment in innovation and renewable energy. Accordingly, this study provides a decision-making method for selecting the optimal expansion direction of the modern coal chemical industrial chain in Inner Mongolia.

Roughness of Fuzzy Sets by Binary Relations Induced from Soft Relations with Application in Decision Making

Abstract Binary relations are significant in mathematics and information sciences. Meanwhile, fuzzy set (FS), rough set (RSs) and soft set (SS) are efficient mathematical schemes for dealing with uncertain and vague information in real-world circumstances. This article explores rough approximations of a FS based on induced binary relations from a soft relation that are given in terms of foresets and aftersets. Initially, two pairs of rough approximations founded on induced binary relations are analyzed, and their distinctive features are reviewed. In addition, two variants of fuzzy topologies are constructed by induced reflexive relations. Meanwhile, several similarity relations associated with induced reflexive relations are also discussed. Also, we outline a method to decision-making (DM) within the invented approach. The decision steps and the algorithms of the decision method are also specified. The legitimacy of the decision method is verified by a practical illustration. A detailed comparative analysis further authenticates the viability and efficacy of the projected method over existing decision-making techniques.

A generalized TODIM approach based on information measurement and consensus building model in probabilistic hesitant fuzzy environment

Information measurement and decision-making methods are two important aspects in the application of probabilistic hesitant fuzzy elements (PHFEs), and these issues are crucial for improving decision quality and enhancing the applicability of PHFSs. This paper focuses on the entropy measure and score function of PHFEs, and then proposes a generalized TOIDM method based on information measurement and consensus building. In this contribution, we develop a novel non-probabilistic entropy measure by two non-fuzzy elements and a novel score function based on sigmoid function. The novel non-probabilistic entropy of PHFEs can comprehensively reflect the dynamic changes of fuzzy uncertainty and hesitant uncertainty, and provide a new axiomatic framework. The novel score function of PHFEs can synthesize multiple information effectively with strong integrity. In the sequel, the two proposed information measurement methods are compared with existing methods to further verify the validity of the developed methods. Eventually a consensus building model is constructed based on the proposed information measurement method, and then a generalized TODIM approach based on information measurement and consensus building is proposed for probabilistic hesitant fuzzy environment. The developed approach is demonstrated with an emergency rescue planning decision-making problem. Sensitivity analysis and comparative analysis are employed to verify the validity and stability of the approach and information measures proposed in this paper. Based on the findings of this study, we conclude that the proposed methods effectively enrich the existing information measurement methods and decision-making methods in the field of probabilistic hesitant fuzzy.

Multi-objective optimization and multi-attribute decision-making support for optimal operation of multi stakeholder integrated energy systems

To efficiently tackle the optimal operation problem of multi-stakeholder integrated energy systems (IESs), this paper develops a multi-objective optimization and multi-attribute decision-making support method. Mathematically, The optimal operation of IESs interconnected with distributed district heating and cooling units (DHCs) via the power grid and gas network, can be formulated as a multi-objective optimization problem considering both economic, reliability and environment-friendly objectives with numerous constraints of each energy stakeholder. Firstly, a multi-objective group search optimizer with probabilistic operator and chaotic local search (MPGSO) is proposed to balance global and local optimality during the random search iteration. The MPGSO utilizes a crowding probabilistic operator to select producers to explore areas with higher potential but less crowding and reduce the number of fitness function calculations. Moreover, a new parameter selection strategy based on chaotic sequences with limited computational complexity is adopted to escape the local optimal solutions. Consequently, a set of superior Pareto-optimal fronts could be obtained by the MPGSO. Subsequently, a multi-attribute decision-making support method based on the interval evidential reasoning (IER) approach is used to determine a final optimal solution from the Pareto-optimal solutions, taking multiple attributes of each stakeholder into consideration. To verify the effectiveness of the MPGSO, the DTLZ suite of benchmark problems are tested compared with the original GSOMP, NSGA-II and SPEA2. Additionally, simulation studies are conducted on a modified IEEE 30-bus system connected with distributed DHCs and a 15-node gas network to verify the proposed approch. The quality of the obtained Pareto-optimal solutions is assessed using a set of criteria, including hypervolume (HV), generational distance (GD), and Spacing index, among others. Simulation results show that the number of Pareto-optimal solutions (NPS) of MPGSO are higher by about 32.6 %-62.1%, computation time (CT) are lower by about 2.94 %-46.1 % compared with other algorithms. Besides, to further evaluate the performance of the proposed approach in addressing larger-scale issues, the study employs the modified IEEE 118-bus system of greater magnitude. The proposed MPGSO algorithm effectively handles multi-objective and non-convex optimization problems with Pareto sets in terms of better convergence and distributivity.

Decision Support Framework for Sustainable and Fire Resilient Buildings (SAFR-B)

AbstractBuildings of all types are increasingly becoming complex ‘systems of systems.’ They are subject to evolving societal objectives, new and innovative materials, and in many countries, regulatory ecosystems are having difficulty keeping pace with rapidly changing societal, environmental and technological changes. Two evolving objectives that are stimulating changes to buildings and communities are the desire for a more environmentally sustainable built environment and the need to become more resilient to the many increasingly hazardous impacts of climate change. Unfortunately, in some building designs these objectives are in conflict. As a first step toward a more integrated, holistic tool to aid in the design of sustainable and fire resilient buildings (SAFR-B), this paper develops and applies a first-order decision framework for a midrise apartment building. The SAFR-B framework is built on an analysis of design and regulatory objectives for fire safety and sustainability for buildings, and of risk and decision methods that can support design decisions. It makes use of risk indexing and the analytical hierarchy process (AHP), with initial scoring and weighting of attributes and strategies derived from international experts in the field of fire safety and sustainability through a Delphi process.

A compromise-based MADM approach for prioritizing failures: Integrating the RADAR method within the FMEA framework

Multi-Attribute Decision-Making (MADM) methods are essential in decision-making processes, particularly in solving problems related to ranking and classifying alternatives. Among the MADM methods frequently utilized in the literature for ranking alternatives are distance-based or compromise-based methods. These methods have been widely applied for decades, with ongoing development leading to new approaches. One such approach is RAnking, based on the Distances And Range (RADAR) method. This novel distance-based method evaluates alternatives by considering their distance relative to the best and worst alternative values for a given criterion and the range between them. This paper applies the RADAR method to rank failure modes identified through a standard Failure Modes and Effects Analysis (FMEA) in an automotive industry company that produces rubber and plastic products. The results obtained from the RADAR method are compared with those derived from the traditional Risk Priority Number (RPN) approach. The comparison demonstrates that the RADAR method provides more distinct rankings, reducing the occurrence of ties between alternatives and thus offering a more nuanced and reliable decision-making tool in the context of failure mode prioritization.

Multi-Criteria Decision Making (MCDM) Approach for Identifying Optimal Solar Farm Locations: A Multi-Technique Comparative Analysis

This study explores the spatial suitability for solar farm placement using Geographic Information System (GIS) techniques (ArcGIS Pro 3.0.2) coupled with Muti-Criteria Decision-Making (MCDM) methods. Nine evaluation criteria significantly impacting solar farm placement in Erbil, Iraq were selected, and the Analytic Hierarchy Process (AHP) was utilized to assign proper weights to these criteria. Suitability maps were derived and classified using three MCDM methods: Weighted Overlay (WO), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and SAW (Simple Additive Weighting) to establish comparisons. Discrepancies were noticeable between the outcomes of these methods, with distinctions observable across most categories. The validation process illustrated superior performance by SAW model, closely followed by TOPSIS model, while the weighted overlay model showed significant differences. The southeastern region of Erbil governorate was identified as the most suitable area for solar farm construction due to favorable criteria values and potential for future expansion. This approach massively facilitates locating and evaluating ideal areas for solar farm construction, thereby minimizing the time, financial resources, and efforts dedicated towards this endeavor.

A three-way decision with prospect-regret theory under Pythagorean fuzzy environments

As science and technology continue to advance at a rapid pace, people are facing increasingly complex data, and research on uncertain information technology has become a hot field. Compared to intuitionistic fuzzy sets, Pythagorean fuzzy sets are more tolerant and flexible in representing uncertain information. Therefore, this article mainly focuses on the study of three-way decisions based on Pythagorean fuzzy β covering. First, considering the disadvantages of other methods in comparing the magnitudes of Pythagorean fuzzy numbers, we propose the concept of the ideal positive degree in a Pythagorean fuzzy environment and determine a method for calculating conditional probabilities based on this newly defined ideal positive degree. Second, regret theory and prospect theory are combined to obtain a relative utility function. Then, based on the aforementioned conditional probabilities and relative utility function, a three-way multi-attribute decision-making method based on the Pythagorean fuzzy environment is established. Finally, the three-way multi-attribute decision-making method is utilized to solve the problem of selecting the optimal choice for rural revitalization industrial projects. Through experimental comparison and analysis, the effectiveness and reliability of this method are verified.