Stochastic Multi-criteria Decision Research Articles

Stochastic multi-criteria decision-making for scheduling of wind–photovoltaic–hydropower systems

Abstract The decision-making process of wind–photovoltaic–hydropower systems involves knowledge from many fields. Influenced by the knowledge level of the decision-maker and the attribute information of the scheme set, there exists a certain uncertainty in the indicator weights. In view of this, this paper proposes a stochastic multi-criteria decision-making framework for scheduling of wind–photovoltaic–hydropower systems, which overcomes the difficulty of uncertainty in indicator weights or even completely unknown information about indicator weights at the time of decision-making. The Stochastic Multi-criteria Acceptability Analysis (SMAA) theory and the VIKOR model are introduced, and the proposed SMAA–VIKOR model makes the indicator weight space explicit. The study shows that the proposed SMAA–VIKOR model can overcome the obstacle of decision-makers’ lack of information on indicator weights. The ranking acceptability indicators calculated by the model show a more obvious trend of advantages and disadvantages, which gives full confidence to the decision-making group to formulate a plan to be implemented. It breaks through the bottleneck of group decision-making, which is difficult to make effective decisions due to the condition of incomplete information, and enriches the library of stochastic multi-criteria decision-making methods for the scientific formulation of scheduling schemes of wind–photovoltaic–hydropower systems under uncertainty conditions.

Repurposing existing skeletal spatial structure (SkS) system designs using the Field Information Modeling (FIM) framework for generative decision-support in future construction projects

Skeletal spatial structure (SkS) systems are modular systems which have shown promise to support mass customization, and sustainability in construction. SkS have been used extensively in the reconstruction efforts since World War II, particularly to build geometrically flexible and free-form structures. By employing advanced digital engineering and construction practices, the existing SkS designs may be repurposed to generate new optimal designs that satisfy current construction demands of contemporary societies. To this end, this study investigated the application of point cloud processing using the Field Information Modeling (FIM) framework for the digital documentation and generative redesign of existing SkS systems. Three new algorithms were proposed to (i) expand FIM to include generative decision-support; (ii) generate as-built building information modeling (BIM) for SkS; and (iii) modularize SkS designs with repeating patterns for optimal production and supply chain management. These algorithms incorporated a host of new AI-inspired methods, including support vector machine (SVM) for decision support; Bayesian optimization for neighborhood definition; Bayesian Gaussian mixture clustering for modularization; and Monte Carlo stochastic multi-criteria decision making (MCDM) for selection of the top Pareto front solutions obtained by the non-dominant sorting Genetic Algorithm (NSGA II). The algorithms were tested and validated on four real-world point cloud datasets to solve two generative modeling problems, namely, engineering design optimization and facility location optimization. It was observed that the proposed Bayesian neighborhood definition outperformed particle swarm and uniform sampling by 34% and 27%, respectively. The proposed SVM-based linear feature detection outperformed k-means and spectral clustering by 56% and 9%, respectively. Finally, the NSGA II algorithm combined with the stochastic MCDM produced diverse “top four” solutions based on project-specific criteria. The results indicate promise for future utilization of the framework to produce training datasets for generative adversarial networks that generate new designs based only on stakeholder requirements.

Endogenous and exogenous performance sources in Asian Banking: A hybrid stochastic Multi-Criteria Decision-Making approach based on sign decomposition and transfer entropy

Endogenous and exogenous performance sources in Asian Banking: A hybrid stochastic Multi-Criteria Decision-Making approach based on sign decomposition and transfer entropy

Stochastic multi-criteria decision making framework based on SMAA-VIKOR for reservoir flood control operation

ABSTRACT In reservoir flood control operation, candidate reservoir operation alternatives are evaluated, ranked and selected by a multi-criteria decision making (MCDM) method. However, deterministic methods cannot handle MCDM problems where uncertainties exist in both criteria performance values (PVs) and criteria weights (CWs). To solve the issue, a SMAA-VIKOR model integrating stochastic multicriteria acceptability analysis (SMAA) theory with the Viekriterijumsko Kompromisno Rangiranje (VIKOR) method is established based on Aydogan and Ozmen (2017). Moreover, we conduct a comprehensive evaluation of the model’s decision making results, including risk assessment and sensitivity analysis. The case study applied to a flood control system in Dadu River in China indicates that the SMAA-VIKOR model can quickly identify the optimal alternative that meets the subjective preferences of decision makers, and has remarkable advantages compared to the traditional SMAA-2 model and the deterministic VIKOR method, which can provide theoretical support for making a robust reservoir release decision.

A Novel Multi-Criteria Decision-Making Framework in Electrical Utilities Based on Gray Number Approach

Given the current trend of reviving the power system, which is considered by competitive markets, the privatization of the power system is forcing them to develop the necessary decision-making policies from a technical and economic point of view to improve their asset management practices. Reliability-centered maintenance is an efficient process to consider these two important aspects, i.e. technical and economic ones, when performing maintenance optimization. This paper proposes a new technique to solve the actual stochastic Multi-Criteria Decision-Making (MCDM) problems with uncertain weight information using a combination of Stochastic Multi-Criteria Acceptability Analysis (SMAA) and Elimination Et Choice Translating Reality (ELECTREIII) methods combined with gray system theory. In maintenance planning, gray system theory is used to determine the specific types of power system components that should receive the most attention. Then, the optimal maintenance strategy of every critical component is determined by recognizing the lowest costs associated with various strategies. The suggested framework demonstrates its relevance and efficacy for actual asset management optimizations in electric power systems, as demonstrated in the IEEE 14-bus test system.

Exploration of Normal Stochastic Multicriteria Decision-Making and Legal Dilemma in Project Investment

The purpose is to help enterprise managers make more accurate, scientific, and objective decisions on the optimal supplier in project investment, improve investment return and overall efficiency, and reduce investment risk. Following an introduction of normal stochastic multicriteria decision-making, a normal stochastic multicriteria decision-making method is proposed based on Bayesian Network- (BN-) Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to help investors select suppliers. First, product cost, product quality, and product service are selected as evaluation indexes, and then, a supplier evaluation model is built based on the BN concept for expert evaluation. Second, the evaluation information is integrated. Based on the TOPSIS normal stochastic multicriteria decision-making method, the optimal supplier is selected under the comparison of the fit degree between each supplier and the best and worst solution. Finally, the optimal solution is determined and verified by a simulation example. The results show that in the problem of supplier selection in the retail industry, the BN model can truly provide feedback the product quality, cost, and service situation of each supplier and fully consider their behavior diversity. The normal stochastic multicriteria decision-making method based on TOPSIS can select the suppliers that are most in line with the investors’ development goals, especially in the face of good and bad suppliers. BN-based TOPSIS normal stochastic multicriteria decision-making method has strong logic and efficiency, is easy to operate, and has high practical value. Furthermore, the relevant laws of project investment decision-making are discussed through consultation on the relevant literature, thus providing help for the construction of relevant laws in the future.

A Stochastic Multi-Criteria Decision-Making Algorithm for Dynamic Load Prioritization in Grid-Interactive Efficient Buildings

Abstract Increasing deployment of advanced sensing, controls, and communication infrastructure enables buildings to provide services to the power grid, leading to the concept of grid-interactive efficient buildings. Since occupant activities and preferences primarily drive the availability and operational flexibility of building devices, there is a critical need to develop occupant-centric approaches that prioritize devices for providing grid services, while maintaining the desired end-use quality of service. In this paper, we present a decision-making framework that facilitates a building owner/operator to effectively prioritize loads for curtailment service under uncertainties, while minimizing any adverse impact on the occupants. The proposed framework uses a stochastic (Markov) model to represent the probabilistic behavior of device usage from power consumption data, and a load prioritization algorithm that dynamically ranks building loads using a stochastic multi-criteria decision-making algorithm. The proposed load prioritization framework is illustrated via numerical simulations in a residential building use-case, including plug-loads, air-conditioners, and plug-in electric vehicle chargers, in the context of load curtailment as a grid service. Suitable metrics are proposed to evaluate the closed-loop performance of the proposed prioritization algorithm under various scenarios and design choices. Scalability of the proposed algorithm is established via computational analysis, while time-series plots are used for intuitive explanation of the ranking choices.

Uncertainty propagation and risk analysis oriented stochastic multi-criteria decision making for unconventional water resources management

In real-world, managers and decision makers (DMs) of unconventional water resources (UWR) such as stormwater, reclaimed and flood water resources often face various uncertainties and work in changing environment. These uncertainties and changing factors derive from imprecise and uncertain information as well as subjective decision uncertainty associated with UWR management. Consequently, in multi-criteria decision making (MCDM) and assessment for UWR management, there exist uncertainties on criteria weights (CWs) and performance values (PVs). Additionally, qualitative criteria in multi-criteria system are often difficult to quantify precisely, making conventional MCDM models difficult to handle. To this end, considering uncertain PVs and CWs, integrated stochastic MCDM framework is developed for solving UWR management where quantitative and qualitative criteria are mixed. The framework contains: 1) novel stochastic multi-criteria acceptability analysis (SMAA) model based on modified grey relational analysis (GRA) and bidirectional projection (BP); 2) SMAA-Ordinal (SMAA-O) model for handling qualitative criteria; 3) coordinated CWs to compromise conflict of multiple criteria and simulate potential uncertainty; 4) decision-making (DM) risk quantification by risk analysis index. The framework is demonstrated by two cases of reclaimed water utilization (RWU) assessment and flood water control (FWC). Results show that framework can effectively handle uncertain information and qualitative criteria quantification. Parameter significance is analyzed to disclose its impact on DM uncertainty. Effectiveness analysis is conducted to demonstrate superiority of novel SMAA-GRBP coupled SMAA-O. Results indicate that framework provides feasible way for understanding stochastic essence of DM process, uncertainty propagation, probabilistic ranking information and releasing robust decisions with quantified decision risk at hand.

Evaluating alternative energy carriers in ferry transportation using a stochastic multi-criteria decision analysis approach

As part of ship acquisition decision making, owners and key stakeholders need to evaluate ship design and technology solutions across economic and environmental criteria in addition to safety and other regulatory requirements. Changes in technology, market, fiscal and other regulatory conditions may furthermore significantly influence value retention over the investment period. Multi-criteria decision making (MCDM) provides structure and clarity in otherwise complex decision problems, allowing decision makers to identify promising courses of action in a holistic manner. Combined with stochastic evaluation techniques, MCDM may facilitate problem exploration and learning in situations where outcomes are uncertain. In this article, we combine two variants of “Stochastic Multicriteria Acceptability Analysis” (SMAA) with “Technique for Ordering of Preference by Similarity to Ideal Solution” (TOPSIS) to evaluate the use of biofuels, natural gas and electricity on Norwegian ferry crossings. We analyze uncertain criteria values for environmental impacts, costs, fuel access and public acceptance across seven ferry combinations for a crossing. Criteria weighting is performed deterministically and stochastically to extract and interpret the conditions under which alternatives perform well or poorly. The case study shows that all-electric propulsion is preferable, while plug-in hybrid solutions with natural gas also give a robust performance. The approach is beneficial in handling multiple, uncertain performance metrics and provides a transparent and holistic foundation for evaluating marine emission reduction technology options in ship acquisition decision contexts.

Stochastic multi-criteria decision making based on stepwise weight information for real-time reservoir operation

Reservoir operation is a typical combination of engineering and non-engineering measures, and plays an important role in sustainable water resources management. However, reservoir operators always work in uncertain and changing environments, and face various uncertainties arising from criteria weights (CWs) and performance values (PVs), which hinder the application of multi-criteria decision making (MCDM) methods in real-time reservoir operations. To address this issue, this paper proposes a stochastic MCDM framework based on stepwise weight information. The framework consists of five sub-modules/models: (1) formulate a stochastic MCDM problem and determine the stochastic decision matrix; (2) a weight aggregation method based on the game theory allied with feasible weight space is proposed to resolve conflict among multi-stakeholders and estimate the uncertainty of CWs; (3) a novel SMAA-FOM model, i.e., a combination of stochastic multi-criteria acceptability analysis (SMAA) and fuzzy optimization model (FOM), is developed to simulate uncertainties existed in PVs and CWs simultaneously and produces several descriptive measures for robust decision-making; (4) a risk assessment model is used to quantitatively evaluate the robustness and risk of decision-making; (5) a multi-stage decision-making procedure is proposed that enables a stochastic MCDM problem to be solved step by step within acceptable decision-making risks. The effectiveness and superiority of the proposed framework are verified via a multi-objective reservoir operation case study. The results indicate that the framework helps decision makers deal with stochastic MCDM problems with greater confidence and less psychological pressure, and enables a risk-informed decision can be made with higher reliabilities. The stochastic MCDM framework can be extended to other sustainability areas to solve decision-making issues with uncertainties.

The cloud model based stochastic multi-criteria decision making technology for river health assessment under multiple uncertainties

The river health assessment (RHA) usually concerns multiple criteria including streamflow, ecology, physical structure, water quality and social service functions and evaluation standards formulated by managers as well as groups with different interests. Conventional multi-criteria decision making (MCDM) models are used to comprehensively evaluate river condition through multiple criteria under deterministic environment. In fact, uncertainties are always existed in these criteria data since they are correlated with hydrology and water resources system. In this work, we proposed a stochastic cloud model based MCDM framework for solving RHA considering multiple uncertainties in criteria performance values (PVs) and criteria weights (CWs). The cloud model is allied to describe uncertainty of PVs using numerous drops following normal distribution generated by forward drop generator (FDG). The minimum deviation principle based aggregated CWs are utilized to efficiently quantify uncertainty in CWs and reduce conflict between multiple CWs sources. A novel stochastic multi-criteria acceptability analysis (SMAA) is developed coupling with grey correlation analysis (GCA) and TOPSIS. The risk information for river evaluation caused by multiple uncertainties are described using quantified decision error risk (QDER) and rank uncertainty degree (RUD). The proposed methodology is verified practicability by applying it to river health evaluation in Taihu basin. The numerical simulations are conducted to demonstrate superiority and efficiency of novel SMAA in comparison with conventional SMAA and deterministic MCDM models based on GCA and TOPSIS. The robustness analysis is implemented to disclose its computation stability and reliability as well as effects of cloud parameters on final MCDM results. The results of novel SMAA show that it provides river managers with comprehensive river health and risk analysis information, assisting them to make highly reliable assessment and adopt effective measures harnessing rivers.

A systematic approach to evaluate risks and failures of public transport systems with a real case study for bus rapid system in Istanbul

Maintenance planning and risk evaluation of public transport systems (PTS) is completely critical and important for the natural structure and ecological balance of the city and its’ environment. It is noteworthy to take the correct actions and decisions for the process to make a significant contribution towards the development and ecological sustainability of the city. We also know that failures occurring in PTS leads to personal and social damages such as hazards for passenger life and financial loss that occurs as a result of damage to vehicles due to these failures. Therefore, it is essential to analyze failures of PTS and to improve maintenance planning for obtain a reliable transport system. For this aim, a systematic approach based on maintenance decision support system has been suggested to eliminate the risks arising from failures for Bus Rapid Transit (BRT) system in this paper. The suggested system aims to minimize the harmful effects of these risks with respect to individuals, society and environment perspectives. The proposed integrated systematic approach consists of fuzzy rule based system (FRBS), fuzzy multi criteria decision making (MCDM), stochastic MCDM, mathematical modelling, information theory and heuristic approaches. The mathematical modeling has been applied to evaluate membership functions (MFs) of FRBS and a heuristic approach named particle swarm optimization (PSO) has been also used to solve the problem. Additionally, MCDM methods are also used to prioritize the failures and to determine weights of them. The suggested systematic approach has been applied on a real case study for BRT system in Istanbul and some actions that includes more details and information about risks and failures have been suggested to obtain a more effective maintenance plan. The validity analysis also confirmed the obtained results of the suggested systematic approach.

Algorithm for Neutrosophic Soft Sets in Stochastic Multi-Criteria Group Decision Making Based on Prospect Theory

To address issues involving inconsistencies, this paper proposes a stochastic multi-criteria group decision making algorithm based on neutrosophic soft sets, which includes a pair of asymmetric functions: Truth-membership and false-membership, and an indeterminacy-membership function. For integrating an inherent stochastic, the algorithm expresses the weights of decision makers and parameter subjective weights by neutrosophic numbers instead of determinate values. Additionally, the algorithm is guided by the prospect theory, which incorporates psychological expectations of decision makers into decision making. To construct the prospect decision matrix, this research establishes a conflict degree measure of neutrosophic numbers and improves it to accommodate the stochastic multi-criteria group decision making. Moreover, we introduce the weighted average aggregation rule and weighted geometric aggregation rule of neutrosophic soft sets. Later, this study presents an algorithm for neutrosophic soft sets in the stochastic multi-criteria group decision making based on the prospect theory. Finally, we perform an illustrative example and a comparative analysis to prove the effectiveness and feasibility of the proposed algorithm.

A stochastic multi-criteria decision making framework for robust water resources management under uncertainty

Water planner and managers always work in a changing and uncertain environment, since the majority of water resource-related data is imprecise or uncertain to some extent. MCDM (multi-criteria decision making) is a well-suited decision support tool for water resources management, however multiple uncertainties existed in criteria weights (CWs) and performance values (PVs) hinder the application of MCDM methods in real-world problems. This paper proposes an application-oriented stochastic MCDM framework for robust water resources management under uncertainty. A weight aggregation approach based on the game theory coupled with feasible weight space (FWS) is developed for conflict resolution and uncertainty estimation. We propose a novel SMAA-GRA model by combining the stochastic multi-criteria acceptability analysis (SMAA) theory and grey relational analysis (GRA). The risk of errors in decision making and rank uncertainty degree are utilized to assess decision-making uncertainty. We perform significance analysis to examine the impacts of input parameter uncertainties on the final assessment of alternatives. The proposed framework is demonstrated via two water resources management case studies. Numerical experiments are conducted to investigate the robustness and computational efficiency of the framework as well as the effects of uncertainty level and probability distribution type on decision-making results. The results indicate that the framework enables water managers to examine the robustness of final decisions and allows a risk-informed water resource decision to be made with higher reliabilities.

Multi-objective optimization scheduling of wind–photovoltaic–hydropower systems considering riverine ecosystem

Hydropower can aid in compensating for wind and photovoltaic power output fluctuations and uncertainties. In this study, a multi-objective optimization model was established by integrating wind and photovoltaic power with hydropower scheduling considering the total power generation, power output stability, and influence of hydropower on a downstream riverine ecosystem. An improved adaptive reference point-based multi-objective evolutionary algorithm was employed to solve the wind–photovoltaic–hydropower system problem with various complicated constraints. Moreover, the large-scale system decomposition principle was used to decouple a wind–photovoltaic–hydropower system into a wind–photovoltaic compensated subsystem and a hydropower system. A combined solution method was developed according to the subsystem characteristics to improve the model efficiency. Considering that direct crossover and mutation of hydropower systems may not yield feasible solutions, dynamic feasible regions for crossover and mutation were constructed for multi-objective optimal scheduling. Furthermore, a stochastic multi-criteria decision making model that accounts for the uncertainty of criterion information was established, and the non-dominated solution obtained using the improved multi-objective evolutionary algorithm was employed for decision-making. The results showed that the total power generation, power output stability, and downstream riverine ecosystem have strong competitive relationships, and the improved adaptive reference point-based multi-objective evolutionary algorithm can produce superior quality Pareto optimal solutions with uniform distribution. Subsequently, the stochastic multi-criteria decision making model was used to rank the Pareto optimal solutions, where each solution can obtain several ranks with different probabilities, providing extensive information for use in decision-making.

Stochastic AHP and fuzzy VIKOR approach for warehouse location selection problem

PurposeThe purpose of this paper is to develop a stochastic multi-criteria decision-making approach to solute the warehouse location problem in the stochastic environment which contains uncertain condition.Design/methodology/approachIn developed approach, the weight of criteria was calculated by using the stochastic analytic hierarchy process (SAHP) method. Alternative ranking was made and evaluated by fuzzy VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje).FindingsThis study dealt with warehouse location selection problem of a supermarket that has sellers in many regions in Turkey and selected proper warehouse.Originality/valueThis study combined SAHP and fuzzy VIKOR methods as a solution approach for warehouse location selection problems.

Trapezoidal interval type-2 fuzzy soft stochastic set and its application in stochastic multi-criteria decision-making

Trapezoidal interval type-2 fuzzy soft set (TrIT2FSS) (Zhang and Zhang, Appl Math Model 37:4948–4971, 2013) is a delicious generalization of soft set theory where the performance of the alternatives over some parameters is assigned by means of trapezoidal interval type-2 fuzzy numbers (TrIT2FNs). Zhang and Zhang (2013) proposed an approach to multi-criteria group decision-making (MCGDM) problems under trapezoidal interval type-2 fuzzy soft set. However, due to the increasing complexity of decision-making problems, the performance of the alternatives may be determined randomly with respect to some possible states related to the decision-making problems which are known as stochastic multi-criteria decision-making (SMCDM) problems. To handle such type of SMCDM problems based on trapezoidal interval type-2 fuzzy soft set, no research exists till now. To fill up this lacuna, here we have formulated a new generalization of TrIT2FSS to solve the MCDM problems under stochastic environment. First, we have introduced the notion of trapezoidal interval type-2 fuzzy soft stochastic set (TrIT2FSSS). Second, we deal with the obligatory definition of expected trapezoidal interval type-2 fuzzy soft set (ETrIT2FSS). Finally, we have proposed a methodology for handling the trapezoidal interval type-2 fuzzy soft stochastic set (TrIT2FSSS) based stochastic MCDM problems. Additionally, in this model we have assumed that the weights of each of the parameters are partially known. We have proposed a process to obtain the weights of the parameters using signed distance measurement. An application of our proposed approach regarding the selection of the best company for investment of a bank has also been given to describe the feasibility and effectiveness of our proposed approach.

Online product purchasing decision through IoT platform: based on normal stochastic multi-criteria decision method and vertical projection distance

Online product reviews in the internet of things platform have important implications for consumers’ buying decisions. Considering the large volume and discrete stochastic characteristics of online review information, an online product purchasing decision method based on normal stochastic multi-criteria decision and vertical distance method is proposed. Firstly, we present and prove the weighted arithmetic average operator under normal stochastic information environment. Then, the idea of vertical projection distance is introduced, according to the binary structure of expectation and variance in the stochastic attribute information, a nonlinear programming model for calculating the product attribute weights is designed. Based on this, the weighted arithmetic average operator is used to aggregate the decision information from the attribute dimension, and the priority order of the candidate products is obtained according to the order relation of the binary structure of normal stochastic attributes. Finally, a case study of mobile phone purchase decision-making is carried out to verify the rationality and practicability of this method.

Multi-criteria group decision making under uncertainty: Application in reservoir flood control operation

This paper proposes an innovative framework for solving stochastic multi-criteria decision making (MCDM) problems when uncertainties exist in criteria performance values (PVs) and criteria weights (CWs) simultaneously. Methods for quantifying uncertainties in criteria PVs and CWs are presented. We establish the SMAA-TOPSIS model by combining stochastic multicriteria acceptability analysis (SMAA) and technique for order preference by similarity to ideal solution (TOPSIS). The risk of decision making errors is proposed to assess the impact of uncertainties on MCDM. We develop the LHS-based Monte Carlo simulation algorithm and corresponding computer program for solving the SMAA-TOPSIS model. We also suggest a three-stage MCDM procedure for stochastic MCDM problems. We apply the proposed methodology to a flood control operation case study to demonstrate its applicability. Our results indicate that the proposed methods can provide valuable risk information and enable risk-informed decisions to be made with higher reliabilities.

Real‐Time Optimal Flood Control Decision Making and Risk Propagation Under Multiple Uncertainties

AbstractMultiple uncertainties exist in the optimal flood control decision‐making process, presenting risks involving flood control decisions. This paper defines the main steps in optimal flood control decision making that constitute the Forecast‐Optimization‐Decision Making (FODM) chain. We propose a framework for supporting optimal flood control decision making under multiple uncertainties and evaluate risk propagation along the FODM chain from a holistic perspective. To deal with uncertainties, we employ stochastic models at each link of the FODM chain. We generate synthetic ensemble flood forecasts via the martingale model of forecast evolution. We then establish a multiobjective stochastic programming with recourse model for optimal flood control operation. The Pareto front under uncertainty is derived via the constraint method coupled with a two‐step process. We propose a novel SMAA‐TOPSIS model for stochastic multicriteria decision making. Then we propose the risk assessment model, the risk of decision‐making errors and rank uncertainty degree to quantify the risk propagation process along the FODM chain. We conduct numerical experiments to investigate the effects of flood forecast uncertainty on optimal flood control decision making and risk propagation. We apply the proposed methodology to a flood control system in the Daduhe River basin in China. The results indicate that the proposed method can provide valuable risk information in each link of the FODM chain and enable risk‐informed decisions with higher reliability.