Decision Maker's Preferences Research Articles

A multi-source transfer-based decision-making method with domain consistency and contributions

Large volumes of historical data that characterize the preferences of decision-makers are generated and recorded. Each decision-maker's historical data contain different knowledge and reflect different preferences. However, the learned decision models based on data-driven multi-criteria decision-making (D2MCDM) cannot help generate satisfactory decision solutions when the available data are insufficient. To solve this problem, the parameter-based transfer learning with multi-source domains (multi-SDs) is considered to be integrated with D2MCDM. Following the idea, this paper proposes a multi-source transfer-based decision-making (MTBDM) method with domain consistency and contributions to acquire the preferences of decision makers. The stability of criterion weights and that of decision makers’ individual consistency are constructed and combined with the difference between data distributions of multi-SDs and the target domain (TD) to develop the domain consistency. Based on the developed domain consistency, two optimization models are constructed to transfer criterion weights and individual consistency between multi-SDs and the TD, in which domain contributions of multi-SDs to the TD are considered based on domain consistency to avoid negative transfer. The advantages of the MTBDM method are demonstrated by its application to the auxiliary diagnosis of breast lesions. Its predictive performance is further validated by its comparison with the MTBDM method in four different situations and existing methods.

DEA cross-efficiency models with prospect theory and distance entropy: An empirical study on high-tech industries

Cross-efficiency evaluation with the data envelopment analysis (DEA) model is an effective way to assess performance and provide a complete ranking of decision-making units. However, it is generally assumed that decision makers are perfectly rational in the cross-efficiency model, which fails to consider the subjective preferences of decision-makers. Moreover, the arithmetic average method is usually adopted to aggregate efficiency scores in traditional cross-efficiency methods, which underestimates the importance of self-evaluation. To address these issues, we extend cross-efficiency with the DEA model by incorporating prospect theory and the distance entropy function. First, we calculate the prospect values of decision-making units to describe the non-rational subjective preferences under the risk of decision-makers. Second, based on prospect cross-efficiency, a new distance entropy function is developed to aggregate the ultimate prospect cross-efficiency values. More specifically, some traditional cross-efficiency evaluation models can be considered special cases of prospect cross-efficiency models with appropriate adjustments to the parameters. Finally, an empirical example is used to evaluate the prospect cross-efficiency results with the high-tech industries of 29 provinces in China to illustrate the applicability and effectiveness of the proposed model in ranking observations.

The Role of Language Concordant Care on Increasing Parental Engagement in Shared Decision Making

BACKGROUND: Shared decision making (SDM) allows patients and physicians to make decisions together by incorporating clinical expertise and patient preference. While SDM can improve patient satisfaction, preferences on the extent of involvement in SDM vary, particularly among different cultures and languages. We sought to describe preferences in decision making and degree of SDM among Spanish-speaking caregivers receiving pediatric Otolaryngology care. We hypothesized that language-concordant encounters would have higher SDM scores. METHODS: A cross-sectional study of Spanish-speaking caregivers undergoing consultation with pediatric otolaryngology was performed (May-August 2022). Care was provided in a language-concordant (LC) setting in which the physician and caregiver communicate in Spanish, or a language–discordant (LD) setting in which the physician communicates with interpreter support. Following their appointment, caregivers were invited to complete the Spanish Control Preferences Scale (CPS, select 2 of 5 options) to determine their two most preferred roles in medical decision making, and the Shared Decision-Making Questionnaire (SDM-Q-9; scored 0-100) to assess perceived SDM. Descriptive statistics, Fisher’s exact and the Mann-Whitney U test were used for analysis. RESULTS: Sixty-one caregivers were enrolled. CPS scores revealed the passive-collaborative medical decision making role was the most favored (59%), followed by an active-collaborative role (26%), and passive role (13%). Median SDM-Q-9 score for all caregivers was 100.00 (mean=93.70). The median SDMQ-9 score was 100 in LC visits, and 97.78 in LD visits (p = 0.428). No differences in SDM-Q-9 scores were detected for CPS categories between LC and LD visits. CONCLUSION: High levels of SDM were observed among Spanish-speaking caregivers using both LC and LD care. Most caregivers preferred a passive-collaborative role in medical decision making. Results may be influenced by positive response bias given the high scores observed. Further studies with larger sample sizes can further our understanding of the impact of LC care in SDM.

EFFORTS TO INTRODUCE THE TARAKAN CITY THROUGH IRAW TENGKAYU CULTURAL TOURISM

The city of Tarakan also has interesting customs and cultures of the community, such as the traditional ceremony of Iraw Tengkayu, which is one of the cultures of the city of Tarakan and comes from the Tidung tribe. Tarakan City is a city that has great tourism potential, but obstacles in the form of a lack of information, exposure, and facilities make it difficult to develop this potential. This study aims to explore the efforts that can be made to introduce the City of Tarakan through cultural tourism and the Iraw Tengkayu traditional ceremony. The method used is descriptive, with data obtained through literature studies. The results of this study indicate that the efforts made to introduce the tourism sector in Tarakan City through Iraw Tengakyu are carried out through improving tourist attractions, increasing digital promotions, and enhancing collaboration and community participation. This is done to increase tourist interest, create positive images and perceptions through the original culture of Tarakan City, and influence the decision-making process and preferences of tourist destinations.

Protecting Infrastructure Networks: Solving the Stackelberg Game with Interval-Valued Intuitionistic Fuzzy Number Payoffs

Critical infrastructure is essential for the stability and development of modern society, and a combination of complex network theory and game theory has become a new research direction in the field of infrastructure protection. However, existing studies do not consider the fuzziness and subjective factors of human judgment, leading to challenges when analyzing strategic interactions between decision makers. This paper employs interval-valued intuitionistic fuzzy numbers (IVIFN) to depict the uncertain payoffs in a Stackelberg game of infrastructure networks and then proposes an algorithm to solve it. First, we construct IVIFN payoffs by considering the different complex network metrics and subjective preferences of decision makers. Next, we propose a lexicographic algorithm to solve this game based on the concept of a strong Stackelberg equilibrium (SSE). Finally, we conduct experiments on target scale-free networks. Our results illustrate that in an SSE, for the defender in a weak position, it is better to defend nodes with high degrees. The experiments also indicate that taking fuzziness into account leads to higher SSE payoffs for the defender. Our work aims to solve a Stackelberg game with IVIFN payoffs and apply it to enhance the protection of infrastructure networks, thereby improving their overall security.

Choice Modeling of Laundry Detergent Data for Sustainable Consumption

Consumer choice modeling takes center stage as we delve into understanding how personal preferences of decision makers (customers) for products influence demand at the level of the individual. The contemporary choice theory is built upon the characteristics of the decision maker, alternatives available for the choice of the decision maker, the attributes of the available alternatives and decision rules that the decision maker uses to make a choice. The choice set in our research is represented by six major brands (products) of laundry detergents in the Japanese market. We use the panel data of the purchases of 98 households to which we apply the hierarchical probit model, facilitated by a Markov Chain Monte Carlo simulation (MCMC) in order to evaluate the brand values of six brands. The applied model also allows us to evaluate the tangible and intangible brand values. These evaluated metrics help us to assess the brands based on their tangible and intangible characteristics. Moreover, consumer choice modeling also provides a framework for assessing the environmental performance of laundry detergent brands as the model uses the information on components (physical attributes) of laundry detergents. Through a comprehensive evaluation of product performance, including brand tangible estimation, we shed light on the sustainability attributes of laundry detergents, offering a roadmap for consumers and manufacturers alike to make more informed, environmentally responsible choices of laundry detergents based on their physical attributes. Knowing the estimates of the attributes for the laundry detergent products, manufacturers can modify their physical attributes, e.g., decrease the amount of the detergent needed for one wash while increasing the total weight of the laundry powder in the package. In this way, more ecology- and consumer-friendly decisions can be made by manufacturers of laundry detergents.

Proposal of a Combined AHP-PROMETHEE Decision Support Tool for Selecting Sustainable Machining Process Based on Toolpath Strategy and Manufacturing Parameters

Sustainable manufacturing technologies are the new challenge faced by enterprises, industries, and researchers. The development of a sustainability-based assessment method considering the environmental and economic impacts is crucial to realize viable manufacturing. However, few studies have addressed environmental economics and social flows using a common perspective. Mechanical machining is one of the most-used manufacturing techniques. The overall ecological, economic, and social footprint requires accurate and effective estimation and optimization. Several studies have addressed this issue by examining the entire process of machining, but sustainability flows for machining parameters and toolpaths have remained relatively unexplored. The lack of systematic assistance tools bridging the gap between decision-maker preferences and the three sustainability pillars—economic, social, and environmental—has impeded the widespread adoption of sustainable machining practices. To this end, this paper proposes an integrated approach to the decision-making problem that combines the Analytical Hierarchy Process (AHP) with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) for selecting a sustainable machining strategy. The sustainability criteria are driven by manufacturing process parameters commonly employed and regulated during the manufacturing phase. This includes toolpath strategies as a qualitative input factor and manufacturing parameters such as cutting speed, feed rate, depth of cut, and stepover as quantitative input factors, affirming the practical applicability of the method in industrial contexts. New fundamental methods are also presented for selecting the most efficient machining parameters and toolpaths according to the weights assigned to each ecological, social, and economic footprint by the decision-maker (the manufacturer or production manager). In this way, sustainable machining strategies in the manufacturing industry will be strengthened in integrity. In a case study of part-end milling, both manufacturing parameters and toolpath strategies are considered to establish sustainable feature-based machining decisions.

Windfall profit-aware stochastic scheduling strategy for industrial virtual power plant with integrated risk-seeking/averse preferences

The increasing penetration of renewable energy in power grids introduces higher levels of uncertainty, while current decision-making models typically favour either a risk-averse or risk-neural strategy, and the research works related to windfall profit-aware risk-seeking strategies are quite limited. In this paper, a novel concept of integrated risk-seeking/averse preference is proposed, and a windfall profit-aware stochastic scheduling model for an industrial virtual power plant (IVPP) is developed based on this type of risk preference, which can realize the joint management of potential high profits and extreme losses. Firstly, the potential best- and worst-case results are incorporated into the holistic decision-making framework, which are quantified by the value at best (VaB) and the conditional value at risk (CVaR) measures, respectively. Next, the windfall profit-aware stochastic scheduling model is developed for the optimal operation of IVPP in day-ahead and real-time electricity markets, where multi-type flexible resources, such as energy conversion and storage devices, industrial production workstations, material storages and financial instruments, are utilized to improve expected profits and minimize the potential risks. Specifically, two types of credit-based virtual transactions, increment and decrement bids, are employed by the IVPP to increase its trading flexibility in electricity markets. Finally, simulation studies are conducted for a multi-energy IVPP to validate the proposed windfall profit-aware framework and model, showcasing that the risk-seeking and risk-averse preferences of decision-makers can be fully satisfied simultaneously under actual market environment. Moreover, risk parameters can be adjusted accordingly to manage windfall profits, expected profits, and extreme losses flexibly in electricity markets.

Unveiling professional and personal preferences of early career dentists during first year of employment at the Thai dental public sector: a one-year cross-sectional study

BackgroundDespite the implementation of various government policies to retain Thai dentists in public sector, a high turnover rate among early career dentists has persisted for decades. This study aims to explore factors relating to early career dentists’ choice of the public sector as their preferred workplace and decisions relating to staying, resigning, or relocating from the workplace after one-year employment.MethodsA one-year cross-sectional survey was conducted among Thai early career dentists who began working in 2020 using two sets of online questionnaires. The first survey assessed factors influencing dentists’ decision to choose the public sector as their preferred workplace at the beginning of the year. The second survey investigated factors influencing dentists’ decision to stay, resign, or relocate from the same workplace at one-year after employment. Descriptive statistics and multivariable binary logistic regression were used for data analysis.ResultsA total of 198 early career dentists completed the online survey questionnaire at the starting point (December 2020–January 2021), and 186 dentists completed the one-year employment questionnaire. The living environment and provided amenities and facilities were the most influential factors in their decision to choose and remain in the public sector. Conversely, their attitude toward unrelated job descriptions and an increased opportunity to pursue postgraduate studies were the most relevant factors when deciding to relocate to a new workplace. Factors such as delayed authority in bureaucracy, hometown location, and being in relationship status were the most significant contributors to resignation from the public sector.ConclusionsThe major factors influencing dentists’ choice and retention in the public sector include the living environment, supportive supervisors and colleagues, and the availability of opportunities for further postgraduate education. Meanwhile, factors impacting retention after one year of work are related to hometown location and the bureaucracy system. Collaborations among ministries, tailored to each local community’s specific requirements, may enhance dentists’ retention in public sectors.

Claiming or abdicating medical authority: Treatment recommendation actions, doctor-patient relationship, and antibiotic overprescription in Chinese paediatrics.

Antibiotic overprescription in China has long been considered a problem on the supply side, linked to the financial incentives of physicians. Based on the conversation analysis of 187 video-recorded naturally occurring medical consultations in Chinese paediatric primary care settings, this study finds that the driving force behind the problem of antibiotic overprescription in China has changed. Physicians use a low-authority communication style to recommend treatment, displaying a low level of medical authority and a willingness to accommodate caregivers' preferences in antibiotic prescribing decisions. The problem is now attributed to physician-caregiver interaction, doctor-patient relationship and the antibiotic-saturated prescribing culture. Practice implications involve deepening the understanding of the evolving nature of the antibiotic overprescription problem in China, building trust between physicians and patients/caregivers in order to facilitate the physicians' role as the gatekeeper of antibiotics and providing training programmes to help physicians develop effective communication skills.

Sensory Perception and Consumer Acceptance of Carrot Cultivars Are Influenced by Their Metabolic Profiles for Volatile and Non-Volatile Organic Compounds.

Sensory parameters as well as the volatile and non-volatile compound profiles of sixteen carrot cultivars were recorded to obtain insight into consumer preference decisions. The sensory test was carried out with a consumer panel of 88 untrained testers allowing a clear acceptance-based differentiation of the cultivars. Five individual sensory characters (sweetness, overall aroma, bitterness, astringency and off-flavor) supported this discrimination. Chemical analyses of volatile organic compounds, polyacetylenes, phenylpropanoids and sugars enabled us to correlate the influence of these ingredients on sensory perception. Higher concentrations of α-pinene, hexanal, styrene and acetophenone correlated with a better acceptance, as well as sweetness and overall aroma perception. In contrast, a low acceptance as well as a stronger perception of bitterness, astringency and off-flavor correlated with enhanced concentrations of camphene, bornylacetate, borneol, myristicine, falcarindiol, falcarindiol-3-acetate, laserin and epilaserin. The present study should support the development of new breeding strategies for carrot cultivars that better satisfy consumer demands.

Multi-criteria group decision making based on graph neural networks in Pythagorean fuzzy environment

Given that the majority of existing approaches for multi-criteria group decision making (MCGDM) rely solely on the preferences of decision makers (DMs) and fail to consider the various relationships between alternatives, this paper attempts to model the relevant relational structures using graphs and introduce the concept of graph neural networks (GNNs) in the context of group decision-making. By leveraging the powerful expressive capabilities of GNNs, the aim is to mine additional information pertinent to the decision-making process and screen out alternatives for the final decision. To begin, we provide a mapping of MCGDM to the graph domain and construct a corresponding relation graph among alternatives. Additionally, to deal with uncertain or vague information, we transform the group decision-making problem into a Pythagorean fuzzy environment and define a novel measure of entropy specifically designed for Pythagorean fuzzy sets (PFSs) in the entropy weight model to determine the weights of criteria. Simultaneously, we propose a new distance measure for PFSs, which is then applied to the extended Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to rank alternatives. Furthermore, we develop a GNNs-based Pythagorean fuzzy MCGDM approach that incorporates the aforementioned techniques for group decision-making. Finally, to validate the effectiveness and superiority of this approach, we employ it to address a supplier selection issue. Compared with baseline group decision-making approaches, our approach can indeed capture the relationships among alternatives in complex group decision-making scenarios and outperforms the best-performing baseline by nearly 2.8% in terms of ranking accuracy.

A behavior prediction method for complex system based on belief rule base with structural adaptive

Predicting the behavior of complex systems and taking appropriate measures for system management is of paramount importance for decision-makers. Belief rule base (BRB) is an effective method for modeling complex systems, and its construction relies on expert knowledge. However, in certain complex system prediction problems, deriving the structure and parameters of BRB from limited expert knowledge or existing models is a challenge, and there may even be a lack of available expert knowledge. Data mining plays a crucial role in obtaining the parameters for model construction in the design of decision support systems (DSSs). Therefore, this paper proposes a method for behavior prediction of complex systems called the structural adaptive BRB (SA-BRB). First, to reduce the randomness of K-means+ +, this paper introduces an error constraint and employs this algorithm to mine historical data for constructing a reference value set. Second, a model ensemble construction process is designed to create different model structures. Subsequently, the evidence reasoning (ER) algorithm is applied to derive the models, and the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is used for model optimization. Finally, a model evaluation method is established, allowing adaptive adjustment of the model structure according to the needs of engineering practice and the preferences of decision-makers. Furthermore, the effectiveness of the proposed method is validated through two case studies: one focusing on predicting the health status of a flywheel system and the other on forecasting full-load power generation in a combined power plant.

Attitudes Toward Hysterectomy in Saudi Arabian Women Undergoing Evaluation for Uterovaginal Prolapse: A Cross-Sectional Study.

Hysterectomy isan effective management approach for uterovaginal prolapse. However, the decision to undergo hysterectomy is a complex matter, influenced not only by medical factors but also cultural beliefs, societal norms, and individual attitudes.In Saudi Arabia, a nation with its distinctive cultural and social norms, the understanding of women's attitudes toward hysterectomy is of utmost importance. Unfortunately, such related attitudes have not been explored. This first-ever study aimed to investigate the attitudes toward hysterectomy among Saudi Arabian women undergoing evaluation for uterovaginal prolapse, by exploring the factors influencing their decision-making process and treatment choices, with a particular focus on the potential impact of cultural beliefs and societal norms. A survey was conducted among 404 women referred for uterovaginal prolapse evaluation. The participants completed a self-administered questionnaire, which included demographic information, perceptions on hysterectomy's impact on well-being, and factors affecting decision-making. The mean ± standard deviation of participants was 51.07 ± 11.1 years. Most participants were currently married (n=327, 81%), were unemployed (n=309, 76.5%), and had an "excellent" self-rated general health status (n=138, 34%). Current prolapse management methods included Kegel exercises (n=103, 25.5%), pessary use (n=32, 8%), physical therapy (n=12, 3%), planned surgery (n=75, 18.5%), and no specific treatment (n=182, 45%). Overall, the study revealed diverse findings on the potential perceived impact of hysterectomy on different aspects of well-being. Notably, for pain symptoms, 152 participants (38%) reported potential improvement, while 123 participants (30%) predicted worsening, and 129participants (32%) anticipated no change. Moreover, the study unveiled insights into the factors influencing patients' decision-making between hysterectomy and uterine-sparing procedures. Remarkably, 97 respondents (24%) considered the doctor's opinion to be "very Important," while 91 respondents (22%) rated the impact on surgical complication risk as "very important." Furthermore, the desire to preserve all healthy organs was deemed "very important" by 106 respondents (26%). The resources of information women depended on when making a decisionto undergo hysterectomy varied and included a second opinion from another physician (n=68, 17%), social media (n=81, 20%), opinion from spouse/partner (21%), second opinion from female family members (n=99, 25%), and opinion from friends (n=70, 17%).Regarding preferences for decision-making, the responses varied substantially. Overall, 65 participants (16%) indicated a preference for their doctor to make the decision entirely, 81 participants (20%) preferred shared decision-making with their doctor, 89 participants (22%) wanted their doctor to make the decision after considering their input, 77 participants (19%) wished to make the final decision after discussing it with their doctor, and 93 participants (23%) expressed a preference for independently making the final decision. Lastly, correlations between women's responses and some demographic factors were identified. This pioneering study provides valuable insights into Saudi Arabian women's attitudes toward hysterectomy, emphasizing the need for patient-centered care and culturally sensitive approaches in managing uterovaginal prolapse.

Consensus-reaching process in multi-stage large-scale group decision-making based on social network analysis: Exploring the implication of herding behavior

Multi-stage large-scale group decision-making refers to a decision-making system with a large number of democratic evaluations provided by decision-makers and a multi-stage dynamic decision-making process. This method can provide the evaluation values of different decision-makers at heterogeneous stages, collect dynamic and complex decision-making information, and obtain objective decision-making results. Here, we consider large-scale group decision-making in the context of social networks, and develop a consensus-reaching process based on herding behavior. Decision-makers are identified based on the characteristics of information gradient dissemination, risk aversion, and authority obedience, and divided into different groups by how much herding behavior they exhibit. Guided by the referenced preferences obtained from a recommendation mechanism, the modification model considering the adjustment willingness and minimum adjustment cost, optimizes the preferences of low-consensus decision-makers in the strong and no herding groups. Meanwhile, the punishment model, which seeks the minimum degree of group consensus that satisfies the consensus threshold condition, optimizes the weight of the low-consensus decision-makers in the weak herding group. Finally, we present an illustrative example of emergency generation selection during the Spanish energy crisis to verify the rationality and soundness of the proposed multi-stage large-scale group decision-making approach.

Methane emission reduction in China's natural gas industry: Construction of technology inventory and selection of optimal technology programs

Methane (CH4) is a potent yet short-lived greenhouse gas (GHG), and CH4 emissions from China's natural gas industry hinder its low-carbon and high-quality development. This paper addressed this challenge by compiling emission reduction technology inventory targeting CH4 emissions from process and vented within the natural gas industry. Using a multi-objective optimization model and the NSGA-II algorithm, various technology programs were generated to minimize both emissions and costs simultaneously. These programs underwent classification using quadrants and the osculating value method to identify optimal programs for emission reduction and cost-effectiveness. This paper examined 30 potential programs as samples for upstream and midstream. The study demonstrated that for decision-makers with preferences, this paper employed quadrant classification, categorizing all potential programs into three categories: “emission reduction,” “cost,” and “comprehensive.” This categorization met the demand preferences of different decision-makers. For decision-makers lacking specific preferences, the paper identified the best emission reduction technology programs under the current target using the osculating value method. Specifically, in the upstream, the most effective program achieved a 37.85 % reduction in emissions, highlighting “Reduced Emissions Completions for Hydraulically Fractured Gas Wells” as the critical technology. In the midstream, the best program achieved a reduction efficiency of 25.80 %, with “Replacing Wet Seals with Dry Seals in Centrifugal Compressors” contributing the most to emissions reduction. This study offers guidance and illuminating reference cases for the selection of CH4 emission reduction technologies within China's natural gas industry.

Exploring the Important Determinants Shaping Treatment Preferences: Qualitative Insights into Breast Cancer Patient Experiences and Perspectives in New Zealand.

Despite the importance of acknowledging patient preferences in treatment decision-making, little is known about the treatment preferences and the factors underlying those preferences of breast cancer patients. This study aims explore patient experience and perspective regarding treatment preferences and identify the important determinants that shape these preferences in the context of New Zealand. Semi-structured online interviews comprised of six focus group discussions and five individual interviews were performed with 26 breast cancer patients. The interviews were recorded, transcribed, and analyzed using the reflexive thematic analysis approach. Four main themes were derived: (1) positive treatment outcomes; (2) the negative impact of treatment-related side effects on quality of life; (3) treatment accessibility, availability, and timeliness; (4) cost of treatment. Patients revealed a strong preference towards treatments that yield longer survival, achieve remission, and prevent cancer recurrence. Additionally, patients favored treatments with minimal side effects that had minimal impact on their quality of life. There was a notable preference for treatments that were easily accessible and available in a timely manner. However, patients faced challenging decisions in balancing the costs of treatments with their benefits, leading to a consistent preference for treatments supported by government funding or medical insurance to alleviate financial burdens. Our study reveals that breast cancer patients in New Zealand have different perceptions and preferences regarding cancer treatment. Patients frequently find themselves making trade-offs among various attributes of a treatment, aligning these decisions with their personal values and beliefs. By considering these preferences and trade-offs in future studies that measure patient preferences, healthcare professionals can enhance their support for patients in making informed choices that align with their values and priorities. Additionally, healthcare policymakers can develop patient-centered policies that cater to the unique needs and preferences of breast cancer patients.

MCDM-AHP and PROMETHEE methods integrated for base service strategy vendor evaluation and selection

Business competition is very important in controlling product-savvy customers. Strong basic service techniques will be the main factor that binds vendors as the final destination in the supply chain through the strength of business network processes. This research aims to create a strategic basis for evaluating and selecting vendors through the integration process services of the multi-criteria decision-making method analytic hierarchy process (MCDM-AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) methods. Empirical studies show how this approach can provide optimal decision support for the vendor evaluation and selection process. Eight different types of criteria are required in its apps and must be realized as a barometer of the strategic basis for selecting vendors so that business processes are of high quality. These criteria include quality of goods, payment methods, payment terms, minimum transactions, discounts, delivery times, inventory, and service. The optimal weight for each criterion will be determined based on its importance to the synthesis process and its feasibility tested using mathematical algebra matrices and expert choice apps. Decision-making was based on the results of ranking evaluation of selected vendors through the development of 342 preference matrices, ten vendors were deemed worthy of acceptance and nine other vendors were rejected.

Post-disaster restoration planning of interdependent infrastructure Systems: A framework to balance social and economic impacts

Hazard-induced service interruption of interdependent infrastructure systems (IISs) (e.g., electricity, water, gas, etc.) can lead to significant disruptions of social and economic functions of a modern society. An effective post-event restoration of the IISs is therefore of paramount importance to the overall recovery of a hazard-stricken community as a whole. As opposed to approaches with pure engineering perspectives, this study proposes an IISs restoration planning methodology aimed at balancing tradeoffs between the loss of social services (e.g., health care, food supply, etc.) and that of economic productions (e.g., construction, manufacturing, trade, etc.) throughout the IISs restoration process. The methodology is distinguished from previous researches with the following contributions: i) quantitatively relates the losses of various social services and economic productions to the service disruptions of IISs through the functionality loss of buildings; ii) the IISs disruption-induced overall losses of social services and economic productions accumulated throughout the whole recovery process is set as the bi-objective in formulating IISs restoration plans, and the Pareto optimal solutions are given to satisfy different decision preferences; iii) physics-based models capturing operational mechanisms of the IISs are embedded to provide realistic estimations of commodity supplies at each time step of the restoration optimization. The optimization is coupled with Monte Carlo simulation to uncover the impact of decision preference on community recovery from a statistical point of view. Testbed illustration shows that the decision preference makes significant impact on the recovery of the community as a whole and of different areas in the community with different socioeconomic characteristics.

Optimal design of low impact development at a community scale considering urban non-point source pollution management under uncertainty

Urban non-point source (NPS) pollution management is receiving increased attention because of the environmental and ecological consequences of urbanization. We developed a Storm Water Management Model (SWMM)-based interval double-sided chance-constrained programming (SWMM–IDCCP) model for the optimal design of low impact development (LID) during urban NPS pollution management at a community scale. Pollution control efficiency and economic costs related to LID practices were considered in the SWMM–IDCCP model, simultaneously addressing uncertainties in pollutant output and decision-making processes. Specifically, by combining SWMM, Monte Carlo simulation, interval linear programming, and double-sided chance-constrained programming, the SWMM–IDCCP model can evaluate export loads of urban NPS pollutants and pollution control efficiencies of LID practices and analyze the corresponding uncertainties. Concurrently, the proposed model can provide reliable and optimal implementation schemes for LID practices under different scenarios, dealing with uncertainties such as discrete intervals and double-sided random parameters. The proposed model was applied to the Dongguan University of Technology in Dongguan City, South China. The random distribution characteristics of pollution load were identified. The ranges of pollution load for total suspended solids, chemical oxygen demand, total nitrogen, and total phosphorus were 76,430–165,698, 44,366–74,155, 2118–3,830, and 97–177 kg in 2021, respectively. Probability distribution characteristics of pollution control efficiencies of three LID practices (i.e., bio-retention cell, green roof, and permeable pavement) for the four urban NPS pollutants were obtained. Using the SWMM-IDCCP model, LID configuration schemes were obtained under diverse pollution control targets and confidence levels, taking into account variations in the decision preference of different administrators. This research indicates that the SWMM-IDCCP model can be used to manage urban NPS pollution at a community scale under uncertainty.