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This work considers a long-term Perishable Inventory Routing Problem with multiple products, static demand, and single vehicle, in the setting of Vendor Managed Inventory. By analyzing the optimal solutions of long-term cases that can be solved in Python+Gurobi within 2 h, we capture some patterns of optimal solutions. Utilizing these patterns, experiments show that under certain conditions, the mathematical models of multi-product problems could be simplified to single-product problems, which have the same optimal solutions while taking less time to solve. Managerial insights were generated that for products with static demand in the long term, delivery should be arranged at the store level rather than at the product level. Products in the same store should have the same delivery pattern, no matter how different the unit holding costs are. By further analyzing the optimal solutions of the simplified models, we find that optimal value will stabilize in the long term, and the optimal solution is very close to the solution point where total inventory holding cost and transportation cost are close. Based on these findings, we have developed a heuristic that always provides optimal or close-to-optimal solutions with far less computational time, compared with Python+Gurobi.

We propose to employ evidential reasoning (ER) rule to construct a clinical decision support system (CDSS) to aid physicians to predict the probability of intensive care unit (ICU) admission and in-hospital death for trauma patients once they arrive at a hospital. A generalized Bayesian rule is used to mine evidence from historical data. Evidence is profiled using a format of belief distribution, where the belief degrees of different trauma outcomes are assigned with derived probabilities linked to the corresponding outcomes. Inputs to the CDSS are clinical data of a patient, and output from the system is predicted belief degree of severe trauma, including ICU admission and in-hospital death. The inner logic of the CDSS is that pieces of evidence that match the clinical data of a patient are identified from the evidence base first, and then the ER rule-based evidence aggregation mechanism is utilized to combine the matched evidences to arrive at a prediction. The reliability, weight, and interdependence of clinical evidence are taken into account. Moreover, an evidence weight training module is constructed. The ER rule-based prediction model has superior performance compared with logistic regression and artificial neural network models. An innovative and pragmatic ER rule-based CDSS for trauma outcome prediction is contributed by this article. In the era of big data, this CDSS helps predict patient outcomes based on historical data and helps physicians in emergency departments make proper trauma management decisions.

In this paper, we propose a new probabilistic modeling approach for interpretable inference and classification using the maximum likelihood evidential reasoning (MAKER) framework. This approach integrates statistical analysis, hybrid evidence combination and belief rule-based (BRB) inference, and machine learning. Statistical analysis is used to acquire evidence from data. The BRB inference is applied to analyze the relationship between system inputs and outputs. An interdependence index is used to quantify the interdependence between input variables. An adapted genetic algorithm is applied to train the models. The model established by the approach features a unique strong interpretability, which is reflected in three aspects: (1) interpretable evidence acquisition, (2) interpretable inference mechanism, and (3) interpretable parameters determination. The MAKER-based model is shown to be a competitive classifier for the Banana, Haberman’s survival, and Iris data set, and generally performs better than other interpretable classifiers, e.g., complex tree, logistic regression, and naive Bayes.

Since late January 2020 when the first coronavirus case reached England, United Kingdom, the coronavirus disease 2019 (COVID-19) has spread rapidly and widely across all local authorities (LAs) in England. In this featured graphic, we visualise how COVID-19 severity changes nationally and locally from 30 January to 23 November 2020. The geo-visualisation shows that there have been large regional disparities in the severity of the outbreak, and the epicentres have shifted from Greater London, Leicester, to the North of England and remained in the North during pre-lockdown, post-lockdown, easing lockdown and second national lockdown phases. We further find that the increase in the testing capacity may partially explain the sharp increase in the confirmed cases during the second wave of the pandemic. However, the disparities in the severity of COVID-19 (i.e., confirmed cases and deaths) among LAs in England become more significant over time. It further sheds light on the necessity of establishing decisive and timely responses to cope with local pandemic situations.

Energy development concerns not only the development of renewable energies but also the shift from centralised to clean, decentralised power generation. The development of decentralised energy (DE) is a core part of the energy and economic strategies being adopted around the world that drives the progress toward a highly sustainable future. This paper reviews the concepts, development status, trends, benefits and challenges of DE systems and analyses the existing models and methods for assessing the performance of these systems. A hierarchical decision model for evaluating the performance of DE systems is also constructed based on the framework of multiple criteria decision analysis, which considers the identification, definition and assessment grade of decision criteria. The evidential reasoning approach is applied to aggregate assessment information in a case study of the implementation of an intelligent decision system. Sensitivity and trade-off analyses are also conducted to show how the proposed model can be used to support decision making in DE systems.

In this study, a new risk assessment model is developed and the evidence reasoning (ER) approach is applied to assess failure risk of knowledge-intensive services (KIS) corporates in the UK. General quantitative financial indicators alone (e.g., operational capability or profitability) cannot comprehensively evaluate the probability of company bankruptcy in the KIS sector. This new model combines quantitative financial indicators with macroeconomic variables, industrial factors and company non-financial criteria for robust and balanced risk analysis. It is based on the theory of enterprise risk management (ERM) and can be used to analyze company failure possibility as an important aspect of risk management. This study provides new insight into the selection of macro and industry factors based on statistical analysis. Another innovation is related to how marginal utility functions of variables are constructed and imperfect data can be handled in a distributed assessment framework. It is the first study to convert observed data into probability distributions using the likelihood analysis method instead of subjective judgement for data-driven risk analysis of company bankruptcy in the KIS sector within the ER framework, which makes the model more interpretable and informative. The model can be used to provide an early warning mechanism to assist stakeholders to make investment and other decisions.

This paper proposes a new belief rule-based (BRB) expert system for fault diagnosis of marine diesel engines. The expert system is the first of its kind that consists of multiple concurrently activated BRB subsystems, in which each subsystem has its distinctive outputs and uses the evidential reasoning approach for inference. This novel modeling approach can be applied to identify fault modes that may co-exist. In essence, the group of BRB subsystems is used to model the nonlinear relationships between the fault features and the fault modes in marine diesel engines. The initial BRB expert system can be established by using expert experience and then optimized by using the data samples accumulated during the operation of marine diesel engines. Due to limitations in knowledge and data collected, ignorance is also considered in some BRB subsystems. The proposed BRB expert system is applied to abnormal wear detection for a kind of marine diesel engine. The performance of the BRB expert system is investigated in comparison with that of artificial neural network (ANN) models, support vector machine (SVM) models, and binary logistic regression model with fivefold cross-validation. The results show that the BRB expert system can be used for fault diagnosis of marine diesel engines in a probabilistic manner, which outperforms the ANN models, SVM models, and the binary logistic regression model in terms of accuracy and stability, and can effectively identify concurrent faults.

As a multi-criteria decision-making (MCDM) method, the evidential reasoning (ER) approach can deal with uncertainties that are resulted from the limited knowledge and experience of experts. Due to the lack of information in the decision-making process, experts usually cannot give quantitative evaluations, but can only express their views with linguistic terms. It is usually impossible for experts to give accurate linguistic terms since they may hesitate among several linguistic terms or interval ones. In addition, experts may have different preferences for different linguistic evaluations. To fully express the evaluations, the probability can be introduced to model the preferences of experts. In this study, we propose the generalized probabilistic linguistic term set (G-PLTS) to represent the evaluation information with various linguistic forms. Then, the ER approach is investigated in the environment with G-PLTSs. Besides, a gained and lost dominance score (GLDS) method is utilized to rank the alternatives, forming an integrated method, which we call the generalized probabilistic linguistic evidential reasoning (GPLER) approach, to solve the MCDM problems with several uncertainties. Finally, we apply this method to the screening of high-risk population of lung cancer to verify the effectiveness of the proposed method.Keywords: General probabilistic linguistic term set; evidential reasoning approach; gained and lost dominance score method; screening of high-risk population for lung cancer

There are typically two types of consistency of fuzzy preference relations (FPR), namely additive and multiplicative consistency. They are defined based on the assumption that decision makers are rational and can provide strictly consistent FPRs. To take into consideration the bounded rationality of decision makers, the current study relaxes this assumption and proposes a new measure called triangular bounded consistency for judging the consistency of FPRs. To define triangular bounded consistency, a directed triangle is used to represent three FPRs among any three alternatives, with each directed edge representing an FPR. The condition of restricted max–max transitivity (RMMT) in the directed triangle is quantitatively examined. Under the assumption that the bounded rationality of a decision maker is characterized by their historical FPRs, which are represented by directed triangles that satisfy RMMT, triangular bounded consistency is determined using the historical FPRs. We then illustrate how triangular bounded consistency can be used to verify the consistency of FPRs that are newly provided by decision makers and how to estimate some missing FPRs that are not provided by decision makers. Finally, to demonstrate the application of triangular bounded consistency of FPRs in multi-attribute decision analysis, we investigate a problem that involves selecting areas to market products for a company.

Road traffic fatalities have long been a main concern of road safety experts in developing countries. Such countries annually lose a large number of their citizens due to road crashes. Globally, the Data Envelopment Analysis (DEA) has been successfully applied in road safety assessment as a powerful decision making method. It is noteworthy that all of the current studies optimistically measured the efficiency score of each Decision Making Unit (DMU) based on its position relative to only the efficient frontier. However, the efficiency value can be pessimistically determined on the basis of the anti-efficient frontier. A double-frontier model will lead to more realistic results. Meanwhile, many studies have recently implemented Cross-Efficiency Method (CEM) as a DEA extension for performance evaluation because of the unique ranking results without requiring unrealistic weight restrictions. In this regard, the present study aims to assess Iranian road safety performance by proposing a novel double-frontier CEM taking into account both optimistic and pessimistic points of view, simultaneously. Therefore, two cross-efficiency matrices are constructed by applying the standard and the inverted input-oriented CCR (Charnes, Cooper, Rhodes) models, respectively. Evidential Reasoning Approach (ERA) is then employed as a nonlinear method of combination to aggregate the transformed cross-efficiency scores to pieces of evidence. Finally, the findings confirm that the presented procedure results in a more realistic and comprehensive assessment, which is relatively in agreement with the evaluation results obtained considering both the efficient and anti-efficient frontiers.

In multiple criteria decision making (MCDM), the degree to which the individual assessment of alternative course of action on a criterion correctly portrays the overall assessment is rarely considered, which is understood as the reliability of the criterion. Meanwhile, the large superior intensity of each alternative in a solution is preferred by a decision maker, which is understood as the reliability of the solution. This paper proposes a new evidential reasoning approach for multiple criteria decision making that can consider criterion reliability and also provide high solution reliability. The reliability of each criterion is generated using a decision matrix on the assumption that criterion weights are allowed to be perturbed to help minimize the maximum difference between the true value of criterion reliability and its estimate. Under this assumption, the post-optimal solution space of weight and reliability variables of criteria is found, within which the maximum solution reliability is determined. Under the conditions where the maximum solution reliability within the post-optimal solution space is ensured, a comparison between alternatives is made using their best rankings and superior intensity successively to generate solutions to MCDM problems. A problem of evaluating environmental protection ways for a company located in Tongling of Anhui Province of China is investigated using the proposed approach to demonstrate its validity and applicability. These are highlighted by the comparison of the proposed approach with two other commonly used methods.

Decision making in financial investment often involves conflicting information and subjective judgment of the investors. The Evidential Reasoning (ER) rule is a probabilistic inference rule that is suitable for handling conflicts in information and allows judgmental weighting on evidence sources. A model of analyst opinions is constructed based on the ER rule, which takes as input stock reports by financial analysts and produces portfolio strategies via evidence combination. Backtesting the investment strategies on data from the China Stock Market & Accounting Research (CSMAR) Database of 120,023 recommendation reports released between November 2008 and December 2012 by 3,761 financial analysts shows that the risk-adjusted average annual return of the strategy outperformed that of the CSI300 index by as much as 10.69% for an investment horizon of six months, with the p value from Student’s t-test as low as 0.02%. The model serves as the first successful application of the ER rule to aggregating financial analysts’ opinions for decision making in stock investment.

In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts'assessments as recorded in historical datasets. Then a data-driven evidential reasoning rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Science Foundation of China is conducted to show the effectiveness of the proposed model. The data-driven evidential reasoning rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historic statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential reasoning rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries., 20 pages, forthcoming in International Journal of Project Management (2019)

The present study is to evaluate the performance of different aggregate production planning (APP) strategies in presence of uncertainty. Therefore, the relevant models for APP strategies including the pure chase, the pure level, the modified chase, the modified level and the mixed chase and level strategies are constructed by using both multi-objective programming and simulation methods.The models constructed for these strategies are run with respect to the corresponding objectives/criteria in order to provide business insights to operations managers about the effectiveness and practicality of various APP strategies in presence of uncertainty. The real world operational data are collected from soft drink industry to validate and implement the models.In addition, multiple criteria decision making (MCDM) methods are used besides multi-objective optimisation to assess the overall performance of each APP strategy. A detailed sensitivity analysis is also conducted by changing the criteria weights in MCDM methods to evaluate the impacts that these weight changes can have on the final rank of each APP strategy.The results of the simulation models are compared to those of multi-objective optimisation models. In general, in both mathematical programming and simulation models, the pure chase and the modified chase strategies presented the best performance, followed by the pure level strategy.

There is increasing evidence that microRNAs (miRs) play critical roles in the pathological and physiological processes associated with myocardial ischemia reperfusion (I/R). miR-145 has been extensively studied in the cardiovascular system; however, the role of miR-145 in myocardial I/R remains unclear. Therefore, the present study aimed to investigate the role and mechanism of miR-145-5p in myocardial I/R by establishing a hypoxia/reoxygenation (H/R) model using H9c2 cardiomyocytes. The expression of miR-145-5p was regulated by transfection and the potential target of miR-145-5p was identified. In addition, apoptosis of the cardiomyocytes was evaluated using flow cytometry and the detection of cleaved caspase-3 by western blotting. The results revealed that miR-145-5p expression was decreased while cell apoptosis and Rho-associated coiled-coil-containing kinase 1 (ROCK1) expression were increased in H/R-stimulated H9c2 cardiomyocytes. The upregulation of miR-145-5p reduced apoptosis and the expression of ROCK1 in H/R-stimulated H9c2 cardiomyocytes. Furthermore, the overexpression of ROCK1 significantly attenuated the miR-145-5p-induced reduction of apoptosis following H/R. In conclusion, the present study indicates that the overexpression of miR-145-5p inhibits H/R-induced cardiomyocyte apoptosis by targeting ROCK1.

This article presents the important research findings of our study on whether delayed retirement will affect the health of Chinese workers from the perspective of sustainability of physical health. The treatment group is those who continue to work after the current statutory retirement age and the control group are those who no longer work after current statutory retirement age, and the physical health of these people is used as a dependent variable. The samples are selected from the following six regions of China: Shanghai, Chengdu, Guangzhou, Beijing, Zhengzhou, and Shenyang. Quantitative studies of physical health status of the samples are carried out. The study illustrates that for female manual workers who are younger than 63 years old, female non-manual workers who are younger than 66 years old, male manual workers who are younger than 64 years old, and male non-manual workers who are younger than 67 years old, delaying retirement, and continuing to stay in work has no effect on their physical health. After these chronological age stages, however, it has significant impact on their health. These research findings have significant impact and value for the design of China’s delayed retirement policy.