Showing papers by "Dong-Ling Xu published in 2022"
TL;DR: In this paper , the authors extend the classical evidential reasoning approach to the linguistic belief-based approach, in which the hesitancy degrees are introduced to determine the weights of experts, and the LB-ER approach is further enhanced to deal with multi-expert multi-criteria decision-making problems, where the best worst method (BWM) is introduced to generate the weights.
Abstract: Evidential Reasoning (ER) approach is a widely used information aggregation method to deal with uncertain information in decision making. However, as decision-making problem becomes complicated, it is usually difficult for experts to provide accurate belief degrees for each evaluation grade. In this regard, the linguistic belief structure allows experts to give belief degrees with linguistic terms. In this study, we extend the classical ER approach to the linguistic belief-based ER (LB-ER) approach in which the hesitancy degrees are introduced to determine the weights of experts. Afterwards, the LB-ER approach is further enhanced to deal with multi-expert multi-criteria decision-making (MEMCDM) problems, where the best worst method (BWM) is introduced to generate the weights of criteria. Finally, to verify the practicability of the proposed method, we implement the method in lung cancer diagnosis.
8 citations
TL;DR: Wang et al. as discussed by the authors proposed a new type of cementitious material composited of DS, ground granulated blast furnace slag (GGBS), steel slag, and flue gas desulfurization gypsum (FGDG).
2 citations
TL;DR: In this article , 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.
Abstract: 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.
2 citations
TL;DR: In this article , the authors investigated the impact of a method to improve both sludge separation and granulation by coupling effluent sludge external conditioning with FeCl3 addition and then reintroducing it into the sequencing batch reactors (SBRs).
Abstract: The separation of light and heavy sludge, as well as the aggregation rate of floccular sludge, are two critical aspects of the rapid granulation process in sequencing batch reactors (SBRs) in the early stages. In this study, we investigated the impact of a method to improve both sludge separation and granulation by coupling effluent sludge external conditioning with FeCl3 addition and then reintroducing it into the SBR. By supplementation with 0.1 g Fe3+ (g dried sludge (DS))−1, the concentration of extracellular polymeric substances (EPS) and sludge retention efficiency greatly increased, whereas the moisture content and specific oxygen uptake rate (SOUR) sharply decreased within 24 h external conditioning. Aggregates (1.75 ± 0.05 g·L−1) were reintroduced into the bioreactor once daily from day 13 to day 15. Afterwards, on day 17, aerobic granules with a concentration of mixed liquor suspended solids (MLSS) of 5.636 g/L, a sludge volume index (SVI30) of 45.5 mL/g and an average size of 2.5 mm in diameter were obtained. These results suggest that the external conditioning step with both air-drying and the addition of Fe3+ enhanced the production of EPS in the effluent sludge and improved rapid aggregation and high sludge retention efficiency. Consequently, the reintroduced aggregates with good traits shortened the time required to obtain mature aerobic granular sludge (AGS) and properly separate light and heavy sludge. Indeed, this method jump-started the aggregation, and rapid granulation processes were successful in this work. Additionally, while the removal efficiency of chemical oxygen demand (COD) and nitrogen from ammonium (NH4+-N) decreased when reintroducing the treated sludge into the SBR, such properties increased again as the AGS matured in the SBR, up to removal efficiencies of 96% and 95%, respectively.
TL;DR: In this paper , a long-term Perishable Inventory Routing Problem with multiple products, static demand, and single vehicle, in the setting of vendor managed inventory, is considered, and the optimal solution is very close to the solution point where total inventory holding cost and transportation cost are close.
Abstract: Abstract 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.
TL;DR: In this article , a long-term Perishable Inventory Routing Problem with multiple products, static demand, and single vehicle, in the setting of vendor managed inventory, is considered, and the optimal solution is very close to the solution point where total inventory holding cost and transportation cost are close.
Abstract: Abstract 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.