Water quality instability is a major constraint in giant freshwater prawn farming, particularly in land-based systems where fluctuations in environmental conditions can reduce survival and productivity. This study applied an integrated CRITIC–TOPSIS framework to evaluate alternative water quality management strategies for giant freshwater prawn farming under multiple operational and production criteria. Four alternatives, representing different levels of management sophistication from conventional monitoring to a full IoT-based real-time monitoring system, were assessed using five criteria: Cost, Labor, Energy, Survival, and Yield. The CRITIC method was used to determine objective criterion weights, while TOPSIS was employed to rank the alternatives according to their relative closeness to the ideal solution. The CRITIC results indicated that Cost (0.2523) and Energy (0.2506) were the most influential criteria, followed by Labor (0.1681), whereas Survival and Yield each received a weight of 0.1645. The TOPSIS results showed that Alternative 4 was the most suitable option, with the highest closeness coefficient (0.5534), followed by Alternative 3 (0.5084), Alternative 2 (0.4583), and Alternative 1 (0.4466). Alternative 4 also achieved the best production performance, with 93% survival, 4.65 kg yield, and the lowest labor requirement (17 h), although it required the highest cost (13,746 Baht) and energy consumption (35 kWh). Compared with the conventional strategy, Alternative 4 improved survival by 11 percentage points, increased yield by 0.55 kg, and reduced labor by 18 h. The findings demonstrate that the integrated CRITIC–TOPSIS framework can serve as an effective decision-support tool for selecting water quality management strategies in freshwater prawn farming by balancing resource requirements and production outcomes. Under the present evaluation framework, the IoT-based real-time monitoring strategy was identified as the most suitable alternative for land-based giant freshwater prawn farming.

A new approach is applied in the process of measuring the efficiency of decision-making units (DMUs) through the cross-efficiency evaluation method. Ideal and Anti-Ideal models are generated to form a comprehensive method based on the cross-efficiency evaluation method. The two models are formulated and combined to the Data Envelopment Analysis using the CRITIC method. In a comparative analysis based on three numerical examples, the proposed approach can lead to achieving a more reliable result than one based on an individual method.

The ranking of decision-making units (DMUs) is one of the main issues in data envelopment analysis (DEA). Hence, many different ranking models have been proposed. However, each of these ranking models may produce different ranking results for similar problems. Therefore, it is wise to try different ranking models and aggregate the results of each ranking model that provides more reliable results in solving the ranking problems. In this paper, a novel ranking method (Aggregating the results of aggressive and benevolent models) based on the CRITIC method is proposed. To prove the applicability of the proposed ranking method, it is examined in three numerical examples, six nursing homes, fourteen international passenger airlines and seven biomass materials for processing into fuel briquettes. First, benevolent and aggressive models were used to calculate the efficiency rating for each DMU. As a result, the decision matrix was generated. In the decision matrix, the results of benevolent and aggressive models were viewed as criteria and DMUs were viewed as alternatives. Then, the weights of each criterion were generated by the CRITIC method. Finally, each DMU was ranked. In a comparative analysis, the proposed method can lead to achieving a more reliable decision than the method which is based on a stand-alone method.

Infectious waste disposal remains one of the most serious problems in the medical, social and environmental domains of almost every country. Selection of new suitable locations and finding the optimal set of transport routes for a fleet of vehicles to transport infectious waste material, location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Determining locations for infectious waste disposal is a difficult and complex process, because it requires combining both intangible and tangible factors. Additionally, it depends on several criteria and various regulations. This facility location problem for infectious waste disposal is complicated, and it cannot be addressed using any stand-alone technique. Based on a case study, 107 hospitals and 6 candidate municipalities in Upper-Northeastern Thailand, we considered criteria such as infrastructure, geology and social & environmental criteria, evaluating global priority weights using the fuzzy analytical hierarchy process (Fuzzy AHP). After that, a new multi-objective facility location problem model which hybridizes fuzzy AHP and goal programming (GP), namely the HGP model, was tested. Finally, the vehicle routing problem (VRP) for a case study was formulated, and it was tested using a hybrid genetic algorithm (HGA) which hybridizes the push forward insertion heuristic (PFIH), genetic algorithm (GA) and three local searches including 2-opt, insertion-move and interexchange-move. The results show that both the HGP and HGA can lead to select new suitable locations and to find the optimal set of transport routes for vehicles delivering infectious waste material. The novelty of the proposed methodologies, HGP, is the simultaneous combination of relevant factors that are difficult to interpret and cost factors in order to determine new suitable locations, and HGA can be applied to determine the transport routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation efficiently in this case.

Effective transshipment network is currently recognized as an important success determinant for most manufacturing organizations, because the transshipment management has significant impact on cost and environmental impact. Due to the complexity of the multi-criteria transshipment problem for infectious waste management (IWM) for this case, forty hospitals and three candidate disposal municipalities in Northeastern Thailand, a novel holistic approach (combination of fuzzy AHP, transshipment model and DEA) was developed for solving this problem. We first utilized the fuzzy AHP technique to calculate the location weights of each candidate disposal municipalities. Secondly, a new cost-based transshipment model was formulated and solved in order to provide the set of feasible solutions. These solutions can be viewed as decision making units (DMUs), inputs and outputs. Finally, DEA-CCR model was applied to calculate the efficiency scores of candidate DMUs. The study results demonstrated that the proposed holistic approach can help decision makers (DMs) to choose a suitable transshipment network for IWM. The major advantage of the proposed holistic approach is that both costs and environmental impacts under constraints are focused on simultaneously. Future work will apply the developed approach with other real-world complex problems to enhance the validity of the research output further. For large-size transshipment problems in which an exact solution cannot be found, meta-heuristics must be applied.

Fake news has become a major problem affecting people, society, the economy and national security. This work proposes a combined deep learning model based on the ideal distance weighting method for fake news detection. The proposed model was validated on the ISOT and COVID-19 fake news datasets. Firstly, the ISOT and COVID-19 fake news datasets were collected. Secondly, the training-based models were used to provide accuracy values. After that, these values were transformed into criteria weights using the new ideal distance weighting method. Finally, the prediction value of the proposed model is calculated by the criteria weights. The results show that the proposed method is effective to distinguish the fake news datasets.

In this paper, a novel combination method is offered to integrate the results of two new relative closeness models, called relative closeness benevolent (RCB) and relative closeness aggressive (RCA) models, for ranking all DMUs. To prove the applicability of the proposed method, it is examined in three numerical examples, performance assessment problem, six nursing homes and fourteen international passenger airlines. Firstly, RCB and RCA models were formulated in order to generate the cross-efficiency intervals matrix (CEIM). After obtaining CEIM, the RC index was utilized to generate a combined cross-efficiency matrix (combined CEM). In combined CEM, target DMUs were viewed as criteria and DMUs were viewed as alternatives. After that, the weights of each criterion were generated using a new weighting method based on standard deviation technique (MSDT). Finally, all DMUs were evaluated and ranked. Comparison with existing cross-efficiency models indicates the more reliable results through the use of the proposed method.