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Cutting Tools Assignment and Control Using Neutrosophic Case-Based Reasoning and Best Worst Method
Cutting tools management is one of the major issues in metal cutting operations. Most of the problems in cutting tools management were mostly addressed using optimization, heuristic, and simulation techniques. This important problem was not studied using decision-based approaches. This study proposed a decision support system (DSS) that can perform part-cutting tools assignment and control decisions by integrating a neutrosophic case-based reasoning and the best-worst method (BWM) in metal cutting processes. Specifically, this study utilized the integration of case-based reasoning (CBR) and single-valued neutrosophic set (SVNS) theories in artificial intelligence (AI). Furthermore, the proposed DSS applies the BWM to determine optimal weights for case attributes from multicriteria decision-making (MCDM). The system retrieves the most similar historical cases using a neutrosophic CBR and the BWM to adapt their cutting tool requirements to the current product orders. In addition, it revises retrieved cases (tool sets) depending on attribute differences between new and retrieved cases using rule-based reasoning (RBR) from experts. This study provided new insights regarding the application of a neutrosophic CBR and its integration with the BWM. Specifically, the integration of SVNS, CBR, and BWM was not articulated in cutting tools management problems. A numerical example was illustrated in a computer-simulated environment to show the applicability of the proposed DSS using lathe machine operations.
On the Odd Perks Exponential Model: An Application to Quality Control Data
In the present study, the group acceptance plan is examined when the lifetime of an item follows the odd Perks exponential distribution, and a large number of items regarded as a group are evaluated simultaneously. The crucial parameters are derived from the consumer risk and the test termination period. The operating characteristics function values are generated for various quality levels. An optimized group acceptance plan and comparison of group acceptance sampling plan with the ordinary sampling plan are also presented. Additionally, a graphical illustration of operating characteristics for diverse groups and parametric values is provided. The minimum ratios of the actual average life to the stipulated average life are likewise computed at the prescribed producer’s risk. Examples are used to illustrate the outcomes via our algorithm under the odd Perks exponential distribution setting. It is explained using a quality control dataset to establish its practical versatility.
Stochastic P-Robust Approach to a Centralized Two-Stage DEA System with Resource Waste
Uncertain data and undesirable outputs are two challenging issues in traditional data envelopment analysis (DEA) models while dealing with the environmental efficiency estimation of decision-making units (DMUs). This study considers Stackelberg and the centralized game theory approach in a two-stage DEA model for evaluating DMUs in the presence of uncertainty and undesirable outputs simultaneously. To tackle the uncertainty, we apply the p-robust technique and assume that undesirable outputs are weakly disposable. The proposed fractional models are linearized using the Charnes and Cooper transformation. We utilize the new models for a real dataset drawn from 11 oil generation ports in the Persian Gulf region consisting of two stages: an oil production stage and a wastewater treatment stage. The results revealed that the managers should take different strategies in environmental efficiency evaluation including undesirable impacts and also efficiency improvement in increasing oil generation. Further, the empirical results showed that the stochastic p-robust approach for controlling the conservatism level leads to a more conservative solution, and policymakers could recognize the significant steps that should be followed to improve each oil generation unit’s environmental performance. Also, to show the reliability and accuracy of the results and the effect of the decision-maker’s preference, a detailed sensitivity analysis is performed.
A Three-Stage Game Analysis of Private Lending Rate Ceiling: Necessity, Impact, and Solution
The ceiling on private lending interest rates is a powerful financial tool to maintain financial stability and reduce usury. Especially now that peer-to-peer lending has encountered some challenges, the ceiling is an essential way to regulate this market. The purpose of this paper is to analyze the necessity and the impact of such a powerful tool and also to find the optimal solution to determine it. The paper proves the ceiling on private lending interest rates is an inevitable choice by using an evolutionary game. However, it is shown that the lowering of the ceiling on private lending interest rates will increase both the difficulty of financing for SMEs and the default rate. Two optimal solutions to the ceiling are obtained in this study, which also prevent an increase in borrowing costs.
Additive Slack-Based Measure for a Two-Stage Structure with Shared Inputs and Undesirable Feedback
The two-stage data envelopment analysis models are among the widely used mathematical programming approaches to evaluate the performance of two-stage structures. In this paper, a two-stage structure with shared inputs and feedback is studied. To reduce undesirable outputs, an additive slack-based measure model is proposed to evaluate the stages and overall efficiencies, while undesirable outputs are weakly disposable. As it does not require determining the weights for combining stages’ efficiencies, all Pareto optimal stages’ efficiencies can be gained. In addition, the proposed approach can identify desirable outputs from undesirable outputs, thereby avoiding the need for weighting. This advantage from the aspect of multiobjective programming helps internal evaluation of the network model to match priorities of managers. The proposed nonlinear model is reformulated as a second-order cone program, which is a convex optimization problem that can be solved to global optimality. This is a computational improvement over the parametric models in the literature. Furthermore, the proposed model is applied for country-wise and area-wise performance evaluations of a real industrial application dataset in mainland China. Results show that the efficiency of the overall system relies between the efficiencies of the two stages and for all DMUs, the first stage’s efficiency scores are always higher than the second stage ones in both evaluations. Also, the Pearson correlation coefficient test results show that the overall efficiency is more correlated with the waste disposal stage. Finally, to show the effect of the decision maker’s preference, a detailed sensitivity analysis is performed.
Green Fuzzy Tourist Trip Design Problem
The shift from mass tourism to more personalized travel denotes great importance in the construction of tourist itineraries. Given the negative impacts of transport and tourism on the environment, sustainability criteria play an important role. The Tourist Trip Design Problem is related to the design of itineraries for tourists. Planning is complex in tourist regions of developing countries where the information associated with tourist activities is difficult to access, vague, and incomplete. With this information, tourists must plan their trip, and the conditions and limitations they establish for it are flexible and imprecise. Fuzzy optimization can address problems with this type of information and constraints. Therefore, in this paper, an analysis of the tourism supply chain is carried out, taking as a case study the Department of Sucre on Colombia's Caribbean coast. A Multiconstraint Multimodal Team Orienteering Problem with Time Windows and fuzzy constraints is developed to model the tourism trip design problem that maximizes profit and minimizes CO2 emissions. The model is tested using datasets from the literature and the real world. The results demonstrate consistency with the fuzzy approach and generate a set of low-emission solutions.