With the rapid development of smart manufacturing, the joint optimization of production, equipment maintenance, and product quality control has become key to improving manufacturing system performance. To formulate maintenance strategies for manufacturing systems, most studies use equipment degradation level as an indicator of whether maintenance actions should be initiated. However, product quality often deteriorates before equipment degradation is detected. This study develops a new joint optimization model based on quality information. First, we quantify a production quality risk indicator based on the deviation of key quality characteristics and potential risk and formulate a preventive maintenance strategy on that basis. Then, we construct a dual-objective optimization model using Pareto frontier analysis that accounts for both cost minimization and equipment availability in cycle maximization, aiming to improve the accuracy of maintenance strategies. Finally, the effectiveness of the proposed method is demonstrated through specific examples and sensitivity analysis, as well as a comparative study. The results show that the proposed method achieves the lowest total cost among the considered policies, while keeping a high availability comparable to existing policies. This outcome can provide decision support for the high-reliability, low-cost operation and maintenance of smart manufacturing systems.

Multiple response optimization (MRO) aims to obtain an optimal solution by optimizing several responses simultaneously for quality improvement. The preference parameters in the traditional MRO are exact real numbers. However, qualified uncertainties of marginally qualified responses can lead to preference parameters, such as the lower and upper bounds, to obtain interval forms. To solve the MRO involving conflicting responses using desirability functions under interval preference parameter, an intuitionistic fuzzy desirability function method (IFDFM) is proposed, which includes the experiments and data regression phase, the intuitionistic fuzzy desirability function set (IFDFS) construction phase, the optimization and decision-making phase. Compared with the existing desirability function methods, in the proposed method, the IFDFSs are introduced. Namely, after evaluating interval preference parameters with marginally qualified responses, the IFDFSs are constructed to measure the qualified uncertainties of multiple response performances based on the membership, non-membership and hesitant degrees. The proposed IFDFM is verified on the tire-tread compound problem. The results show that the IFDSs of the optimal solution obtained by the IFDFM can better measure the desirability uncertainties of the marginally qualified responses. By comparing results of the IFDFM under different combinations of marginally qualified responses, the influence orders of responses on optimal solutions are obtained, providing important information for developing quality improvement strategies.

This study considers a response surface methodology (RSM) variation in which a response has multiple central tendencies (MCTs) that can have multiple influences on a sheet metal part. This is formulated as a set response surface (SRS) problem, which, in industrial practice, is studied using the thinning ratio example. The set RSM (SRSM), consisting of three phases, is proposed to solve the SRS problem. The first phase is the problem definition and regional division phase, where based on the analysis of MCTs and response influence, a sheet metal part that needs quality improvement is divided intoqregions. The second phase is the experiment and data regression phase. The third phase is the optimization and interactive decision phase, where the condition relaxation strategy (CRS) is proposed, and relaxation is obtained based on the barrier and engineering requirement analysis. The optimization models are constructed for the (q+1)-level optimal solutions using the CRS. The proposed SRSM is verified by tests on the wrinkle and crack problem of the inner plate of the back door. A possible optimization model combination and trend analysis strategy is proposed to solve the CRS challenge for higher dimensions in the tendencies.

To simultaneously deal with the uncertain interaction topology, parametric errors and external disturbances, this paper proposes a new coordinated control scheme for the platoon composed of nonlinear and heterogeneous automated vehicles (AVs). In this scheme, different perturbations are dealt with separately to reduce the contraction among them by using the sliding mode control theory. Considering the individual dynamics, a distributed coordinated controller including both lateral and longitudinal motions is designed for each AV with online estimation of the unknown parameters and disturbances. On the sliding surfaces of longitudinal motion, a decoupling approach using the eigenvalue decomposition of topological matrix and linear transformation is proposed to deal with the topological uncertainty. Then the dynamical system of platoon coupled by the information flow is decomposed into the subsystems with lower order. The relationship of the robust performance between the original and decoupled system is analyzed theoretically. Based on this theoretical conclusion, a numerical way is given based on linear matrix inequality (LMI) theory to design the parameters of sliding motion dynamics. By this way, the exact topological matrix is not necessary and only the bound of its eigenvalue is required. The effectiveness of the proposed strategy is validated by several comparative simulations under variety conditions.

To solve multiple response optimization problems that often involve incommensurate and conflicting responses, a robust interactive desirability function approach is proposed in this article. The proposed approach consists of a parameter initialization phase and calculation and decision-making phases. It considers a decision maker's preference information regarding tradeoffs among responses and the uncertainties associated with predicted response surface models. The proposed method is the first to consider model uncertainty using an interactive desirability function approach. It allows a decision maker to adjust any of the preference parameters, including the shape, bound, and target of a modified robust function with consideration of model uncertainty in a single and integrated framework. This property of the proposed method is illustrated using a tire tread compound problem, and the robustness of the adjustments for the approach is also considered. The new method is shown to be highly effective in generating a compromise solution that is faithful to the decision maker's preference structure and robust to uncertainties associated with model predictions.

This paper uses the priority labels to express the detailed prioritized relationship between criteria and develops some scaled prioritized geometric aggregation operators, including the scaled prioritized geometric score (SPGS) operator and the scaled prioritized geometric averaging (SPGA) operator. We also present the uncertain scaled prioritized geometric scoring (USPGS) operator and the uncertain scaled prioritized geometric averaging (USPGA) operator. We investigate the properties of these operators and build the models to derive the weights by maximizing square deviations from a possible range to distinguish the candidate alternatives. The principal advantage of these scaled prioritized geometric aggregation operators is that they are very stable and satisfy monotonicity. Furthermore, we investigate approaches to multi-attribute decision making based on the proposed operators or models and examples are illustrated to show the feasibility and validity of the new approaches. Finally, some further discussions are given.

The Bonferroni mean (BM) was originally presented by Bonferroni and had been generalized by many researchers on Atanassov's intuitionistic fuzzy sets (AIFSs) for its capacity to capture the interrelationship between input arguments. Nevertheless, the forms of the combinations of the newly proposed interaction theory on AIFSs with BM are very single, and the existing BMs on AIFSs are not consistent with aggregation operations on the ordinary fuzzy sets. As complements to the existing generalizations of BM under Atanassov's intuitionistic fuzzy environment, this paper develops the extended Atanassov's intuitionistic fuzzy interaction Bonferroni mean (EIFIBM) and the extended weighted Atanassov's intuitionistic fuzzy interaction Bonferroni mean, which can evolve into a series of BMs by taking different generator functions that reflect the different preference attitudes of the decision makers. In addition, some of the EIFIBMs are consistent with aggregation operations on the ordinary fuzzy sets, and some of the EIFIBMs consider the interactions between the membership and nonmembership functions of different Atanassov's intuitionistic fuzzy sets; thus, they can be used in more decision situations. We investigate the properties of these new extensions and apply them to multiple-attribute decision-making problems with admissible orders. Finally, numerical examples show the validity and feasibility of the new approaches.

The geometric Bonferroni mean (GBM) can capture the interrelationships between input arguments, which is an important generalization of Bonferroni mean (BM). In this paper, we combine geometric Bonferroni mean (GBM) with the power geometric average (PGA) operator under intuitionistic fuzzy environment and present the intuitionistic fuzzy geometric power Bonferroni mean (IFPGBM) and the weighted intuitionistic fuzzy power geometric Bonferroni mean (WIFPGBM). The desirable properties of these new extensions of Bonferroni mean and their special cases are investigated. We list the detailed steps of multiple attribute group decision making with the developed IFPGBM or WIFPGBM, and give a comparison of the new extensions of Bonferroni mean by this paper with the corresponding existing intuitionistic fuzzy Bonferroni means. Finally, examples are illustrated to show the validity and feasibility of the new approaches.

The Bonferroni mean (BM) was originally presented by Bonferroni and had been generalized by many researchers for its capacity to capture the interrelationship between input arguments. Nevertheless, the existing intuitionistic fuzzy BMs only consider the effects of membership function or nonmembership function of different intuitionistic fuzzy sets (IFSs). As complements to the existing generalizations of BM under intuitionistic fuzzy environment, this paper also considers the interactions between the membership function and nonmembership function of different IFSs and develops the intuitionistic fuzzy interaction BM and the weighted intuitionistic fuzzy interaction BM. We investigate the properties of these new extensions of BM and discuss their special cases. Furthermore, the detailed steps of multiple attribute decision making with the presented operators under intuitionistic fuzzy environment are investigated and an example is illustrated to show the validity and feasibility of the new approach.

As a useful generalization of fuzzy sets, the hesitant fuzzy set is designed for situations in which it is difficult to determine the membership of an element to a set because of ambiguity between a few different values. In this paper, we define the ith-order polymerization degree function and propose a new ranking method to further compare different hesitant fuzzy sets. In order to obtain much more information in the process of group decision making, we combine the power average operator with the Bonferroni mean in hesitant fuzzy environments and develop the hesitant fuzzy power Bonferroni mean and the hesitant fuzzy power geometric Bonferroni mean. We investigate the desirable properties of these new hesitant fuzzy aggregation operators and discuss some special cases. The new aggregation operators are utilized to present techniques for hesitant fuzzy multiple attribute group decision making. Finally, a numerical example is provided to illustrate the effectiveness of the developed techniques.

The aim of this paper is to develop a new compatibility, which is very suitable to deal with group decision making (GDM) problems involving interval multiplicative preference relations, based on the continuous ordered weighted geometric averaging (COWGA) operator. First, we define some concepts of the compatibility degree and the compatibility index for the two interval multiplicative preference relations based on the COWGA operator. Then, we study some desirable properties of the compatibility index and investigate the relationship between each expert's interval multiplicative preference relation and the synthetic interval multiplicative preference relation. The prominent characteristic of the compatibility index based on the COWGA operator is that it can deal with the compatibility of all the arguments in two interval arguments considering the risk attitude of decision maker rather than the compatibility of the two simple points in intervals. Second, in order to determine the experts' weights in the GDM with the interval multiplicative preference relations, we propose an optimal model based on the criterion of minimizing the compatibility index. Finally, we give a numerical example to develop the new approach to GDM with interval multiplicative preference relations.