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As manufacturers continue to provide new and unique products and services for their customers, we argue that warranty menus with differentiated options are an often-overlooked unique selling point. Meanwhile, there is a huge swathe of customers who own durable products which are still in use even after the expiration of a warranty. It is in this area that we develop a research perspective which urges customers to consider warranty plans and post-warranty maintenance strategies simultaneously when making a purchasing decision. In this paper, through the lens of a consumer, we propose an integrated model of warranty and post-warranty maintenance under a flexible renewable warranty. This model takes into account repair limits and multiple failure types with time-varying probabilities. As part of this, a new post-warranty maintenance strategy is designed, in which the repair time-based corrective replacement and the age-based preventive replacement are combined. The life cycle cost rate of the product is minimised by a synthetic decision-making about the consumer's warranty purchase and post-warranty maintenance strategy. Numerical studies show that there exists a joint optimal solution, which can help the customer to develop an optimal overall warranty-maintenance strategy under the warranty menu with minimised cost rate. An integrated model of warranty and post-warranty maintenance is developed. The warranty product has minor and catastrophic failures with time-varying probabilities. A flexible renewable warranty is considered for the consumer. A post-warranty maintenance strategy with the repair time limit is proposed. The optimal warranty-maintenance strategy is derived to minimise the Life cycle cost rate. [ABSTRACT FROM AUTHOR]

The instability of product quality and low printing efficiency are the main obstacles to the widespread application of 3D printing in the manufacturing industry. Optimizing printing parameters can substantially improve product quality and printing efficiency. However, existing methods for optimizing process parameters primarily rely on computationally expensive numerical simulations or costly physical experiments, which cannot balance model accuracy and experiment cost. To the best of our knowledge, almost no relevant papers have been found to address the issues of product quality and printing efficiency in 3D printing from experimental data‐driven perspective. In this paper, we propose a method that integrates multiobjective Bayesian optimization (MOBO) with experimental data‐driven, aiming at obtaining more accurate optimization results at a lower cost. Distinguishing from previous studies, the proposed method utilizes experimental data instead of predicted values to update the model and find the optimal process parameters based on expected hypervolume improvement. The results of the 3D printing case study show that the proposed method can better model and optimize the highly fluctuating 3D printing process and obtain the optimal process parameters at a much lower cost. In addition, confirmatory experiments verify that the proposed method achieves higher printing efficiency while maintaining product quality. [ABSTRACT FROM AUTHOR]

Microgrid has attracted more and more attention to provide backup power for customers in the case of power grid outages. Microgrid expansion planning is significant to handle the increasing customer demand and to enhance power resilience. Current research about long‐term microgrid expansion planning rarely if ever considered the uncertainties associated with energy storage and power generation units, for example, battery cycle degradation. These factors have important influence on the performance of microgrid expansion planning in reality. In this paper, a long‐term microgrid expansion planning model with multiple energy resource is presented. Deep reinforcement learning method is used to obtain the cost‐effective microgrid expansion policies to enhance power resilience. In the case study, optimal microgrid expansion planning is achieved based on the proposed model. The impacts of battery degradation and resilience constraint on microgrid expansion policy optimization are also investigated. The simulation results prove the effectiveness of the proposed method on economic and resilient microgrid expansion planning. [ABSTRACT FROM AUTHOR]

Integrated parameter design and tolerance design (IPTD) is an effective way to improve product quality and reduce manufacturing cost in micro-manufacturing processes. However, the current modeling techniques rarely analyze the influence of model uncertainty on the optimal machining parameters. It may not obtain the robust optimal machining parameters due to model uncertainty. This paper proposes a novel economically integrated design method which considers the correlations among quality characteristics, the variability of manufacturing process, and uncertainty in the model predictions. First, a new rework and scrap cost functions are established via Monte Carlo simulation. Meanwhile, an integrative expected quality loss function is constructed based on interval analysis theory for quantifying model uncertainty. Second, to make the proposed method closer to the practical micro-manufacturing problem, we consider the trade-offs among cost, time, and success rate in the modeling process. Finally, a total cost model is proposed to take into account the quality loss, tolerance cost, unit manufacturing cost, and scrap cost. The effectiveness of the proposed modeling method is verified by a laser beam micro-drilling manufacturing. The results illustrate that the proposed method can achieve better robustness property and economically than the traditional method that does not consider model uncertainty. [ABSTRACT FROM AUTHOR]

In robust parameter design (RPD), estimating the response surface model from the noisy experimental data is critical for parameter optimization. The multivariate Gaussian process (MGP) model has become a popular tool for response surface modelling. However, most existing MGP models may not effectively evaluate the uncertainty of noisy data. This article proposes a stochastic MGP for efficient emulation and robust optimization by considering uncertainty. Firstly, the hierarchical modelling technique is adopted to estimate the relationship model with noisy data. Secondly, the hyperparameters are estimated by the Gibbs technique, and the Bayesian averaging method is used to measure the parameter uncertainty. Finally, a novel optimization model integrated with the Bayesian averaging and quality loss function is developed to find the optimal solution. Two case studies are used to illustrate the effectiveness of the proposed method. The results show that the proposed approach obtains smaller quality loss than the existing ones. [ABSTRACT FROM AUTHOR]

Many industrial process optimisation methods rely on empirical models that relate output responses to a set of design variables. One of the most crucial problems in process optimisation is how to efficiently implement model selection and model estimation. This paper presents a Bayesian hierarchical modelling approach to process optimisation based on the seemingly unrelated regression (SUR) models. This approach can estimate a set of predictors to be included in a model based on a Bayesian hierarchical procedure (i.e. model selection) and then give model prediction based on a Bayesian SUR model (i.e. model estimation). Meanwhile, a two-stage optimisation strategy considering practitioners' preference information is proposed in process optimisation, which initially finds a set of non-dominated input settings and then determines the best one based on the similarity to an ideal solution method. The performance and effectiveness of the proposed method are illustrated with both simulation studies and a case study. The comparison results demonstrate that the proposed method can be a good alternative to existing process optimisation methods. [ABSTRACT FROM AUTHOR]

In robust parameter design, it is common to use computer models to simulate the relationships between input variables and output responses. However, for the contaminated experimental data, the model uncertainty between computer models and actual physical systems will seriously impair the robustness of the optimal input settings. In this paper, we propose a new weighted robust design approach concerning the model uncertainty from outliers based on the robust Gaussian process model with a Student-t likelihood (StGP). Firstly, to reduce the impact of outliers on the output means and variances, the StGP modelling technique is adopted to estimate the relationship models for contaminated data. Secondly, the Gibbs sampling technique is employed to estimate model parameters for better mixing and convergence. Finally, an optimisation scheme integrating the quality loss function and confidence interval analysis approach is built to find the feasible optimisation solution. Meanwhile, the hypersphere decomposition method and data-driven method are applied to determine the relative weights of objective functions. Two examples are used to demonstrate the effectiveness of the proposed approach. The comparison results show that the proposed approach can achieve better performance than other approaches by considering the model uncertainty from outliers. [ABSTRACT FROM AUTHOR]