Optimizing The Global Performance Of Build-to-order Supply Chains

Build-to-order supply chains (BOSCs) have recently received increasing attention due to the shifting focus of manufacturing companies from mass production to mass customization. This shift has generated a growing need for efficient methods to design BOSCs. This research proposes an approach for BOSC design that simultaneously considers multiple performance measures at three stages of a BOSC Tier I suppliers, the focal manufacturing company and Tier I customers (product delivery couriers). We present a heuristic solution approach that constructs the best BOSC configuration through the selection of suppliers, manufacturing resources at the focal company and delivery couriers. The resulting configuration is the one that yields the best global performance relative to five deterministic performance measures simultaneously, some of which are nonlinear. We compare the heuristic results to those from an exact method, and the results show that the proposed approach yields BOSC configurations with near-optimal performance. The absolute deviation in mean performance across all experiments is consistently less than 4%, with a variance less than 0.5%. We propose a second heuristic approach for the stochastic BOSC environment. Compared to the deterministic BOSC performance, experimental results show that optimizing BOSC performance according to stochastic local performance measures can yield a significantly different supply chain configuration. Local optimization means optimizing according to one performance measure independently of the other four. Using Monte Carlo simulation, we test the impact of local performance variability on the global performance of the BOSC. Experimental results show that, as variability of the local performance increases, the mean global performance decreases, while variation in the global performance increases at steeper levels

Quantile-based optimization under uncertainties using adaptive Kriging surrogate models

Uncertainties are inherent to real-world systems. Taking them into account is crucial in industrial design problems and this might be achieved through reliability-based design optimization (RBDO) techniques. In this paper, we propose a quantile-based approach to solve RBDO problems. We first transform the safety constraints usually formulated as admissible probabilities of failure into constraints on quantiles of the performance criteria. In this formulation, the quantile level controls the degree of conservatism of the design. Starting with the premise that industrial applications often involve high-fidelity and time-consuming computational models, the proposed approach makes use of Kriging surrogate models (a.k.a. Gaussian process modeling). Thanks to the Kriging variance (a measure of the local accuracy of the surrogate), we derive a procedure with two stages of enrichment of the design of computer experiments (DoE) used to construct the surrogate model. The first stage globally reduces the Kriging epistemic uncertainty and adds points in the vicinity of the limit-state surfaces describing the system performance to be attained. The second stage locally checks, and if necessary, improves the accuracy of the quantiles estimated along the optimization iterations. Applications to three analytical examples and to the optimal design of a car body subsystem (minimal mass under mechanical safety constraints) show the accuracy and the remarkable efficiency brought by the proposed procedure

Reliability based robust design optimization based on sensitivity and elasticity factors analysis

In this paper, a Reliability Based Robust Design Optimization (RBRDO) based on sensitivity and elasticity factors analysis is presented. In the first step, a reliability assessment is performed using the First-and Second Order Reliability Method (FORM)/ (SORM), and Monte Carlo Simulation. Furthermore, FORM method is used for reliability elasticity factors assessment, which can be carried out to determine the most influential parameters, these factors can be help to reduce the size of design variables vector in RBRDO process. The main objective of the RBRDO is to improve both reliability and design of a cylindrical gear pair under uncertainties. This approach is achieved by integration of two objectives which minimize the variance and mean values of performance function. To solve this problem a decoupled approach of Sequential Optimization and Reliability Assessment (SORA) method is implemented. The results obtained shown that a desired reliability with a robust design is progressively achieved

OPTIMAL TESTING STRATEGIES FOR GENETICALLY MODIFIED WHEAT

A stochastic optimization model was developed to determine optimal testing strategies, costs, and risks of a dual marketing system. The model chooses the testing strategy (application, intensity, and tolerance) that maximizes utility (minimizes disutility) of additional system costs due to testing and quality loss and allows simulation of the risk premium required to induce grain handlers to undertake a dual marketing system versus a Non-GM system. Cost elements including those related to testing, quality loss, and a risk premium were estimated for a model representing a grain export chain. Uncertainties were incorporated and include test accuracy, risk of adventitious commingling throughout, and variety declaration. Sensitivities were performed for effects of variety risks, penalty differentials, re-elevation discounts, import tolerances, variety declaration, risk aversion, GM adoption, and domestic end-user.Segregation, Testing, Tolerance, Genetically Modified, Wheat, Risk Premium, Crop Production/Industries, Research and Development/Tech Change/Emerging Technologies,

Adaptive active subspace-based metamodeling for high-dimensional reliability analysis

To address the challenges of reliability analysis in high-dimensional probability spaces, this paper proposes a new metamodeling method that couples active subspace, heteroscedastic Gaussian process, and active learning. The active subspace is leveraged to identify low-dimensional salient features of a high-dimensional computational model. A surrogate computational model is built in the low-dimensional feature space by a heteroscedastic Gaussian process. Active learning adaptively guides the surrogate model training toward the critical region that significantly contributes to the failure probability. A critical trait of the proposed method is that the three main ingredients-active subspace, heteroscedastic Gaussian process, and active learning-are coupled to adaptively optimize the feature space mapping in conjunction with the surrogate modeling. This coupling empowers the proposed method to accurately solve nontrivial high-dimensional reliability problems via low-dimensional surrogate modeling. Finally, numerical examples of a high-dimensional nonlinear function and structural engineering applications are investigated to verify the performance of the proposed method

Complexity Management to design and produce customerspecific hydraulic controls for mobile applications

Complexity management is the key to success for mobile machinery where the variety of customers and applications requires individual solutions. This paper presents the way Bosch Rexroth supports each OEM with hydraulic controls – from specification and conception towards application and production. It gives examples how platforms and processes are optimized according to the customer needs. The demand for flexible, short-term deliveries is met by an agile production with the technologies of Industry 4.0