Derivation of the Optimal Solution for the Economic Production Quantity Model with Planned Shortages without Derivatives

In this paper, we study a reformulation of the Economic Production Quantity (EPQ) problem. We study a more general version of the problem first and derive the conditions for an optimal solution, as well as the optimal solution itself, all without using derivatives. Then, we apply the approach to the reformulated EPQ problem. This version of the EPQ problem has been tackled by a number of researchers, wherein they have derived the conditions for the optimal solution and proposed algebraic derivations. However, their derivations for the conditions, as well as the optimal solution, have been shown to be questionable. Other than being questionable, the existing approaches are so complicated that they defeat the purpose of simplifying the optimization by using a derivative-free approach. We propose a correct and more succinct, much less complicated approach to derive the conditions and the optimal solution without using derivatives.

Simulation of Colloidal Stability and Aggregation Tendency of Magnetic Nanoflowers in Biofluids

A population balance model for the aggregation of iron oxide nanoflowers (IONfs) is presented. The model is based on the fixed pivot technique and is validated successfully for four kinds of aggregation kernels. The extended Derjaguin, Landau, Verwey, and Overbeek (xDLVO) theory is also employed for assessing the collision efficiency of the particles, which is pertinent to the total energy of the interaction. Colloidal stability experiments were conducted on IONfs for two dispersant cases—aqueous phosphate buffered saline solution (PBS) and simulated body fluid (SBF). Dynamic light scattering (DLS) measurements after 24-h of incubation show a significant size increase in plain PBS, whereas the presence of proteins in SBF prevents aggregation by protein corona formation on the IONfs. Subsequent simulations tend to overpredict the aggregation rate, and this can be attributed to the flower-like shape of IONfs, thus allowing patchiness on the surface of the particles that promotes an uneven energy potential and aggregation hindering. In silico parametric study on the effects of the ionic strength shows a prominent dependency of the aggregation rate on the salinity of the dispersant underlying the effect of repulsion forces, which are almost absent in the PBS case, promoting aggregation. In addition, the parametric study on the van der Waals potential energy effect—within common Hamaker-constant values for iron oxides—shows that this is almost absent for high salinity dispersants, whereas low salinity gives a wide range of results, thus underlying the high sensitivity of the model on the potential energy parameters.

Modelling Key Performance Indicators in a Gamified Waste Management Tool

Broader understanding of waste management has the potential to bring about broad societal change impacting the climate crisis and public health. We present existing waste management tools and commercially-available games involving waste management, highlighting the strengths and opportunities left unaddressed by these tools in educational contexts and planning use cases. A survey motivates the need for enhanced interactive tools providing clear feedback through quick-visibility performance indicators. After identifying an opportunity to build upon highly-detailed multi-criteria simulation tools, we explore the need for easy-to-read performance metrics that will bring to the field of waste management easily identifiable and measurable key performance indicators (KPIs) that vary alongside factors affecting waste management policies. Such metrics are introduced and detailed as part of a unified waste management model. We then develop a representative gamified educational tool based upon this model to be used by students, decision makers planning real-world policies, and the public. This simulator is built upon the Unity Game Engine and emulates waste management techniques and resulting KPIs within the context of a virtual city.

Error Distribution Model to Standardize LPUE, CPUE and Survey-Derived Catch Rates of Target and Non-Target Species

Indices of abundance are usually a key input parameter used for fitting a stock assessment model, as they provide abundance estimates representative of the fraction of the stock that is vulnerable to fishing. These indices can be estimated from catches derived from fishery-dependent sources, such as catch per unit effort (CPUE) and landings per unit effort (LPUE), or from scientific survey data (e.g., relative population number—RPN). However, fluctuations in many factors (e.g., vessel size, period, area, gear) may affect the catch rates, bringing the need to evaluate the appropriateness of the statistical models for the standardization process. In this research, we analyzed different generalized linear models to select the best technique to standardize catch rates of target and non-target species from fishery dependent (CPUE and LPUE) and independent (RPN) data. The examined error distribution models were gamma, lognormal, tweedie, and hurdle models. For hurdle, positive observations were analyzed assuming a lognormal (hurdle–lognormal) or gamma (hurdle–gamma) error distribution. Based on deviance table analyses and diagnostic checks, the hurdle–lognormal was the statistical model that best satisfied the underlying characteristics of the different data sets. Finally, catch rates (CPUE, LPUE and RPN) of the thornback ray Raja clavata, blackbelly rosefish Helicolenus dactylopterus, and common mora Mora moro from the NE Atlantic (Azores region) were standardized. The analyses confirmed the spatial and temporal nature of their distribution.

A Model-Based Design Method for the Correlation between Customer Feedback and Technical Design Parameters in the Context of Systems Engineering

Nowadays considering trends such as digitalization, automated driving as well as electric mobility in products in automotive development processes is a major challenge, which has led to an enormous increase in the number of product functions of technical systems. However, the recognized processes in automotive development are strongly component-oriented and such processes partially support the development of product functions. In order to meet future trends and ensure long term customer satisfaction, a transfer from component-oriented to function-oriented development is necessary. Accordingly, a holistic concept can be useful that enables the integration of customer feedback into the early phase of product development in the context of function-orientation. However, the customer feedback evaluation and their mapping with technical subsystems have been considered mainly in the context of component-oriented development. In this contribution, a method is proposed, which is generated in the context of a product model of product generation engineering. Product Generation Engineering enables the structuring of the development process of a product generation and supports function-oriented development. The Product Model provides customer- oriented development of mechatronic products. The proposed method is achieved in the sense of model-based systems engineering and validated by the exemplarily application of a case study of a specific vehicle. Both the past and current product generations of the specific vehicle are taken into account in the development of the subsequent product generation.

Characterizing Prediction Uncertainty in Agricultural Modeling via a Coupled Statistical–Physical Framework

Multiple factors, many of them environmental, coalesce to inform agricultural decisions. Farm planning is often done months in advance. These decisions have to be made with the information available at the time, including current trends, historical data, or predictions of what future weather patterns may be. The effort described in this work is geared towards a flexible mathematical and software framework for simulating the impact of meteorological variability on future crop yield. Our framework is data driven and can easily be applied to any location with suitable historical observations. This will enable site-specific studies that are needed for rigorous risk assessments and climate adaptation planning. The framework combines a physics-based model of crop yield with stochastic process models for meteorological inputs. Combined with techniques from uncertainty quantification, global sensitivity analysis, and machine learning, this hybrid statistical–physical framework allows studying the potential impacts of meteorological uncertainty on future agricultural yields and identify the environmental variables that contribute the most to prediction uncertainty. To highlight the utility of our general approach, we studied the predicted yields of multiple crops in multiple scenarios constructed from historical data. Using global sensitivity analysis, we then identified the key environmental factors contributing to uncertainty in these scenarios’ predictions.

Logit Truncated-Exponential Skew-Logistic Distribution with Properties and Applications

In recent years, bounded distributions have attracted extensive attention. At the same time, various areas involve bounded interval data, such as proportion and ratio. In this paper, we propose a new bounded model, named logistic Truncated exponential skew logistic distribution. Some basic statistical properties of the proposed distribution are studied, including moments, mean residual life function, Renyi entropy, mean deviation, order statistics, exponential family, and quantile function. The maximum likelihood method is used to estimate the unknown parameters of the proposed distribution. More importantly, the applications to three real data sets mainly from the field of engineering science prove that the logistic Truncated exponential skew logistic distribution fits better than other bounded distributions.

Development of a Multi-Objective Optimization Tool for Screening Designs of Taut Synthetic Mooring Systems to Minimize Mooring Component Cost and Footprint

As the offshore wind industry develops, more lease sites in the intermediate water depth (50–85 m) are being released to developers. In these water depths floating wind turbines with chain catenary systems and fixed-bottom turbines with jacketed structures become cost prohibitive. As such, industry and researchers have shifted focus to floating turbines with taut or semi-taut synthetic rope mooring systems. In addition to reducing the cost of the mooring systems, synthetic systems can also reduce the footprint compared to a chain catenary system which frees areas around the turbine for other maritime uses such as commercial fishing. Both the mooring systems component cost and footprint are pertinent design criteria that lend themselves naturally to a multi-objective optimization routine. In this paper a new approach for efficiently screening the design space for plausible mooring systems that balance component cost and footprint using a multi-objective genetic algorithm is presented. This method uses a tiered-constraint method to avoid performing computationally expensive time domain simulations of mooring system designs that are infeasible. Performance metrics for assessing the constraints of candidate designs are performed using open-source software such as Mooring Analysis Program (MAP++), OpenFAST and MoorDyn. A case study is presented providing a Pareto-optimal design front for a taut synthetic mooring system of a 6-MW floating offshore wind turbine.

Software Architecture Modeling of AUTOSAR-Based Multi-Core Mixed-Critical Electric Powertrain Controller

In this paper, we present a transition journey of automotive software architecture design from using legacy approaches and toolchains to employing new modeling capabilities in the recent releases of Matlab/Simulink (M/S). We present the seamless approach that we have employed for the software architecture modeling of a mixed-critical electric powertrain controller which runs on a multi-core hardware platform. With our approach, we can achieve bidirectional traceability along with a powerful authoring process, implement a detailed model-based software architecture design of AUTOSAR system including a detailed data dictionary, and carry out umpteen number of proof-of-concept studies, what-if scenario simulations and performance tuning of safety software. In this context, we discuss an industrial case study employing valuable lessons learned, our experience reports providing novel insights and best practices followed.

Simulating Nearshore Wave Processes Utilizing an Enhanced Boussinesq-Type Model

The simulation of wave propagation and penetration inside ports and coastal areas is of paramount importance to engineers and scientists desiring to obtain an accurate representation of the wave field. However, this is often a rather daunting task due to the complexity of the processes that need to be resolved, as well as the demanding levels of required computational resources. In the present paper, the enhancements made on an existing sophisticated Boussinesq-type wave model, concerning the accurate generation of irregular multidirectional waves, as well as an empirical methodology to calculate wave overtopping discharges, are presented. The model was extensively validated against 4 experimental test cases, covering a wide range of applications, namely wave propagation over a shoal, wave penetration in ports through a breakwater gap, wave breaking on a plane sloping beach, and wave overtopping behind breakwaters. Good agreement of the model results with all experimental measurements was achieved, rendering the wave model a valuable tool in real-life applications for engineers and scientists desiring to obtain accurate solutions of the wave field in wave basins and complex coastal areas, while keeping computational times at reasonable levels.