In this research, we study the optimal batching and scheduling in a single machine system where production costs have non-linear dependence on the batch composition. The main objective is to minimize the production costs while taking into account the due production dates. The problem originates in using metal Additive Manufacturing (aka 3D printing) for spare parts production where multiple items of different types can be batched together in the printing chamber, and the printing costs heavily depend on the batch composition. This paper presents a mixed-integer linear scheduling problem and demonstrates how it can be efficiently solved using the set covering reformulation.
Cumulative Sum (CUSUM) quality control chart is commonly used in many industrial applications. In this research, an improved scheme of the CUSUM chart is introduced to monitor attribute data. In this scheme, the difference between actual and in-control numerals of nonconforming items is raised to an exponent w to enhance the detection effectiveness. The exponent w is optimized, along with other charting parameters to minimize the Average Number of Defectives (AND) which is used as an objective function. The proposed scheme is found to outperform the conventional CUSUM chart for detecting a wide range of shifts in fraction non-conforming.
Supply chains are a vital component in many industries that provide vital services on a daily basis, and healthcare supply chains are not an exception. However, these supply chains are very complicated in the way they are established which introduces critical issues such as imprecise or incorrect information, lack of appropriate historical record, and lack of transparency. These issues all together hinder the process of tracking and tracing throughout the pharmaceutical supply chain. Therefore, to guarantee that only authentic drugs are being transported within the supply chain, a comprehensive, end-to-end solution is needed. There has already been a number of attempts to resolve these issues, however, they are conceptualized and built in a centralized system resulting in transparency, data privacy, and authenticity issues. This paper leverages the smart contract feature in the Ethereum blockchain to provide traceability, authenticity, and data provenance. In addition, decentralized off-chain storage is utilized when needed.
This study proposes a framework to predict ma- chine failures using sensor data and optimize the predictive/corrective maintenance schedule. Machine learning (ML) models are trained to predict the failure probabilities. The ML model's output is fed to an optimization model to propose a maintenance policy. Hence, demonstrating how prediction models can help increase system reliability at lower costs.
An incremental machine learning approach is proposed to quantify the influence of occupants' gender, age, and nationality on predictive models of occupant indoor comfort, perceived productivity, and perceived happiness. A three-step methodology is proposed, including: (1) data collection through sensors and a questionnaire; (2) development and comparison of machine learning models; and (3) incremental introduction of demographic variables while observing its influence in the the model's performance. Results confirm that the inclusion of demographical variables in the models can improve prediction accuracy (F1 scores) by as much as 10%. However, reductions in the models' performances were observed in some instances, highlighting the need to strike a balance between increasing model complexity and the subsequent rise in computational costs.
Producing precise and complex functional parts for industrial application with different types of material has been an enormous challenge in the additive manufacturing industry. Several attempts have been made to optimize the quality of the fabricated parts by understanding the relationship between the input and output process parameters. The main objective of this paper is to construct a reliable data set utilizing literature data and to obtain a linear regression model that explains the interaction between the laser power, scan speed, hatch distance, and layer thickness with the relative density of fabricated steel parts. The R2 value of the linear model revealed that 77.36% of the data fit the regression model and can be utilized in future optimization processes.
This research proposes an optimization model for the economic statistical design of the np chart for detecting shifts in fraction nonconforming. In this model, the charting parameters of the np chart are optimized to minimize the Expected Total Cost (ETC) and in the meantime, ensure that the false alarm rate and the inspection rate do not exceed the allowable levels. It is found that the proposed optimal np chart considerably outperforms the traditional np chart.
This paper proposes a new combined scheme (termed as Mnp-EWMA scheme) for monitoring multi-attribute characteristics. The proposed Mnp-EWMA scheme combines both the multi-attribute np (Mnp) and multi-attribute Exponentially Weighted Moving Average (MEWMA) charts for effective monitoring of fraction nonconforming. In this study, the Average Number of Defectives (AND) is used as an objective function in an optimization model to identify the best design variables of Mnp-EWMA scheme. The performance of the proposed Mnp-EWMA scheme is compared with that of the Mnp and MEWMA charts. The results show that the Mnp-EWMA chart outperforms the Mnp and MEWMA charts under different scenarios.
Fresnel lenses have many solar energy-based applications and focus light from x-rays to extreme ultraviolet (UV) radiation. Typically, solar concentrators are based on large-scale Fresnel lenses. The Fresnel lens novel design for a targeted application is very prominent among researchers. In this work, using a novel micro-Fresnel lens in healthcare applications for pressure sensing is proposed. Such lenses might be used separately or combined with commercial contact lenses for optical sensing purposes. These lenses have a significant role in ophthalmologic disease detection, such as glaucoma. For this purpose, a flat Fresnel lens is designed and simulated to observe focusing ability and investigate focal length variation with geometric lens parameters. A flat Fresnel lens is transformed into a curved one, in analogy to a contact lens uses for vision correction, therapeutics, and cosmetics. This work also opens the possibilities of additively manufactured patient-specific contact lenses for sensing applications.
Diffusive reflection spectroscopy is employed to study the influence of probe pressure on human skin and nails. A non-invasive study is conducted which covers the important body parts for in-vivo measurements. Reflection spectra were measured for fingertips, forearm, and nails. A set of probe pressure ranging from 0 to 258 kPa for the finger and 0 to 385 kPa for the nail is applied. It is found that all the measured spectra revealed the increase in the reflection intensity under increasing pressure in the specific wavelength region (525-575 nm - oxyhemoglobin region). Since every tissue has its own composition and morphology so the shape, size, intensity, and position of the measured peaks are affected. Time-based reflection spectroscopy was also performed on the forearm under blood occlusion for 5 mins with an interval of 30 sec.
