Imperfect Quality Research Articles

Supply Chain Modelling of the Automobile Multi-Stage Production Considering Circular Economy by Waste Management Using Recycling and Reworking Operations

The supply chain management plays a crucial role in delivering products from a supplier, through the manufacturer, distributors, and retailers to the targeted customers. The lifecycle of the products can be ended at any stage due to imperfect quality or waste, which are typically not managed well for a good price. This product’s life can be extended and increased with the use of the circular economy for the value addition processes which turn the waste into byproducts, which can be sold with maximum profit. The automobile industry is associated with various other small industries and is very significant for the economy at the local, national, and international levels. However, the industry also requires sustainable development in its supply chain management, gained by introducing the circular economy concept to manage and reduce the generated waste. The consumption of carbon fiber-reinforced composites (CFRCs) in the manufacturing of numerous automotive parts has acquired immense attention this decade, but the process also generates imperfect products (waste). The proposed model is based on a mathematical formulation to manage imperfect production by reworking and recycling, where the former is required to re-add value to the proportion of the rejected parts, and the latter is to recycle the remaining scrap into useful products by using a circular economy. The outsourcing operation is also added to provide an optimal level of inventory and lot sizing for minimizing the total cost of the supply chain management. Data from the automobile part industry are tested to provide the practical implications of the proposed SCM mathematical model. Sensitivity analysis is performed to understand the significance level of the individual parameters affecting the objective function, i.e., the total cost of the SCM. The results show a meaningful insight for the managers to obtain the benefits of the circular economy in multi-stage automobile part production for sustainable and resilient supply chain management.

Formation of the ecological infrastructure of the Lviv city

A modern city is a complex, dynamically changing natural and anthropogenic system that must provide its residents with a comfortable and safe living environment. Along with the traditional elements of the city's infrastructure: the transport system, sewerage, water supply, heat and electricity supply, and elements of the social infrastructure (schools, hospitals, etc.), the role of the city's ecological infrastructure is growing. The latter includes both artificial and natural objects that provide environmental services and reduce the negative anthropogenic impact of the urban environment on nature and living organisms. Such components are the city's green space and communal systems that neutralize the most pronounced manifestations of anthropogenic influence, such as ensuring waste disposal. The state of the urban ecosystem depends on a complex combination of structure, activity, risks, and intentions of its inhabitants, the state of the economic and financial and economic system, the stability of the natural base expressed in the landscape, and the city's visibility, the intensity of industrial activity and the level of environmental education. The prerequisite for developing the city as a dynamic system is ensuring its sustainability. According to the modern vision, a sustainable city is based on three pillars: economic, ecological, and social, which must develop harmoniously. Therefore, the ecological infrastructure of the city should be based on the appropriate level of technology and economic development. Lviv is a large city in Ukraine and the largest city in the territory of the western regions, a logistical and cultural center. At the same time, the ecological infrastructure of the city is mainly at the formation stage. A meaningful sign of its puberty is the lack of an effective waste management system and an imperfect air quality monitoring system. Municipal sewage treatment facilities and the city's transport system need improvement. The city's green infrastructure as a subsystem of the ecological infrastructure causes relatively few comments. However, it should be noted that its main structure has been updated for decades, and new microdistricts with active construction may need more green space. In order to improve the environment of Lviv, it is necessary to improve the elements of its ecological infrastructure with the involvement of best practices of well-known cities of the world.

A Lot-Sizing Model for a Multi-State System with Deteriorating Items, Variable Production Rate, and Imperfect Quality

Conventional production systems assume that during the manufacturing processes, machines operate without breakdown over an infinite planning horizon and manufacture only products of good quality. Imperfect production processes as a result of machine degradation are common in manufacturing. This paper deals with a problem that concerns the modelling and evaluation of the performance of a multi-state production system that is subject to degradation and its effect on lot sizing. Here, we consider that the cycle starts with a particular production rate until a point when the inventory reaches a certain level after which the failure mode is activated due to the deterioration of certain components, leading to a reduction in the production rate in order to ensure the continuity of supply until the maximum inventory level is reached. Production then stops to restore the machine and the cycle starts again. We have assumed that the rate at which inventory deteriorates is exponential and that demand is constant. A numerical example is used to illustrate the model application, followed by sensitivity analysis. This paper contributes to lot sizing in the area of machine reliability by considering a production system in a degraded state with a non-increasing production rate for deteriorating items with imperfect quality and partial backlogging.

Fair Incentive Mechanism With Imperfect Quality in Privacy-Preserving Crowdsensing

Mobile crowdsensing (MCS) enables a platform to recruit users to collectively perform sensing tasks from requesters. In order to maximize the completion qualities of tasks, an incentive mechanism should be well designed for the platform to incentivize high-quality users’ participation. The existing works largely adopt the Stackelberg game to model the strategic interactions in the incentive mechanism. However, there are practical issues that are less investigated in the context of the Stackelberg-based incentive mechanism. First, the platform has no knowledge about users’ sensing qualities beforehand due to their private information. Second, the platform needs users’ continuous participation in the long run, which results in fairness requirements. Third, it is also crucial to protect users’ privacy due to the potential privacy leakage concerns (e.g., sensing qualities) after completing tasks. In this article, we jointly address these issues and propose the three-stage Stackelberg-based incentive mechanism for the platform to recruit participants. In detail, we leverage combinatorial volatile multiarmed bandits (CVMABs) to elicit unknown users’ sensing qualities. We use the drift-plus-penalty (DPP) technique in Lyapunov optimization to handle the fairness requirements. We blur the quality feedback with tunable Laplacian noise such that the incentive mechanism protects locally differential privacy (LDP). Finally, we carry out experiments to evaluate our incentive mechanism. The numerical results show that our incentive mechanism achieves <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sublinear</i> regret performance to learn unknown quality with fairness and privacy guarantee.

An inventory model for imperfect quality items considering learning effects and partial trade credit policy

An inventory model for imperfect quality items considering learning effects and partial trade credit policy

Political stringency, infection rates, and higher education students' adherence to government measures in the Nordic countries and the UK during the first wave of the COVID-19 outbreak

Understanding predictors of adherence to governmental measures to prevent the spread of the COVID-19 is fundamental to guide health communication. This study examined whether political stringency and infection rates during the first wave of the pandemic were associated with higher education students' adherence to COVID-19 government measures in the Nordic countries (Denmark, Finland, Norway, Iceland, and Sweden) and the United Kingdom.Both individual- and country-level data were used in present study. An international cross-sectional subsample (n = 10,345) of higher-education students was conducted in May–June 2020 to collect individual-level information on socio-demographics, study information, living arrangements, health behaviors, stress, and COVID-19-related concerns, including adherence to government measures. Country-level data on political stringency from the Oxford COVID-19 Government Response Tracker and national infection rates were added to individual-level data. Multiple linear regression analyses stratified by country were conducted.Around 66% of students reported adhering to government measures, with the highest adherence in the UK (73%) followed by Iceland (72%), Denmark (69%), Norway (67%), Finland (64%) and Sweden (49%). Main predictors for higher adherence were older age, being female and being worried about getting infected with COVID-19 (individual-level), an increase in number of days since lockdown, political stringency, and information about COVID-19 mortality rates (country-level). However, incidence rate was an inconsistent predictor, which may be explained by imperfect data quality during the onset of the pandemic.We conclude that shorter lockdown periods and political stringency are associated with adherence to government measures among higher education students at the outset of the COVID-19 pandemic.

Impact of Credit Financing on the Ordering Policy for Imperfect Quality Items With Learning and Shortages

The paper develops an order quantity model with trade credit plus shortages under learning effects for deteriorating imperfect quality products. Generally, when the lot has imperfect items, the inspection of a lot is necessary to improve the quality of the lot. In this article, the seller provides a defective lot to his buyer under credit financing scheme, and after that buyer separates the whole lot under the screening process into two categories, one is defective and the other is non-defective items. The buyer sells out defective items at a low price as compared to non-defective items. It is assumed that customers' demand of good quality items is greater than the inspection rate for the whole lot to neglect the shortages situation. After keeping all points together, the buyer optimized his total profit concerning order quantity and shortage. A suitable numerical example and a sensitivity analysis have been provided for the validity of this model. The aim and utility of this paper have been presented in the conclusion section.

The Effects of Cognitive and Skill Learning on the Joint Vendor–Buyer Model with Imperfect Quality and Fuzzy Random Demand

This study investigates the optimization of an integrated production–inventory system that consists of an original equipment manufacturer (OEM) supplier and an OEM brand company. The cognitive and skill learning effect, imperfect quality, and fuzzy random demand are incorporated into the integrated two-echelon supply chain model to minimize the total cost. We contribute to dividing the learning effect into cognitive learning and skill learning, we build a new learning curve to resemble the real complexity more closely and avoid the problem that production time tends towards zero after production is stable. In total, five production–inventory models are constructed. Furthermore, a solution procedure is designed to solve the model to obtain the optimal order quantities, and the optimal shipment size. Additionally, the symbolic distance method is used to deal with the inverse fuzzification. Then numerical analysis shows that the increase of the cognitive learning coefficient and skill learning coefficient will reduce the total cost of the production–inventory system. With the increase of the cognitive learning coefficient, the gap between the total cost of cognitive learning and skill learning, and that of Wright learning, correspondingly decreases consistently. However, with the increase of the skill learning coefficient, there is a consistent corresponding increase. The total cost of cognitive learning and skill learning shows hyperbolic characteristics. The important insights of this study for managers are that employees’ knowledge plays an important role in reducing costs in the early learning stage and humanistic management measures should be taken to reduce employees’ turnover. Compared with the skill learning training for production technicians, we should pay more attention to the training of cognitive learning.

Two-echelon production inventory model with imperfect quality items with ordering cost reduction depending on controllable lead time

Two-echelon production inventory model with imperfect quality items with ordering cost reduction depending on controllable lead time

Impact of Learning on the Inventory Model of Deteriorating Imperfect Quality Items With Inflation and Credit Financing Under Fuzzy Environment

The present paper contributes to a set of models capturing economic order quantity with learning effect and fuzzy environment for decaying defective quality items under the inflation condition and credit financing. In real-life situations, the demand is uncertain and is controlled with fuzzy numbers. When each item goes through inspection process, the screening rate is assumed to be more than the demand rate otherwise shortages may occur and this consideration also helps one to meet their demand parallel to the screening process, out of the items which are of perfect quality. Further, the defective items are sold immediately after the screening process as a single lot at a discounted price. Further, the fraction of defective items follows the S-shaped learning curve. An expression for the total fuzzy profit of the retailer has been de-fuzzified with the help of a signed distance method and maximizes the cycle length. Conclusively, sensitive analysis has been presented on the various effective parameters of the inventory model.

EOQ Models for Imperfect Items under Time Varying Demand Rate

In the classical Economic Order Quantity (EOQ) model, the common unrealistic assumptions are that all the purchased items are of perfect quality and the demand is constant. However, in a real-world environment, a portion of the purchased items might be damaged due to mishandling or an accident during the shipment process, and the demand rate may increase or decrease over time. Many companies are torn between repairing or replacing the imperfect items with new ones. The right decision on that options is crucial in order to guarantee that there is no shortage of stocks while at the same time not jeopardising the items’ quality and maximising the company’s profit. This paper investigates two EOQ models for imperfect quality items by assuming the demand rate varies with time. Under Policy 1, imperfect items are sent for repairs at an additional cost to the makeup margin; under Policy 2, imperfect items are replaced with equivalent quality items from a local supplier at a higher price. Two mathematical models are developed, and numerical examples along with sensitivity analyses are provided to illustrate these models. Our results reveal that Policy 1 is preferable to Policy 2 most of the time. However, Policy 2 outperforms Policy 1 if there is no minimum threshold on the purchased stock quantity. This research allows a company to discover solutions to previously identified inventory problems and make the inventory-patching process more controlled.

Prototype of Monitoring and Controlling System on Gondola Cage Security Based Android Application

Gondola is one of the facilities used in high-rise buildings that are used to access the outside of the building. Gondola has a maximum load limit that is safe to transport it. Gondola is also often damaged in the cage caused by collisions on the side of the building. Until now, the gondola control still uses buttons which also often experience problems. This study aims to provide monitoring of the cage load and provide information about the distance of the cage to surrounding objects. Gondola control through an android application also includes this research in order to provide an alternative if the buttons on the gondola control experience problems. The short-term goal of this research is to carry out an innovation in the gondola field by conducting experiments through prototypes. The medium term goal is to apply the results of this research into a gondola. The results of this study control and monitoring can function properly even though the results produced are not yet accurate due to the imperfect quality of components, materials and assembly.

Research status and prospects of integration of habitat processing and processing of Chinese medicinal decoction pieces

The integration of habitat processing and processing of Chinese medicinal decoction pieces(hereinafter referred to as &quot;integration&quot;) has changed the traditional processing mode and can ensure the quality of Chinese medicinal decoction pieces from the source. This paper introduced the background of integration from the connotation and denotation of integration, relevant policies and regulations, and variety development. The present situation of integration was analyzed from the existing problems and current research progress, and the development suggestions were proposed. It is considered that although the integration is in line with the development trend of the industry with the advantages of improving the quality and standardizing the management of decoction pieces, there are still some problems, such as the lack of variety selection principles and production technical specifications, imperfect quality control stan-dards in the production process, and inadequate integration of standards and supervision. Therefore, it is suggested to determine the integrated variety selection principles and variety range as soon as possible, establish relevant technical specifications, improve quality control standards in the production process, and strengthen policy guidance and supervision to promote the healthy and orderly development of integration.

Alternative shipment policies compared for an international supply chain model with stochastic exchange rate, carbon emission and imperfect quality items

Researchers have recently developed a model for international supply chains for transferring lots in equal-sized batches, taking environmental concerns into account. This scheme incorporated stochastic behaviour of the exchange rate between the vendor's and buyer's currencies as part of the process for making optimal decisions on the supply chain. They demonstrated that their model leads to a decrease in the total cost. However, a lower total cost can be achieved by synchronizing the integrated production flow through generalization of the batch transfer method used to deliver the lot. Furthermore, there seems to be a better fit from a new data set for comparing two currencies. With these developments in mind, we suggest an alternative model, considering transferring batches of unequal sizes, transfer costs that may be fixed or variable, and with an eye on environmental issues. Numerical experiments are presented to demonstrate the proposed model and solution method. These numerical experiments show the value of this integrated approach in the combined determination of optimal policies for production and shipment. Suggestions for further research are given in the conclusion.

Multi-layered rational inattention and time-varying volatility

Standard rational inattention models suppose that agents process noisy signals about otherwise fully revealing data. I show that introducing imperfect data quality yields new insights in settings in which volatility is time-varying. I impose a two-layered signal structure in which agents learn imperfectly about noisy sources. Treating data as only partially revealing of the true fundamental amplifies impulse responses to a second moment shock and, if data quality is sufficiently poor, can change the qualitative direction of the response. I apply my findings to the price-setting problem of firms and find that higher data quality enhances the transmission of monetary policy and reduces macroeconomic volatility. I also show how the empirically documented procyclicality of data quality has non-trivial implications for the Phillips curve.

Optimum buffer stock during preventive maintenance in an imperfect production system

The paper investigates a production‐inventory model where regular preventive maintenance starts at the end of production for smooth functioning in the next cycle. During production run time, the manufacturing system may shift from “in‐control” state to “out‐of‐control” state after a certain time. The manufacturing system produces imperfect quality products during “in‐control” state and “out‐of‐control” state with different rates, and these imperfect quality products are reworked at costs in parallel system. The whole items are sold with free minimal repair warranty if any fault arises after sale. This study extends and corrects the Sana's production‐inventory model and derives optimal buffer inventory to minimize the expected costs per unit item. A numerical illustration with its sensitivity analysis of the key parameters is studied to justify the proposed model.

Considering the κ − µ fading channels adopted in multiple antennas downlink non-orthogonal multiple access

Massive connectivity and effective spectrum usage have become more important as the use of wireless communication devices and networks has grown dramatically. The approach of non-orthogonal multiple access (NOMA) is advocated as a viable solution for meeting consumers’ current needs. The signals are overlaid with various power levels for each user in a NOMA-assisted system, and then broadcast to the receiver. SIC (successive interference cancellation) is used by the receiver to discriminate and get the needed signal. Until far, most studies have concentrated on SIC with ideal features, with only a handful focusing on SIC with imperfect qualities (ipSIC). While the perfect SIC (pSIC) represents the ideal condition of no data loss and no external sounds, the ipSIC represents data transfer in a real-time context. In this research, we will assess the system performance metrics of the investigated NOMA system in the presence of ipSIC and compare them to the performance of the same user’s pSIC. We define channels as κ−µ fading distributions, which is more essential. For two destinations, we construct accurate outage probability formulas. Meanwhile, Monte-Carlo simulations are run to ensure that the mathematical expressions derived are genuine.

Ionic Liquid Treatment for Highest-Efficiency Ambient Printed Stable All-Inorganic CsPbI3 Perovskite Solar Cells.

All-inorganic cesium lead triiodide (CsPbI3 ) perovskite is well known for its unparalleled stability at high temperatures up to 500 °C and under oxidative chemical stresses. However, upscaling solar cells via ambient printing suffers from imperfect crystal quality and defects caused by uncontrollable crystallization. Here, the incorporation of a low concentration of novel ionic liquid is reported as being promising for managing defects in CsPbI3 films, interfacial energy alignment, and device stability of solar cells fabricated via ambient blade-coating. Both theoretical simulations and experimental measurements reveal that the ionic liquid successfully regulates the perovskite thin-film growth to decrease perovskite grain boundaries, strongly coordinates with the undercoordinated Pb2+ to passivate iodide vacancy defects, aligns the interface to decrease the energy barrier at the electron-transporting layer, and relaxes the lattice strain to promote phase stability. Consequently, ambient printed CsPbI3 solar cells with power conversion efficiency as high as 20.01% under 1 sun illumination (100mW cm-2 ) and 37.24% under indoor light illumination (1000 lux, 365 µW cm-2 ) are achieved; both are the highest for printed all-inorganic cells for corresponding applications. Furthermore, the bare cells show an impressive long-term ambient stability with only ≈5% PCE degradation after 1000 h aging under ambient conditions.

An EOQ Model with Carbon Emissions and Inflation for Deteriorating Imperfect Quality Items under Learning Effect

We developed an economic order quantity (EOQ) model with a learning effect and carbon emissions under inflationary conditions and inspection for retailers where the items deteriorate naturally. Finally, the total profit of the retailer is maximized with respect to cycle length. A sensitivity analysis was also performed to understand the robustness of the model. In the sensitivity analysis, we discuss the impact of learning rate, inflation rate, and deterioration rate on lot size and length of the cycle, as well as the retailer’s entire profit function. Observations and managerial insights are discussed. The effect of inventory parameters on the total profit is shown in the sensitivity section.

Learning EOQ Model with Trade-Credit Financing Policy for Imperfect Quality Items under Cloudy Fuzzy Environment

An imprecise demand rate creates problems in profit optimization in business scenarios. The aim is to nullify the imprecise nature of the demand rate with the help of the cloudy fuzzy method. Traditionally, all items in an ordered lot are presumed to be of good quality. However, the delivered lot may contain some defective items, which may occur during production or maintenance. Inspection of an ordered lot is indispensable in most organizations and can be treated as a type of learning. The learning demonstration, a statistical development expressing declining cost, is necessary to achieve any cyclical process. Further, defective items are sold immediately after the screening process as a single lot at a discounted price, and the fraction of defective items follows an S-shaped learning curve. The trade-credit policy is adequate for suppliers and retailers to maximize their profit during business. In this paper, an inventory model is developed with learning and trade-credit policy under the cloudy fuzzy environment where the demand rate is treated as a cloudy fuzzy number. Finally, the retailer’s total profit is maximized with respect to order quantity. Sensitivity analysis is presented to estimate the robustness of the model.