Sustainable Operations and Computers最新文献

Even though corporate sustainability management has become a major topic of research and of the business world, its digitalization is still in an infancy stage. One of the reasons for this situation is the fact that domain-specific abstractions, which are a major underpinning of digitalization in general, have so far received very little attention. This is particularly a major obstacle for the digitalization of the work area of corporate environmental compliance management (CECM), which is intended to ensure that the firm complies with environmental legislation. Using general system theory, the CECM area is analyzed, and a set of domain-specific abstractions is proposed. Further contributions of the article are focused on two crucial CECM decision tasks: the relevance assessment of environmental regulations and the determination of compliance enforcement measures. Based on the abstractions, decision schemes and tailored domain-specific templates of decision trees for these tasks are suggested and demonstrated through a running example. It is expected that through these novel building blocks, this research contributes to an advancement of the digitalization of CECM processes and of sustainability management in general. For example, the proposed building blocks may serve as a foundation for the development of compliance management information systems that offer smart assistance for CECM tasks. Digital assistance may mitigate deficiencies in CECM expertise and CECM experience, which are often imposed in practice by tight budget constraints.

In the 21st Century, societal welfare has captured global imagination and companies are encouraged to participate in this process. The paper aims to study how professional acculturation and appropriate capital is leading to the advance agenda of sustainability through the actions and initiatives of corporates considering the ESG initiatives and gauge the effectiveness of resulting responsible actions by all social actors through an assessment based on national ranking system for ESG disclosures and sustainability in Indian context. Reliance industries Limited (RIL) has been chosen to study system approach because it has been aligning its initiatives with regulations on ESG and such investments made by the companies attract socially conscious investors and create value within their portfolios. Systems approach has been adopted to highlight environmental and socially responsible initiatives undertaken by Reliance Industries, a large Indian multinational. Considering the ESG structure as a processing framework, we have tried to propose how an initiative (input) can be turned as an outcome (output) to benefit the society at large, through a case study. We propose system input and output methodology through efficient strategies to conquer good health, education, development, safety, energy, environment and climate protection. RIL has undertaken various ESG initiatives such as energy and water conservation, upliftment of community and initializing educational programs, adhering to various legal compliance and health and safety measures. On comparing RIL performance with top ten companies highlights that RIL has to go a long way in terms of environmental performance. Adopting system approach using ESG structures will serve RIL as a potential benchmark to the Indian corporate sector and emerging world towards spreading awareness and achievement of sustainable development goals (SDGs).

Aviation stock sensitivity to crude oil prices has raised a suspicion of nexus between aviation stocks and crude oil prices. In this paper, the sustainable nexus between oil Prices and aviation stocks in India are analysed. In particular, it is tested whether there is any sustainable long-term co-movement between the stocks of three aviation companies listed on the Indian stock market and crude oil. The oil prices and aviation stocks are decomposed into different time horizons using the methodology of wavelets. The nexus is studied at shorter, medium, and longer time horizons. Broadly, results show that there is some nexus in the short-run, however, sustainable long-run nexus is missing. The results are found to be robust and approximately similar when data from three Indian aviation stocks are used. It is inferred that sustainable long-run movements in aviation stocks should be attributed to the reasons not related to crude oil price changes.

The spread of coronavirus disease around the world has had an immense impact on most economic sectors. Yet amid the turmoil and chaos from the worldwide pandemic, one industry is thriving noticeably. The coronavirus disease is a once in a lifetime business opportunity for pharmaceutical companies. This study presents an artificial intelligence method composed of optimization and machine learning. Data envelopment analysis (DEA) is used to measure productivities and efficiencies of pharmaceutical companies during the COVID-19 pandemic using the additive model in window analysis, the BCC (Banker-Charnes-Cooper) model, and the CCR (Charnes-Cooper-Rhodes) model. The three models are assessed using DataStream financial information with research and development (R&D) investment. The results indicated the additive model's superiority in window analysis, followed by the BCC and CCR models. In the end, some of well-known data mining algorithms, based on the suggested data, have been evaluated in various tools to find the most efficient tool and algorithm.

There are different flow prediction models such as Autoregressive models, Autoregressive moving average models, first-order autoregressive-moving average models, etc. The main purposes of this dissertation were to fit a model to represent a river flow data of 10 rivers in the Northern part of Cyprus. The modeling was built on the estimate of parameters, modeling the residuals, generating synthetic river flows, and checking for the goodness of fit to the monitored data. Finally, the findings were used to evaluate the synthetic series for future flow predictions. The study on available data demonstrated that the (AR) model was an efficient and reliable technique in which, the model identification technique was supplemented by the Akaike's information criterion (AIC) in order to decide the type and the order of the model. The Box-Pierce Porte Manteau test is used to check the dependency of residuals. it is recommended to generate stochastic modeling for the downstream drainage areas of the 10 rivers in which the surface geology totally changes and surface flow turns to be a subsurface flow due to the gravel and pebbles distributed all around the riverbeds.

Mycobacterium tuberculosis (TB) is an infectious bacterial disease presenting similar symptoms to pneumonia. Therefore, differentiating between TB and pneumonia can be challenging for physicians and lead to delays in diagnosis and treatment. Early diagnosis of TB in particular, is critical in preventing community spread. The purpose of this study is to propose a method of differential diagnosis of TB from pneumonia using low-cost features. A two-step decision support system called Pneumonia-Tuberculosis Diagnosis Support System (PTBDSS) is proposed for differential diagnosis of TB from pneumonia based on stacked ensemble classifiers. The first step of the proposed model aims to identify an early diagnosis based on low-cost features, including demographic characteristics and patient symptoms. The second step of the proposed model confirms a diagnosis based on meta features extracted in the first step, laboratory tests, and chest radiography reports. The meta feature is a vector of length five, and each number in that vector comes from the vote of one classifier. This retrospective study considers 199 medical records of patients admitted to the isolation ward of a hospital in Arak, Iran, with suspected TB or pneumonia. Experimental results show that the proposed method outperforms the compared machine learning methods for early differential diagnosis of pulmonary tuberculosis from pneumonia with AUC of $90.26\pm 2.30$ and accuracy of $91.37\pm 2.08$ with $95\%$ CI and final decision making with AUC of $92.81\pm 2.72$ and accuracy of $93.89\pm 2.81$ with $95\%$ CI.

Development and deployment of FSPV systems are still in the nascent stages hence long-term performance, control and feasibility study of FSPV systems are not well addressed. Precise and robust estimation of FSPV panel parameters will play an important role in determining the actual performance, carbon savings and long-term feasibility studies of FSPV systems. Here, hybrid stochastic firefly algorithm (HSFA) is used for parameter estimation and optimization. The hybrid algorithm is used to find out the parameters of single diode model, double diode model and FSPV module. An experiment is also performed using FSPV modules under varying irradiance conditions. The accuracy of model is evaluated by comparing the simulated results with the experimental results and computing the relative error and root mean square error (RMSE). The parameters extracted using the proposed method has a very low RMSE value of 9.83002E-04. Assessment of the experimental and estimated results show that the relative error for measured electricity on a sunny day is 0.57% while for an overcast day it is 0.89%. For partial shading condition, the relative error and RMSE was 0.79% and 6.5%, respectively. The results which are in well agreement with the experimental values demonstrate the superior performance of the model in determining the FSPV parameters. Proper estimation of the FSPV parameters will help researchers, scientists, engineers and all actors associated with solar PV systems in making sound judgements towards the deployment of FSPV systems and help the society in developing a sustainable ecosystem towards implementation of industry 4.0 by adapting to low-carbon power generation methods.

Recently, Circular Economy (CE) is implemented by the manufacturing industries since it helps the management to reduce waste and increase productivity. Industries are adopting CE because it helps them gain economic, environmental, and social benefits. This study is primarily aimed at the identification and ranking of the drivers and barriers for the implementation of CE in the automobile industry. For this, an integrated approach of interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) is utilized. The results of this study indicate that the share/ benefit and reduction of cost are the most critical drivers while unaware/limited knowledge and cost and financial constraint are the major barriers for the implementation of CE in the automobile industry. This integrated approach will help the decision makers and policy makers to take immediate and effective actions, and focus on the productivity of the company.

This paper proposes a novel fuzzy mathematical model for a distribution network design problem in a multi-product, multi-period, multi-echelon, multi-plant, multi-retailer, multi-mode of transportation green supply chain system. The three purposes of the model are to minimise total network cost, maximise net profit per capita for each human resource, and diminish CO2 emission throughout the network. P-hub median location with multiple allocations is used for locating the distribution centres. One scenario is designed for fuzzy customer demands with a trapezoidal membership function. Furthermore, the model determines the design of the network (selecting the optimum numbers, locations of plants, and distribution centres to open), finding the best strategy for material transportation through the network with the availability of different transportation modes, the capacities level of the facilities (plants or distribution centres (DCs)), and the number of outsourced products. Finally, all uncertain customer demands for all product types can be satisfied based on the methods mentioned above. This multi-objective mixed-integer non-linear mathematical model is solved by NSGA-II, MOPSO and a hybrid meta-heuristic algorithm. The results show that NSGA-II is the exclusive algorithm that obtains the best result according to the evaluation criteria.