Sustainable Operations and Computers最新文献

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.

COVID-19 pandemic, which has announced to the world from Wuhan in China, has naturally formed economic shocks in air transport. As a result of the COVID-19 crisis, governments closed international borders and almost all airlines have drastically reduced their available seat capacity. The aim of this study is to examine the early and late responses such as financial decisions, managing and recovering flights, human resources management and hygiene measures taken by Turkish air carriers in a crisis environment during pandemics and economic shocks. Turkish Civil Aviation Industry (TCAI) is analyzed pre and during COVID-19 in terms of market overview. Finally, we also present current and future directions, and provide examples of the reactions from Turkish and global carriers. The results show that TCAI is heavily impacted by the COVID-19 Pandemic and the market is re-shaping with fewer carriers in the recovery phase. Airline staff faced significant salary decreases in TCAI due to revenue decrease of the airlines. Cargo-only flights are increased crucially in the TCAI, although passenger figures are dropped.

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.

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.

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.

Purpose

The rising interest in the integration of digital advanced manufacturing and production systems in the Industry 4.0 context is one of the main factors in the introduction of Additive Manufacturing (AM). The novel technology might change the way firms operate, and the way they interact with consumers, opening new horizons for an improved profit margin and more sustainable business models. The research presented a comprehensive review on the potent role of AM in adhering to customers’ complicated and unique needs using various technologies and techniques, as it discussed the role of AM in introducing new business models that increase the business competitiveness and profitability through an optimisation of production processes. In addition, AM is grasping with innovative solutions varying from waste reduction to shorter supply chains to longer products lifecycle, incentivising firms to adopt it for the economies realised on materials, energy, and costs. Notwithstanding, AM implementation is still in its infancy and faces technical challenges of capability, IT integration, and outcomes.

Design/Methodology/approach

This research is based on a quantitative approach that was administered online by means of a highly structured online survey, which aim was to collect primary data that fills the gaps of the research hypotheses due to the lacking nature of research papers exploring them. Therefore, the literature review was a paramount phase in acquiring empirical knowledge about the problem background and concept boundaries that shaped the topic's core objectives and research questions from the lacking nature of explored areas.

Findings

This research investigated the role of AM in industry 4.0 by exploring its impact and intersection with AM firms’ business models while exposing the limitations and challenges to its adoption within industrial contexts. The study highlighted that the AM positive impacts on companies’ business models on the value chain and turnover. This study also revealed the eco-design prospect of AM that will be helpful for different firms to rethink their business models shaping them to be more cost-efficient.

Originality

This research gave insight on AM technology through a quantitative survey that mainly aimed to classify knowledge and to investigate the role of AM as a lever in improving firms’ value chains through an exploration of possible intersections with business models and impacts of implementation, possible sustainability scenarios and challenges it may face within Industry 4.0 context.

3D printing is increasingly becoming an important technology in the manufacturing sector and has the potential to revolutionize manufacturing. 3D printing allows customization, which produces sophisticated structures while lowering waste and at the same time allowing more flexibility in the design. This paper includes a brief overview of the main types of additive manufacturing (AM) technologies. It reviews the work carried out in various types of 3D printing technologies particularly focusing on mechanical characterization. Based on the literature studied, comparisons have been drawn on the various merits and challenges offered by various 3D printed materials. Dedicated sections on various materials aspects and application areas have been included particularly from a medical science point of view. This paper ends with a dedicated section on applications of Additive Manufacturing (AM) in orthopedic, dental, prosthetics, food and textile sectors. It tries to establish relationships between AM, industry 4.0 and sustainability. This paper shall act as a stimulant to trigger further advancements in the above fields.

In most organizations, especially order-oriented ones meeting deadlines are crucial. Job shop environments could be mentioned as an example where decision makers are try to schedule all jobs in such a way that tardy jobs (TJ) are minimized. Indeed, manufacturers are received customers’ orders and required to deliver them no later than the determined due dates. Otherwise, penalties should be paid to customers and it can lead to a huge amount of financial loss. Also, regarding global warming and climate changes manufacturers should redesign their processes from an eco-friendly perspective. Motivated by these issues, in this paper, a green bi-objective model has been formulated to solve the problem of scheduling parallel machines considering TJ and job splitting property (JSP). In the proposed model, the first objective function minimizes the total number of TJ while the second objective function is minimization of total energy consumption. An augmented ε-constraint method has been deployed for solving small-scale problems. However, to solve large-scale problems, an efficient Simulated Annealing (SA) algorithm has been developed while a Harmony Search (HS) algorithm has also been applied to examine the quality of the proposed SA algorithm. Random generated problems have been used to compare the results of three deployed algorithms. Results approved that the proposed SA algorithm outperforms others significantly. In particular, SA solved the problems sooner than others while its solutions were closer to solutions of the augmented ε-constraint method.

The chief objective of this study is to employ a predictive software called adaptive neuro-fuzzy inference system (ANFIS) approach which assess stress amongst doctors employing industry 4.0 technology during their surgeries. This study further investigates factors contributing the operation accuracy, sensitivity and specificity amongst doctors. Also, the effective performance of doctors can be optimized through earlier prediction for percentage of incorporating Industry 4.0 technologies. Survey was conducted amongst doctors using industry 4.0 technologies who provided unbiased answers to several queries in the questionnaire. The ANFIS model was employed to predict success rate of surgeries through models build with the aid of several input parameters. The outcomes such as accuracy, sensitivity and specificity were studied while employing Industry 4.0 technology which were considered significant factors influencing the perceived various kinds of surgeries in different domains. Moreover, the results of the ANFIS modelling approach showed that with increase in percentage of industry 4.0 machines in medical equipment, the operations sensitivity and accuracy increased, hence the most critical predictors. While specificity did not have any major impact on the surgeries. Henceforth, doctors can take preventive actions and simultaneously plan their work load with the aid of industry 4.0, providing better health benefits to patients making the healthcare industry much more efficient and stress-free.