Sustainable Operations and Computers最新文献

Fused Deposition Modeling (FDM) 3D printing is an advanced Additive Manufacturing (AM) method for developing thermoplastic-based parts. Researchers studied FDM-based 3D printing of PLA with whole biomass and biomass resources such as lignin, hemicellulose, and cellulose. These composites are environmentally friendly, sustainable and have wide applications in healthcare. There is scope for developing the 3D printing of biocomposite with medical surgical cotton fabric, where PLA is unique for such manufacturing. The development and characterisation of FDM 3D printed medical surgical cotton fabric- PLA biocomposite is the primary objective of this work. Experimental methods used for the development of biocomposites involve the use of three types of cotton fabric of pore sizes 0.6 mm x 0.6 mm, 0.8 mm x 0.8 mm, and 1.0 mm x 1.0 mm with three different 3D printing pore sizes 0.5 mm x 0.5 mm, 1.0 mm x 1.0 and 1.5 mm x 1.5 mm. The development of biocomposites is found feasible. Breaking strength, percentage extension, and water absorption capacity increased in 3D printing pore sizes and pore wall width for each fabric. The maximum 235.40 N and minimum 123.20 N breaking strength, maximum 2.288 % and minimum 1.506 % extension, and maximum 7.63 % and minimum 3.57 % absorption capacity have been observed for the developed biocomposite. The behaviours of these biocomposites are adequate for healthcare applications and may be used as a bandage in wound healing. The present work is limited to the feasibility study of the unique biocomposite. Analysis of other properties and testing of biocomposite on animals and humans may be carried out in future work.

Management 4.0 assists businesses in evolving, surviving and performing in the competitive and dynamic world. This fourth revolution uses advanced technologies like Artificial Intelligence (AI), Virtual Reality (VR), Internet of Things (IoT), Robotics, Holography, Additive Manufacturing etc., for the proper management systems. These technologies facilitate working personnel and make it more appealing to complete their duties efficiently and accurately. The main aim of this paper is to understand the concept of Management 4.0, its technologies and applications for proper management systems. As Management 4.0 enhances process control, the chance of human error is reduced, leading to increased efficiency. It enables rapid and intelligent decision-making, reduces costs, accelerates growth, and raises profitability. Management 4.0 technologies and advanced data analytics are helpful to make smart supply chain management well suited to fulfil industry 4.0. Thus, to overcome various obstacles and effectively deploy Management 4.0 technologies in manufacturing industries, top management must establish a clear asset performance management plan with the help of process engineers familiar with industrial system failure occurrences and what operators need to improve. Management 4.0 involves advanced technologies, system connectivity, data collection & analysis at the organisation level. Management 4.0 is expected to be a critical component in the long-term survival of any business, either manufacturing or service-providing organisations. This paper explores the development of Management 4.0 and its dimensions and transformations through Management 4.0 perspectives. Finally, the significant role of Management 4.0 for appropriate private management system in manufacturing industries are identified. Organisations require a system that seamlessly meets the company's expectations, consumers, investors, and other stakeholders to remain competitive, and Management 4.0 will enable this. Many businesses strive to integrate technologies and upskill their personnel to adapt to the new job duties and attract more workers with the necessary abilities.

In today's power systems, performing demand response (DR) programs is considered a solution for sustainable operation, carbon reduction, and facilitating renewable energy integration. Currently in Iran, several DR programs are available to industrial customers. Moreover, the design of a market-based DR has recently been considered by the power system operator. In DR programs, customer baseline load (CBL) is used to determine the level of customers’ demand reduction and is key to the billing process; thus, the CBL calculation methodology can directly affect the efficiency of these programs. This paper investigates the current CBL calculation method in Iran's power system and, as its main contribution, proposes some effective methods to reduce the probable incentives for CBL manipulation. In order to show the effectiveness of the methods, a real study case is considered based on the historical load data of the industries in the Khorasan region.

The frequent occurrence of natural disasters caused by human activities and environmental pollution has forced people to pay attention to environmental protection. Of course, emergency rescue after natural disasters is also a great component of research. Emergency rescue often generates a large number of emergency evacuation and rescue traffic needs. In the face of scarce road resources, how to allocate limited road resources for emergency traffic needs reasonably to improve the efficiency of emergency rescue has important practical significance. This paper studies the emergency traffic distribution method and the traffic organization method of the emergency traffic network. First, on the basis of the existing traffic distribution method based on Logit model, combined with the characteristics of emergency traffic flow, an emergency multi-path traffic distributed method based on the second modified Logit model is proposed. As well as the emergency lane reversal implementation strategy is proposed according to the actual situation. Finally, an example is proposed to comprehensively verify the emergency traffic distribution method and emergency lane reversal strategy proposed in this paper. The experimental results show that the proposed method can cope with the complex scenarios of multiple emergency traffic flows and is helpful to improve the overall efficiency of emergency traffic flow.

Blockchain is a new technology that is seen to be transforming businesses across various industries. This technology provides transparency under all levels of business and it supports traceability across various supply chains which older technologies could not reach. This research explores various factors affecting the adoption of blockchain technology in seafood towards sustainable supplychain for exports. Based on feedback from seven companies, technique for order performance by similarity to ideal solution is deployed to identify important factors for sustainable supply chain solutions. Regulatory uncertainity emerges as the major factor, which calls for reforms in regulatory compliance. Implementation costs rank second and need to be reduced for better adoption of the technology.

The purpose of this research is to introduce a Bi-Objective (BO) model for dealing with Aggregate Production Planning (APP) for a multi-product and multi-period Supply Chain Network (SCN) that incorporates multiple suppliers, factories, and demand points. One of the goals of the model is to minimize the total cost of this network during the disaster period. The other goal is to account for probabilistic lead times to maximize the minimum level of producers' reliability during the COVID-19 pandemic. They are done to ameliorate the system's performance and improve the reliability of production plans. Finally, considering that the mentioned problem is NP-hard, a Multi-Objective Imperialist Competitive Algorithm (MOICA) based on Pareto is used to solve the proposed model, and a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is also utilized to measure the performance of the mentioned algorithm. The generated experimental problems' results demonstrate the proposed algorithm's power in finding Pareto solutions. According to innovation, this is the first paper on these topics considering the conditions of the COVID-19 disaster.

The fractal emergency organization is a new kind of organization form with plenty of advantages compared with the traditional emergency organization. A system dynamic (SD) simulation model was built to test its effectiveness in this paper. In the developed model, the emergency organization form plays the premier role in the whole response process by influencing the pattern of local control, information transmission, and decision-makers hierarchy. The comparison analysis results show that the fractal emergency organization could finish the emergency response process in a more efficient way, making the supply chain of emergency resources more agile and the resources' organization more efficient. And when the situation deteriorates, and the demand surges, the advantages of fractal emergency organizations are more significant.

Logistics distribution faces difficulties such as increasing delivery demands, demanding delivery time and lower delivery costs. However, the routing optimization for logistic distribution is not a simple vehicle routing problem (VRP). This study focuses on crowdsourcing multi-depot vehicle routing problem with time windows (MDVRPTW) problem, permitting one crowdsourcing driver to deliver multiple customers, which has not been covered yet. To quickly distribute many delivery tasks within a certain time, this paper proposes a two-stage MDVRPTW framework. The first stage is the division stage in which a k-means algorithm based on the elbow method is used to transform the multi-depot problem into several single-depot problems. Then a crowdsourcing VRPTW model is developed, which is a mixed-integer linear programming model. The second stage is the optimization stage, for which an adaptive ant colony (ADACO) algorithm is proposed to determine the most reasonable distribution routes for each distribution center. Solomons classic VRP data is then used to validate the models’ effectiveness, with the results confirming that the crowdsourcing MDVRPTW strategy could save 10.02% costs than traditional MDVRPTW strategy. By comparison, the ADACO algorithm could significantly improve the ACO’s weakness of falling into a local optimum, and would be more suitable for solving large-scale vehicle routing problems than the GUROBI solver.

Two transitions, green and digital, are changing the operations and strategies of industrial systems. At the same time, businesses are challenged to be globally competitive. Europe has a very ambitious agenda as it aims to be the first climate-neutral continent in 2050. The european emissions trading scheme (EU ETS) has proven to have facilitated the reduction of significant amounts of greenhouse gas emissions, but the risk of carbon leakage is present. This work seeks to explore these issues and their relationships. Through the use of a long short-term memory (LSTM) neural network, a model is built to determine the price of european union allowance (EUA) as a function of different financial energy futures. The results show that the model is very robust and the EUA tends to vary between 78 and 91 €/tCO₂. In addition, a multi-criteria decision analysis (MCDA) is applied to identify the best policy alternatives to enable businesses subject to the EU ETS to be competitive in global markets. The analysis is carried out with the help of academic and industrial experts and it emerges that the criteria considered most relevant are two: (i) public expenditure and its expected benefits and (ii) the industrial ecosystem. The policy implications identify that bonuses should be provided to businesses for innovative solutions that protect both the energy and raw material components. The framework of the 3E (Energy Efficiency, Renewable Energy, and Circular Economy) are critical to businesses' long-term strategies, flanked by digital development.