Sustainable Operations and Computers最新文献

The cloud computing paradigm provides services to users in an on-demand fashion using high-speed Internet. This Internet-based computing paradigm provides resources on a rent basis without any fault. Virtual machine resource allocation is one of the challenging concerns in a cloud computing environment. The existing static, dynamic, and Meta-Heuristic approaches provide the solution to the virtual machine allocation problem. These techniques stuck with the local optimal solution. The slow convergence rate leads to the optimal solution locally and fails to provide the optimal solution Globally. This manuscript proposes a hybrid Spotted Hyena optimizer and artificial neural network, named the SHO-ANN technique, to provide a solution to the virtual machine assignment problem. The presented hybrid technique is evaluated and analyzed using performance metrics “Energy Consumption (Kwh) (8.54%), Host Utilization (24.8%), Average Execution Time(ms) (26.33%), SLA Violations (1.33%), and Number of Migrations (Counts) (19.73%)”. The spotted hyena optimizer is used to provide the vast data set to the ANN model for better accuracy. The hybrid approach provides an optimal solution globally with high convergence. The experimental results exhibit that the SHO-ANN outperforms the IqMc, SHO, and Genetic approaches using real workload scenarios and fabricated scenarios.

Wind energy is an alternative form of energy easily obtainable in the landscape. However, the main challenge is to extract electrical power from varying wind speeds. Wind energy can be a significant production resource for power electronics technologies, converters, and electrical generators. Due to their dependence on wind speed, the output power from wind turbines experiences severe fluctuations with the change in wind speed, and ripples increase the output power from the wind turbine. Therefore, the engineers’ critical research prediction will smooth these extraction fluctuations. Several speed prediction methods have been used to reduce the changes in the output power of wind turbines. One of these wind speed prediction methods is a fast energy storage system that can be charged and discharged in seconds. Applying wind speed prediction to overcome the slowness of the wind source will be the primary approach considered in this article. Also, a wind turbine with a nominal power of 50 kW and an ultra-capacitor storage system are determined, and these sources are made in MATLAB/SIMULINK softwareIn this study, the control signal for adjusting the turbine pitch angle is derived from both actual and predicted data. The signal from actual data undergoes a multiplication by 0.8, while the signal from predicted data is multiplied by 0.2. This approach serves two purposes: firstly, it helps prevent overshooting of turbine power at the initial stages, ensuring a smoother transition. Secondly, it aids in maintaining a consistent power output of 50 kW during subsequent moments. By combining actual and predicted data in this weighted manner, the control system achieves a balanced response, effectively managing turbine power dynamics. Finally, the results show that utilizing wind speed prediction to improve output wind turbine in Micro-grids (MGs) will reduce fluctuations in the wind source's output power and the ultra-capacitor storage.

This paper presents the energy planning problem (EPP) as an optimization problem to find the optimal schedules to minimize energy consumption costs and demand and enhance users’ comfort levels. The grey wolf optimizer (GWO), One of the most powerful optimization methods, is adjusted and adapted to address EPP optimally and achieve its objectives efficiently. The GWO is adapted due to its high performance in addressing NP-complex hard problems like the EPP, where it contains efficient and dynamic parameters that enhance its exploration and exploitation capabilities, particularly for large search spaces. In addition, new energy and real-world resources based on solar renewable energy systems (RESs) are combined with the proposed GWO to enhance its performance and ensure the optimisation of EPP objectives. Furthermore, EPP is presented as a multi-objective planning problem to optimize all objectives simultaneously. To efficiently investigate the proposed method performance, the results obtained by the GWO with the RESs are compared in three stages: comparison with original methods without RESs, comparison with methods using RESs, and comparison with state-of-the-art. The obtained results proved the robust performance of the proposed method in handling EPP and optimizing its objectives.

With the increasing development of logistics industry, an important question arises: which logistics format should an agricultural product seller select? Or when an agricultural product seller does select a certain logistics format? To answer this question, we explore three different green agricultural product supply chain models/scenarios: a self-managed logistics format(N); a third-party enterprise logistics format(S); and a platform logistics format(E). Using the game-theoretic model, we investigate the impact of the cost coefficient on the agricultural product seller's logistics preference under the three logistics scenarios. Our theoretical analyses show that when the cost coefficient is small, the agricultural product seller prefers to cooperate with the 3PL enterprise, but as the coefficient increases, the agricultural product seller prefers to choose her self-managed logistics. Therefore, to optimize the logistics strategy of the agricultural product seller under B2C mode, it is necessary to build a reasonable logistics management mechanism and a perfect logistics distribution system to reduce the logistics service cost coefficient.

The ongoing Russia-Ukraine conflict has made the global energy crisis a severe issue, particularly for emerging economies with a sharp rise in load shedding in communities, disruptions in industrial operation, and an increased cost of living. Shifting our focus from fossil fuel-based energy to sustainable and promising renewable energy sources, like geothermal energy (GE), is crucial to addressing the ongoing energy crisis. Therefore, this study aims to evaluate the significant factors influencing the adoption of GE to support the national grid of an emerging economy like Bangladesh. An integrated framework consisting of the Delphi method, fuzzy total interpretive structural modeling (TISM), and fuzzy Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis was utilized in this study to evaluate the hierarchical interrelationships among the significant factors. The findings indicate that the top two influencing factors are the “scope for new investments and employment” and the “growing need for inexpensive and renewable energy sources”. The study's findings can offer significant insights to decision-makers and policymakers, which can aid in the development of long-term strategic plans to facilitate the successful adoption and integration of GE and promote sustainability and low-carbon economy in the energy sector.

A substantial extent of harmful rubbish produced from manufacturing processes and health segments has posed a significant warning to the health of humans by affecting environmental concerns and the pollution of soil, air, and water resources. In this research, a multi-objective mixed-integer nonlinear programming (MINLP) model is presented for a sustainable hazardous waste location-routing problem. The position of the facilities and decisions on the routes for transferring hazardous waste as well as the waste remainder are considered to design a proper waste collection system. The proposed model tries to minimize the whole costs of the waste management system, the total hazards from the facilities and transportation, together with the CO₂ emissions, simultaneously equipped with a real case study to show the applicability of the developed model. In order to show the sustainability importance, the outputs of the original model are compared with the model, not including sustainability. The outcomes illustrate that, under the lack of sustainability, total costs, transportation, and site risk along with the CO₂ emissions increase, demonstrating the importance of sustainability. Besides, the extracted managerial insights support managers in making better decisions in the hazardous waste management system.