Multi-objective Optimization of Shared Logistics and Express Delivery Platforms: an Algorithm Framework for Balancing Cost, Time, and Resource Utilization

The efficient utilization of resources in repetitive construction projects is crucial to optimizing schedules and reducing costs. This study investigates the application of Discrete Event Simulation (DES) to enhance resource utilization in scheduling repetitive construction activities, specifically the construction of Type-36 houses in Palu City, Indonesia. Traditional project-based planning methods, such as the Critical Path Method (CPM), are compared with production-based planning approaches. The study shows that although CPM can effectively determine project completion times, it falls short in optimizing resource allocation, particularly in scenarios involving repetitive tasks. By employing the DES tool SIMUL8, the study models the construction process and simulates various resource allocation scenarios to identify the optimal resources required. The results indicate that DES provides a more dynamic and accurate analysis, allowing real-time adjustments and improved resource management. The study concludes that integrating DES into construction project management can significantly enhance efficiency, reduce project duration, and optimize resource utilization.

Energy-efficient cloud systems: Virtual machine consolidation with -robustness optimization.

A combined strategy of an improved simulated annealing (SA) algorithm and genetic algorithm is presented, with the goal of reducing the computational expenses. The improvements made on the SA algorithm include two parts, i.e., the adaptive regulating for the step vector, and the dynamic testing for the equilibrium of the Metropolis process. The proposed strategy has both the advantage of SA algorithm, the ability to avoid being trapped in a local optimum, and that of genetic algorithm, the ability to use the information about the searched states for the next iteration. A practical application on geometry optimization of pole shoes in large salient pole synchronous generators is effectively implemented using the strategy. The numerical results show that the number of iterations used by executing the combined strategy are only about 75% of those by executing basic SA algorithm.

Anesthetic techniques and early extubation: Does it matter?

The key parameters for Simulated Annealing Algorithm (SAA) and Genetic Algorithm (GA) were determined by test experiments. With optimal parameters obtained for both algorithms, the multilayer antireflection coatings in the 7.7-12.3μm band and a neutral beam splitter in the visible region were designed. Results showed that, for infrared multilayer antireflection coatings, the evaluation function value of SAA was 18.7% lower than that of GA, and the average transmittance of SAA was 0.4% higher than that of GA. Compared to GA, SAA achieved a higher average transmittance and a more flat transmittance curve. And for the neutral beam splitter, the running time of GA was 0.407s less than that of SAA, whereas the convergence effect of SAA was superior to that of GA. Therefore, the design with SAA has certain advantages for both the infrared multilayer antireflection coating and the beam splitter.

Yield gap and resource utilization efficiency of three major food crops in the world – A review

Cooperative dynamic free-space optical (FSO) networks exploit the fact that atmospheric losses are distance dependent to enhance the performance of FSO networks. This enhancement is achieved by sharing the resources of shorter links among different nodes in the network. In this paper, two joint transceiver placement and resource allocation schemes are proposed to optimally place FSO redundant transceivers based on optimal resource allocation in cooperative dynamic FSO networks. Specifically, one scheme increases reliability and capacity, while the other increases reliability and fairness of cooperative dynamic FSO networks during severe weather conditions. The schemes are formulated as multi-objective and bi-level integer linear programming problems and solved using an exhaustive search to obtain optimal solutions. The numerical results reveal that higher reliabilities can be achieved with enhanced capacities and fairness using the first and second schemes, respectively. Furthermore, these improvements are achieved by using many fewer numbers of FSO redundant transceivers than those of random placement.

An effective logarithm-degree resource allocation strategy for three-layer networks

In current networks, large flows often co-exist with small flows and consume a significant proportion of the network bandwidth. This imposes a challenge on network resource management. Various hybrid switching architectures have been proposed in recent years aiming to address this challenge and improve efficiency and resource utilization. However, there is still a need for means for design and dimensioning of resources among multiple switching planes. In this work, we first argue that the resource allocation in hybrid switching systems is fact the interaction between three components: resource allocation, traffic partitioning and performance (or cost) constraints. We then introduce a framework called Blocking-LOss Curve (BLOC) to address the resource allocation problem in hybrid switching systems. BLOC can capture the behavior of hybrid switching systems under different resource allocation strategies. This framework also allows us to identify the feasible region for resource allocation, within which performance metrics like the packet loss and request blocking requirements can be satisfied. An optimal resource allocation strategy may be found based on the feasible region. We also discuss the application of BLOC in hybrid switching Data Center networks.

This article presents a novel approach to Kubernetes autoscaling for 5G network functions using reinforcement learning techniques. Traditional threshold-based autoscaling mechanisms in Kubernetes environments have shown significant limitations when handling the complex dynamics of 5G workloads, particularly in scenarios requiring network slicing and guaranteed resource allocation. The solution introduces a deep reinforcement learning-based system to address these challenges, incorporating domain-specific optimizations for 5G environments. The proposed architecture leverages deep Q-learning algorithms to create an intelligent scaling system that learns and adapts to emerging traffic patterns while maintaining strict performance requirements. Experimental results demonstrate substantial improvements in resource utilization, service reliability, and scaling efficiency compared to conventional approaches while effectively managing multiple concurrent network slices with varying quality of service requirements.

Microservices architecture has emerged as a transformative paradigm in modern software development, enabling organizations to build resilient and scalable distributed systems. This comprehensive exploration delves into the fundamental principles, implementation strategies, and real-world applications of microservices architecture. The architectural framework facilitates independent service deployment, enhanced fault isolation, and streamlined maintenance processes while promoting technology stack flexibility. Through the implementation of sophisticated monitoring patterns, testing strategies, and data consistency mechanisms, organizations can effectively address common challenges in distributed systems. The integration of enterprise architecture principles with microservices has demonstrated significant improvements in resource utilization, system reliability, and operational efficiency. The adoption of API gateway patterns, security frameworks, and containerization technologies further enhances system performance and scalability. Real-world applications, particularly in e-commerce platforms, showcase the practical benefits of microservices architecture in managing complex business operations while maintaining high availability and performance standards.

In recent years, the integration of Micro-Electro-Mechanical Systems (MEMS) into sensor networks has shown promising potential for advancing circuit theory applications. This research addresses the increasing demand for efficient and high-performance implementations by proposing a novel methodology utilizing Field-Programmable Gate Arrays (FPGA). As circuit theory applications continue to evolve, there is a pressing need for innovative solutions that can handle the intricate demands of modern electronic systems. MEMS sensor networks offer advantages in terms of miniaturization, low power consumption, and enhanced sensing capabilities. However, existing implementations face challenges in achieving real-time processing and scalability, prompting the exploration of FPGA-based solutions. Current implementations of MEMS sensor networks for circuit theory applications often encounter bottlenecks related to processing speed and resource utilization. The research gap lies in the lack of efficient methodologies that can harness the parallel processing capabilities of FPGAs to address these challenges, ensuring real-time data processing and optimal resource allocation. The proposed methodology involves the design and implementation of an FPGA-based architecture specifically tailored for MEMS sensor networks. Leveraging the parallel processing capabilities of FPGAs, the architecture aims to enhance real-time data processing, reduce latency, and provide scalability for diverse circuit theory applications. The integration of FPGA with MEMS sensor networks is expected to result in a versatile platform capable of handling complex tasks efficiently. The results demonstrate significant improvements in processing speed and resource utilization compared to traditional implementations. The FPGA-based MEMS sensor network architecture exhibits enhanced performance in real-time circuit theory applications.

In this paper Hybrid Genetic- Simulated Annealing Algorithm (GSA) is studied and applied to the suspension system of an automobile with two different case studies. Mathematical modelling of an automobile is done for passive suspension system. GA, SA and GSA were simulated using programming in MATLAB. First case study is based on the data of researcher in the field where GA was applied for optimization of automobile suspension system. Here SA, as well as Hybrid GSA are applied and compared with the results of GA. Convergences of the three methods are also plotted. In second case study an automobile model MARUTI 800 is considered. All the necessary parameters are calculated through measurements. The results shows required seat acceleration for current available springs of the automobile in field. For both the case studies the Hybrid GSA shows best results among the three. This can be performed before actual manufacturing of springs and automobile to check whether desired seat acceleration and ride comfort will achieve or not. If it is satisfied with the value of seat acceleration, decision would be made regarding manufacturing of springs with those values of stiffness and damping constants.

Limited edge server resources and uneven distribution of traffic density in vehicular networks result in problems such as unbalanced network load and high task processing latency. To address these issues, we proposed an efficient caching and offloading resource allocation (ECORA) strategy in vehicular social networks. First, to improve the utilization of vehicular idle resources, a collaborative computation and storage resource allocation mechanism was designed using mobile social similarity. Next, with the optimization objective of minimizing the average task processing delay, we studied the combined resource allocation optimization problem and decoupled it into two sub-problems. For the service caching subproblem, we designed a stable matching algorithm by mobile social connections to dynamically update the cache resource allocation scheme for improving the task unloading efficiency. For the task offloading subproblem, a discrete cuckoo search algorithm based on differential evolution was designed to adaptively select the best task offloading scheme, which minimized the average task processing delay. Simulation results revealed that the ECORA strategy outperformed the resource allocation strategy based on particle swarm optimization and genetic algorithm, and reduced the average task processing delay by at least 7.59%. Meanwhile, the ECORA strategy can achieve superior network load balancing.

Last-train timetable coordination is extremely complex because a number of transfer directions involve in the subway network. In this paper, transfer redundant time (TRT) and transfer binary variables (TBV) that affect transfer results are considered in the Markowitz mean-variance model. By adjusting running time and dwelling time, the model creates a high-quality timetable that greatly improves the efficiency of transferring passengers. Furthermore, a genetic simulated annealing (GSA) algorithm is developed to solve this problem in the Beijing subway network, which consists of 14 lines, 17 transfer stations, and 42 key directions. The present model increases the number of successful connections by 40.0% and reduces the amount of just-missed connections by 83.3%, respectively. In addition, the mean-variance model significantly improves the subway network accessibility compared with the current practice of the last-train timetable.