Resource Capacity Constraints Research Articles

Evaluation of Decentralisation Policy: Impact on Regional Development

This study evaluates the impact of decentralisation policies on regional development, focusing on the redistribution of administrative powers from central to local governments. Decentralisation is often implemented with the aim of improving governance, promoting local autonomy, and enhancing public service delivery. This research explores how these policies have influenced economic growth, infrastructure development, and social welfare at the regional level. Using a mixed-methods approach, including quantitative analysis of regional development indicators and qualitative case studies, the findings reveal a complex relationship between decentralisation and regional outcomes. While some regions have experienced significant improvements in governance efficiency and socio-economic conditions, others continue to face challenges such as uneven resource distribution and capacity constraints. The study concludes with policy recommendations aimed at optimizing decentralisation frameworks to ensure balanced and sustainable regional development.

Adaptive Scheduling Method for Passenger Service Resources in a Terminal

To alleviate the tense situation of limited passenger service resources in the terminal and to achieve the matching of resource scheduling with the flight support process, the process–resource interdependent network is constructed according to its mapping relationship and the time-varying characteristics of the empirical network and network evolution conditions are analyzed. Then, node capacity, node load, and the cascading failure process are investigated, the impact of average service rate and service quality standard on queue length is considered, the node capacity model is constructed under the condition of resource capacity constraints, and the load-redistribution resource adaptive scheduling method based on cascading failure is proposed. Finally, the method’s effectiveness is verified by empirical analysis, the service efficiency is assessed using the total average service time and variance, and the network robustness is assessed using the proportion of maximum connected subgraph. The results indicate that the resource adaptive scheduling method is effective in improving service efficiency, and the average value of its measurement is smaller than that of the resource average allocation method by 0.069; in terms of the robustness improvement of the interdependent network, the phenomenon of re-failure after the load redistribution is significantly reduced.

The Role of Green Finance in Sustainable Business Strategies: Opportunities and Challenges for Business Organizations

Amidst increasing awareness of environmental and social issues, businesses worldwide are increasingly recognizing the importance of adopting sustainable business strategies. Green finance has become a popular approach among companies to integrate financial aspects with sustainable environmental goals. This research aims to investigate in-depth the opportunities and challenges associated with green finance practices for business organizations. The study is a literature review with a qualitative approach, utilizing descriptive analysis. Data for this research were obtained through a search of articles on Google Scholar spanning from 2014 to 2024. The study findings indicate that in an era where environmental awareness is on the rise, green finance practices are becoming increasingly crucial for companies looking to adopt sustainable business strategies. Green finance enables companies to integrate environmental values into investment decisions and fund allocation, creating incentives for more environmentally efficient business practices, and helping to mitigate environmental risks that may disrupt long-term operations. However, the implementation of green finance faces several challenges, including lack of understanding and awareness, complexity in measuring environmental impact, initial costs and economies of scale, limitations in data and measurement methods, as well as resource and internal capacity constraints. To address these challenges, cross-sector collaboration among private, government, and financial institutions is key.

Solving a real-life multi-skill resource-constrained multi-project scheduling problem

This paper addresses a multi-skill resource-constrained multi-project scheduling problem (MSRCMPSP) with different types of resources and complex industrial constraints, which originates from SNCF heavy maintenance factories. Two objective functions, that have been rarely addressed in the literature, are independently considered: (i) Minimization of the sum of the weighted tardiness of the projects and (ii) Minimization of the sum of the weighted duration of the projects. A time-indexed mixed-integer linear programming model is presented with both resource assignment and capacity constraints. To solve large instances with several thousand activities, a new memetic algorithm combining a novel hybrid simulated genetic algorithm with a simulated annealing is implemented. The memetic algorithm is compared with popular solution approaches. Computational experiments conducted on real instances and benchmark instances validate the efficiency of the proposed algorithm.

Loads prediction and consolidation of virtual machines in cloud

SummaryVirtual machine (VM) consolidation is to assign a set of VMs requested by operators to the physical machines (PMs) in the data centers so that certain cost, profit or performance objective is optimized, subject to the PMs resource capacity constraints. It is an important mean to decrease the total power consumption by reducing the number of active physical machines (PM) in a cloud. In this context, most of the existing solutions rely on highly frequent live migration to reduce the number of active physical machines. However, live migration is a high resource consumption and time‐consuming operation, thus, frequent use of live migration not only increases energy consumption but also affects the stability of the physical machine, which in turn affects the services on the virtual machine. Reducing the number of active physical machines while reducing the number of live migrations is a major challenge in the face of massive fluctuating virtual machine loads. In order to solve this problem, in this paper, we present a VM consolidation algorithm for predictable loads (VCPL) to reduce the live migration operations. First, we present a cyclic usage prediction (CUP) method to predict the load in a whole cycle (a day) of a VM. Then, we separate the VMs with stable and cyclic load out from others and consolidate them to PMs by using VCPL to make sure each PM has a stable load. Thus, energy can be reduced by avoiding most of live migration operations, and the stability of the data center can be observably improved. We evaluate our solution through simulations on real‐world workloads, the results show that, 66% of long‐term VMs have stable and cyclic loads and are predictable, by using VCPL, the live migration operations occurring on the PMs which accommodate those VMs can be reduced significantly than other solutions.

Advance admission scheduling via resource satisficing

We study the problem of advance scheduling of ward admission requests in a public hospital, which affects the usage of critical resources such as operating theaters and hospital beds. Given the stochastic arrivals of patients and uncertain usage of resources, it is often infeasible for the planner to devise a risk‐free schedule to meet these requests without violating resource capacity constraints and creating adverse effects that include healthcare overtime, long patient waiting times, and bed shortages. The difficulty of quantifying these costs and the need to safeguard against resource overutilization lead us to propose a resource satisficing framework that renders the violation of resource constraints less likely and also diminishes its impact whenever it occurs. The risk of resource overutilization is captured by our resource satisficing index (RSI), which is calibrated to reflect a risk‐adjusted utilization rate for a better interpretation to the healthcare planner. Unlike the expected utilization rate, RSI is risk‐sensitive and serves to mitigate the risks of overutilization better whenever overutilization can be avoided in expectation. Our satisficing approach aims to balance out the overutilization risks by minimizing the maximal RSI among all resources and periods. Under our proposed partial adaptive scheduling policy, the resource satisficing model can be formulated and solved via a converging sequence of mixed‐integer linear optimization problems. A computational study establishes that our approach reduces resource overutilization risks to a greater extent than the benchmark methods.

When Edge Caching Meets a Budget: Near Optimal Service Delivery in Multi-Tiered Edge Clouds

More and more artificial intelligence (AI) applications, such as virtual reality (VR) and video analytics, is rapidly progressing towards enterprise and end users with the promise of bringing immersive experience. Such AI applications need unprecedented requirements for ultra-low latency. Edge computing is emerged to guarantee the timeliness for the delay-sensitive applications. The delay experienced by AI users can be significantly reduced, by caching various services that are initially deployed at data centers to cloudlets in edge networks. However, it is impractical to cache all services to cloudlets, due to often limited operational budget of service providers and resource capacity constraints of edge cloudlets. In this paper, we investigate a fundamental problem of service caching from remote data centers to cloudlets in a multi-tiered edge cloud network. We first develop two approximation algorithms with approximation ratios to solve the problem for a single type of service demanded by users. We then devise an efficient heuristic to solve the problem that users request different types of services. We finally conduct extensive experiments on a real testbed to evaluate the performance of the proposed algorithms, and experimental results demonstrate that our algorithms can outperform some existing algorithms significantly with 25% lower average delay.

Regulatory Compliance of Small-Scale Pharmaceutical manufacturing facilities in Addis Ababa, Ethiopia

Background: there is growing concern that medicines supplied to most of the developing countries are of substandard quality. Along with the establishment of strong regulatory systems for ensuring compliance and regulatory law enforcement, manufacturing facilities need to work to implement dependable systems at each stage of the production process to ensure the safety, efficacy, and quality of their products. Objective: the aim of this study was to assess the level of compliance with regulatory requirements by small-scale pharmaceutical manufacturing facilities in Addis Ababa, Ethiopia.
 Material & Method: A cross-sectional observational study was conducted using a structured questionnaire and checklist to assess the level of compliance of small-scale local pharmaceutical manufacturing facilities to regulatory requirements and also to identify the challenges faced by the manufacturing facilities.
 Result: This study indicated that the overall implementation status of regulatory requirements in the local small-scale pharmaceutical manufacturing companies is far below the minimum standard set by WHO and the national regulatory authority. Basic on specific regulatory requirement elements compliance: requirements related to QA were 26.9, requirements related to personnel qualification were 38.1, requirements related to Quality control were 25.9, and requirements related to sanitation and hygiene were 15.3. Of the total regulatory requirements, only 26.1% were found to be implemented. Major challenges faced by the local small-scale pharmaceutical manufacturing industry for the implementation of regulatory requirements were: human resource capacity constraints, limited investment, limited support from Governments and other stakeholders, and poor infrastructure.
 Conclusion: The study demonstrated that level of regulatory requirements implementation in the local small-scale pharmaceutical manufacturers is far below the minimum standard set by the national regulatory authority. Important gaps were reported particularly in materials management, production operations, quality control, and sanitation and hygiene.

Research on Resource Allocation Method of Integrated Avionics System considering Fault Propagation Risk

The integrated modular avionics (IMA) system has coupled cross-linked support characteristics between physical resource entities and logical functions, and the existing resource allocation methods, which mainly consider system performance and resource utilization, do not consider the associated impact of fault propagation among resource entities. Aiming at the fault propagation risk in the resource allocation process of the IMA system, a hierarchical model of the IMA system is established. The fault propagation behavior caused by coupling association during the physical realization of the function layer logical architecture is analyzed, and the impact of different resource allocation methods on the fault propagation behavior is determined. Secondly, resource capacity constraints are established according to the resource requirement for hosted function, and the fault propagation risk model of the IMA system is constructed by considering the fault propagation impact factor, the relative importance of nodes, and the function safety criticality. The resource allocation method is evaluated according to the fault propagation risk model, and a heuristic algorithm is applied to optimize the resource allocation method of the IMA system. The simulation results show that the average propagation probability of the optimized resource allocation scheme decreases by 17.4%, and the overall fault propagation risk of the resource network decreases by 50.3%, indicating that the proposed resource allocation method can effectively improve the safety of the IMA system.

Hierarchical Four-Dimensional Trajectories Planning Method for Manned and Unmanned Aircraft Integrated Airspace

A waypoint-based trajectory planning framework is proposed. The motivation is to allocate capacity-constrained airspace resources for pretactical safe separation in the manned and unmanned aircraft integrated airspace. First, the spatial-temporal related aircraft with high conflict risks are clustered to reduce the scale of the problem induced by increasing traffic volumes. A two-stage optimization is further implemented. In the first stage, trajectories within each group are sequentially planned in order determined by a priority ranking mechanism, where the relative conflict-critical aircraft takes more deconflicting responsibilities. A distributed decision-making architecture is developed to account for aircraft heterogeneity in the sequential deconflicting process. In the second stage, the high-quality solutions of different groups are coupled together to obtain the overall trajectory distribution under system-wide resource capacity constraints. Empirical studies using operational data in China show that the proposed method reduces 95% of conflicts with low cost and few aircraft disturbance, which demonstrate superiority in deconflicting effectiveness and efficiency. In addition, the distributed decision-making process ensures scalability and flexibility by allowing aircraft to determine the optimal trajectories based on their internal tradeoffs. Several major conclusions and future work are presented based on the current investigation.

Service migration in multi-access edge computing: A joint state adaptation and reinforcement learning mechanism

With the development of the internet of things (IoT), the concept of an edge network has been gradually expanding to other fields including internet of vehicles, mobile communication networks and smart grids. Because the resources of terminals are limited, the long-distance movements of users will increase the running costs of the services that are offloaded to edge servers, and even the services on terminals will stop running. Another problem is that resource shortages or hardware failures of these edge networks can affect the service migration policy. In this paper, a novel service migration method based on state adaptation and deep reinforcement learning is proposed to efficiently overcome network failures. Before migration, we define four edge network states to discuss the migration policy and adopt the two-dimensional movement around the edge servers to adapt to the applications scenarios of our work. Then, we use the satisfiability modulo theory (SMT) method to solve the candidate space of migration policies based on cost constraints, delay constraints and available resource capacity constraints to shorten the interruption time. Finally, the service migration problem can be transformed into the optimal destination server and low-cost migration path problem based on the Markov decision process by the deep Q-learning (DQN) algorithm. Moreover, we theoretically prove the rate of convergence in the learning rate function of our algorithm to improve the convergence rate. Our experimental results demonstrate that our proposed service migration mechanism can effectively shorten the delays from service interruptions, and better avoid the impact of edge network failure on the migration results and, thus, improve the users’ satisfaction.

Integrated Online Learning and Adaptive Control in Queueing Systems with Uncertain Payoffs

Many online service platforms have dedicated algorithms to match their available resources to incoming clients to maximize client satisfaction. One of the key challenges is to balance the generation of higher payoffs from existing clients and exploration of new clients’ unknown characteristics while at the same time satisfy the resource capacity constraints. In “Integrated Online Learning and Adaptive Control in Queueing Systems with Uncertain Payoffs,” Hsu, Xu, Lin, and Bell show that traditional approaches such as maximizing instantaneous payoffs with current knowledge or using queue-length based controls guided by “shadow prices,” would lead to suboptimal long-term payoffs. Instead, they propose a novel utility-guided assignment algorithm that seamlessly integrates online learning and adaptive control to provide high system payoffs with performance guarantees. The theoretical performance bound also lends system design insights into the impact of uncertain client dynamics, payoff learning, and backlogged clients. They further develop a decentralized version of the algorithm, which is applicable to large systems and performs well even when the service rates are random.

Reverse logistics system applied to the reuse of iron ore tailings.

Iron ore tailings (IOT) from tailings dams cause significant environmental, economic, and social impacts, which has motivated the search for reuse alternatives. In this scenario, the present work uses reverse logistics to size the resources required to reuse the IOT for road infrastructure. A reverse logistics system was modelled to transport the IOT from the tailings dam to a processing plant and then to the construction site of a road. Resource capacity constraints and operational limitations were considered, and the economic feasibility of the system was analysed. Two scenarios for tailings transportation on the dam/plant route were simulated aiming at a cost less than R$ 25 (US$ 6.13) per tonne: (1) trucks; and (2) conveyor belts. The economic feasibility of both scenarios was proven, with scenario 1 presenting the most promising results - a transport distance of 290 km within the established price limit. This methodology can thus be applied to encourage the large-scale reuse of IOT.

PCN71 MODELLING THE RESOURCE UTILIZATION DIFFERENCE OF USING SUBCUTANEOUS AND INTRAVENOUS ADMINISTRATION OF RITUXIMAB WITHIN CHEMOTHERAPY FOR THE TREATMENT OF B-CELL LYMPHOMA

A healthcare system faces both budgetary and resource constraints on treatment provision. Standard modelling approaches focus heavily on the treatment costs and impact on the budget; however, little attention is given to the resource utilisation within a healthcare system. Capacity pressures on resources would require slow structural healthcare system changes, such as increasing bed space, practitioner time and specialist equipment availability. This provides challenges additional to creating budgetary headroom. This study aimed to analyse the resource use of different interventions and evaluate the implications treatments have on resource capacity constraints. A resource utilization model (RUM) was developed to model the likelihood of different treatments reaching capacity constraint for different resources. The modelling approach combined expected time on treatment with treatment scheduling to estimate resource use. The framework evaluates subcutaneous versus intravenous administration of rituximab within chemotherapy for treating B-cell Lymphoma to highlight the reduced resource burden achieved for each patient treated subcutaneously. The model reveals that subcutaneous administration is expected to reduce administration time by 79.6%, reduce time in hospital by up to 14.8%, and halve the time a patient occupies a chair or bed during infusion. Within a one-million-person insurance plan, 3.2 patients are expected to suffer from B-cell lymphoma. Subcutaneous administration would save 58.7 hours of infusion time, 85.3 hours of infusion chair or bed time, and 34.3 hours of time spent in hospital for these patients. These results have shown reductions in resource use across all elements assessed, with the greatest reductions seen for administration time. Therefore, the change to subcutaneous treatment will greatly decrease the need for specialised equipment for these patients. It may be preferable for healthcare systems to opt for more expensive and less resource burdensome treatments as opposed to structural changes required to accommodate more resource intensive treatments.

Online Matching with Bayesian Rewards

We study in this paper an online matching problem where a central platform needs to match a number of limited resources to different groups of users that arrive sequentially over time. The reward of each matching option depends both on the type of resource and the time period the user arrives. The matching rewards are assumed to be unknown, but drawn from probability distributions that are known a priori. The platform then needs to learn the true rewards online based on real-time observations of the matching results. The goal of the central platform is to maximize the total reward from all the matchings without violating the resource capacity constraints. We formulate this matching problem with Bayesian rewards as a Markovian multiarmed bandit problem with budget constraints, where each arm corresponds to a pair of a resource and a time period. We devise our algorithm by first finding policies for each single arm separately via a relaxed linear program, and then assembling these policies together through judicious selection criteria and well-designed pulling orders. We prove that the expected reward of our algorithm is at least $(\sqrt{2}-1)/2$ the expected reward of the optimal algorithm. In particular, in the single-resource case, we prove the ratio is at least $\sqrt{2}-1$. We also design numerical experiments to verify our algorithm's performance guarantee, and compare the algorithm with alternative heuristics to illustrate the algorithm's good and robust performance in various settings.

A Hybrid Learning Framework for Service Function Chaining Across Geo-Distributed Data Centers

Service function chaining (SFC) focuses mainly on deploying various network functions in geographically distributed data centers and providing interconnect routing among them. Traditional (convex optimization-based) SFC algorithms exhibit some disadvantages on the scalability and accuracy. Recently, researches have shown the effectiveness of deep reinforcement learning (DRL) in the field of SFC. However, current DRL-based algorithms possess an extremely large action space, which leads to slow convergence and poor scalability. Some researchers relieve this issue by reformulating the SFC problem, which usually results in low utilization and high cost. To address this issue, we develop a hybrid DRL-based framework which decouples the VNF deployment and flow routing into different modules. In the proposed framework, a DRL agent is only responsible for learning the policy of VNF deployment. We customize the structure of the agent base on deep deterministic policy gradient (DDPG) and adopt several techniques to improve the learning efficiency, such as adaptive parameter noise, wolpertinger policy, and prioritized experience replay. The flow routing is conducted in a game-based module (GBM). We design a decentralized routing algorithm for the GBM to address the scalability. The end-to-end latency of flows is minimized while the resource capacity and location constraints are satisfied. During the learning process of the proposed framework, the DRL agent improves its deployment policy with the reward from the GBM (the value of reward depends on flow routing). Thus, the VNF deployment and flow routing are still jointly optimized. Compared to existing DRL-learning algorithms, the proposed hybrid DRL framework can achieve a lower cost since 1) the action space is significantly reduced due to flow routing decoupling; 2) the flow routing procedure is more efficient (the GBM adopts model-based information, e.g., the gradient). Through trace-driven simulations, we show the efficiency of our algorithm compared to existing DRL-based algorithms.

Advance Admission Scheduling via Resource Satisficing

We study the problem of advance scheduling of ward admission requests in a public hospital, which affects the usage of critical resources such as operating theaters and hospital beds. Given the stochastic arrivals of patients and their uncertain usage of resources, it is often infeasible for the planner to devise a risk-free schedule to meet these requests without violating resource capacity constraints and creating negative effects that include healthcare overtime, longer patient waiting times, and even bed shortages. The difficulty of quantifying these costs and the need to safeguard against their overutilization lead us to propose a resource satisficing framework that renders the violation of resource constraints less likely and also diminishes their impact whenever they occur. The risk of resource overutilization is captured by our resource satisficing index (RSI), which is inspired by Aumann and Serrano (2008) riskiness index and is calibrated to coincide with the expected utilization rate when the random resource usage corresponds to some referenced probability distribution commonly associated with the type of resource. RSI, unlike the expected utilization rate, is risk sensitive and could better mitigate the risks of overutilization. Our satisficing approach aims to balance out the overutilization risks by minimizing the largest RSIs among all resources and time periods, which, under our proposed partial adaptive scheduling policy, can be formulated and solved via a converging sequence of mixed-integer optimization problems. A computational study establishes that our approach reduces resource overutilization risks to a greater extent than does the benchmark method using the first fit (FF) heuristics.

Efficient NFV-Enabled Multicasting in SDNs

Multicasting is a fundamental functionality of many network applications, including online conferencing, event monitoring, video streaming, and so on. To ensure reliable, secure, and scalable multicasting, a service chain that consists of network functions (e.g., firewalls, intrusion detection systems, and transcoders) usually is associated with each multicast request. We refer to such a multicast request with service chain requirement as an network function virtualization (NFV)-enabled multicast request. In this paper, we study NFV-enabled multicasting in a software-defined network (SDN) with an aim to maximize network throughput while minimizing the implementation cost of admitted NFV-enabled multicast requests, subject to network resource capacity, where the implementation cost of a request consists of its computing resource consumption cost in servers and its network bandwidth consumption cost when routing and processing its data packets in the network. To this end, we first formulate two NFV-enabled multicasting problems with and without resource capacity constraints and one online NFV-enabled multicasting problem. We then devise two approximation algorithms with an approximation ratio of $2M$ for the NFV-enabled multicasting problems with and without resource capacity constraints, if the number of servers for implementing the service chain of each request is no greater than a constant $M$ (≥1). We also study dynamic admissions of NFV-enabled multicast requests without the knowledge of future request arrivals with the objective to maximize the network throughput, for which we propose an efficient heuristic, and for the special case of dynamic request admissions, we devise an online algorithm with a competitive ratio of $O(\log n)$ for it when $M=1$ , where $n$ is the number of nodes in the network. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising and outperform existing heuristics.

Tinjauan Terhadap Sinkronisasi Perencanaan Pembangunan Pasca Pemilihan Kepala Daerah Serentak di Kota Cimahi Tahun 2017

Cimahi City is one of the three regions that carried out national concurrent election in West Java Province. The victory of the pair Ir. H. Ajay M. Priatna, MM and Lieutenant Colonel (Ret.) Ngatiyana as determined by KPU on February 23, 2017 must undergo a long journey towards the inauguration for 9 (nine) months plus 6 (six) months of the Regional Medium Term Development Plan (RPJMD) for 2017-2022. Thus, the government for the 2017-2022 period only has 4 (four) years effective time to carry out its vision and mission. This study will review the synchronization of development planning after simultaneous regional head elections in Cimahi City in 2017, with descriptive methods through a qualitative approach. The source of research data is a group of government officials involved in the synchronization process. The study concluded that the synchronization of development planning in Cimahi City was in accordance with the legislation governing regional planning, the synchronization process of development planning was in transition to leadership which affected the drafting of the RKPD 2018 which became the basis for the RAPBD KUA-PPAS 2018 document. Although there are still human resource capacity constraints and an understanding of the technical division employee and the budget burden for 2018, the synchronization pattern of program priorities becomes a strategy to achieve leadership effectiveness Mayor/Vice Mayor of Cimahi Period 2017-2022.Keywords: synchronization, leadership transition and development planning

Optimal Torpedo Scheduling


 
 
 We consider the torpedo scheduling problem in steel production, which is concerned with the transport of hot metal from a blast furnace to an oxygen converter. A schedule must satisfy, amongst other considerations, resource capacity constraints along the path and the locations traversed as well as the sulfur level of the hot metal. The goal is first to minimize the number of torpedo cars used during the planning horizon and second to minimize the time spent desulfurizing the hot metal. We propose an exact solution method based on Logic based Benders Decomposition using Mixed-Integer and Constraint Programming, which optimally solves and proves, for the first time, the optimality of all instances from the ACP Challenge 2016 within 10 minutes. In addition, we adapted our method to handle large-scale instances and instances with a more general rail network. This adaptation optimally solved all challenge instances within one minute and was able to solve instances of up to 100,000 hot metal pickups.