Resource Allocation Scenarios Research Articles

An exact constraint programming method for the multi-manned assembly line balancing problem with assignment restrictions

This paper proposes an exact Constraint Programming (CP) method with an extensive focus on real-world constraints for the Multi-manned Assembly Line Balancing Problem with Assignment Restrictions (MALBP-AR). We perform an in-depth literature review to gather examples from real assembly lines and organize the AR regarding tasks, stations, workers, and mounting positions. Our study classifies the AR related to transformed resources and provides a general and unified model. We explore the concept of variable workplaces to dynamically assign workers to mounting positions and aggregate no overlap restrictions to avoid interference between workers. The classic MALBP model is extended by gradually incrementing the number of restrictions. The model variant found in the literature is herein called Partial MALBP-AR. Compared to the previous state-of-the-art Tabu Search Algorithm (TSA) for this problem, the Partial MALBP-AR found twelve additional optimality proofs. Besides the relevant results regarding solution quality, the CP method also has a satisfactory CPU performance. We also propose an entirely new set of AR and test these practical conditions with the so-called Extended MALBP-AR. Such an extended model, which covers all the AR presented here, reached optimality within the computational time limit for 36 out of 38 instances. The worst-case gap for an open instance is 8.20%. The results show a trade-off between the number of deemed restrictions and the computational performance. However, considering the detailed set of AR, we can obtain more representative solutions regarding the final balancing implementation compared to theoretical cases. The method can be used to design experiments, turning certain constraints on and off and allowing managers to evaluate different resource allocation scenarios.

Modeling the impact of resource allocation decisions on genomic prediction using maize multi‐environment data

AbstractIn a hybrid maize (Zea mays L.) breeding program that utilizes genomic selection, resource allocation used in phenotypic data acquisition must be balanced between population size, number of environments, and the number of testers used for generating hybrids. Plant breeders evaluate newly developed inbred lines using multi‐environment trials to account for genotype‐by‐environment interaction effects. The replication of hybrids across environments in these trials impacts the training data accuracy for developing genomic prediction models. This study examined the impact of resource allocation scenarios on genomic prediction accuracy using a multi‐environment trial dataset generated using inbred lines crossed to multiple testers. A total of 369 Stiff Stalk double haploid lines from a synthetic mapping population were testcrossed to three non‐Stiff Stalk inbred lines as testers, PHZ51, PHK76, and PHP02, and evaluated across 34 environments by the Genomes to Fields Initiative in 2020 and 2021. Resource allocation scenarios significantly impacted site‐specific genomic prediction accuracy for unobserved hybrids in unobserved environments. A training set with three to five environments that had the highest quality data produced similar prediction accuracy as data from 10 random environments for both observed and unobserved hybrids, indicating that strong prediction models can be built with a limited set of environments for both grain yield and plant height. We found that resource‐efficient prediction models that use data from one tester and three to five environments can effectively conduct selection of untested hybrids and in untested environments. Public research programs are often limited in testing capacity, and this study provides support for genomic selection in resource‐limited breeding programs.

The educational competition optimizer

In recent research, metaheuristic strategies stand out as powerful tools for complex optimization, capturing widespread attention. This study proposes the Educational Competition Optimizer (ECO), an algorithm created for diverse optimization tasks. ECO draws inspiration from the competitive dynamics observed in real-world educational resource allocation scenarios, harnessing this principle to refine its search process. To further boost its efficiency, the algorithm divides the iterative process into three distinct phases: elementary, middle, and high school. Through this stepwise approach, ECO gradually narrows down the pool of potential solutions, mirroring the gradual competition witnessed within educational systems. This strategic approach ensures a smooth and resourceful transition between ECO's exploration and exploitation phases. The results indicate that ECO attains its peak optimization performance when configured with a population size of 40. Notably, the algorithm's optimization efficacy does not exhibit a strictly linear correlation with population size. To comprehensively evaluate ECO's effectiveness and convergence characteristics, we conducted a rigorous comparative analysis, comparing ECO against nine state-of-the-art metaheuristic algorithms. ECO's remarkable success in efficiently addressing complex optimization problems underscores its potential applicability across diverse real-world domains. The additional resources and open-source code for the proposed ECO can be accessed at https://aliasgharheidari.com/ECO.html and https://github.com/junbolian/ECO.

A Tutorial on Cross-layer Optimization Wireless Network System Using TOPSIS Method

Each other, leading to issues such as interference, limited bandwidth, and varying channel conditions. These challenges require specialized optimization techniques tailored to the wireless environment. In wireless communication networks is to maximize the overall system throughput while ensuring fairness among users and maintaining quality of service requirements. This objective can be achieved through resource allocation optimization, where the available network resources such as bandwidth, power, and time slots are allocated to users in an optimal manner. Optimization-based approaches in wireless resource allocation typically involve formulating the resource allocation problem as an optimization problem with certain constraints.. These techniques provide practical solutions with reduced computational complexity, although they may not guarantee optimality. In summary, optimization-based approaches have been instrumental in studying resource allocation problems in communication networks, including the wireless domain. While techniques from the Internet setting have influenced the understanding of congestion control and protocol design, specific challenges in wireless networks necessitate tailored optimization techniques that account for interference, limited bandwidth, and varying channel conditions. power allocation problem in wireless ad hoc networks Cross-layer optimization refers to the process of jointly optimizing the allocation of resources across different layers of wireless networks, the interactions between different layers become more complex due to the shared medium and time-varying channel conditions. Nash equilibrium, where no user can unilaterally improve its own performance by changing its strategy. Game theory can capture the distributed nature of wireless networks and provide insights into the behavior of users in resource allocation scenarios Additionally, heuristics and approximation algorithms are often employed in wireless resource allocation due to the complexity of the optimization problems involved. In traditional cellular systems, each user is allocated a fixed time slot for transmission, regardless of their channel conditions. However, in opportunistic scheduling. Alternative parameters for “Data rate Ž kbps, Geographic coverage , Service requirements , cost ” Evaluation parameter for “Circuit-switched cell, CDPD, WLAN, Paging, Satellite.” “the first ranking training is obtained with the lowest quality of compensation.”

Sliding Window-Based Machine Learning for Environmental Inspection Resource Allocation.

Environmental regulation is pivotal in mitigating environmental risks and promoting sustainable development, yet regulators frequently encounter resource constraints when inspecting enterprises. To address this limitation, we employed four sliding window-based machine learning techniques to enhance effective environmental inspections. Utilizing feature-engineered time-series data of characteristic and compliance records from 16,777 chemical enterprises in Jiangsu between 2010 and 2021, the four models were used to establish the predictive models that link enterprise characteristics to the likelihood of inspection failure. The results indicated that the models were comparable with the widely utilized deep learning sequence model, the Long Short-Term Memory model, achieving areas under the ROC (receiver operating characteristic) curves exceeding 0.83. Past violation significantly influences future violations, with a more recent violation history exerting stronger impacts. Besides, using predicted failure rates, we proposed seven resource allocation scenarios, considering different regulation intensities to target high-risk enterprises. Notably, the provincial-level risk-based method yielded a significant inspection failure rate increase (detecting violation), surpassing 8-fold the baseline. Overall, our study offers regulators an optimized inspection resource allocation method, thereby enhancing the regulatory effectiveness. Moreover, this study demonstrates the potential of window-based machine learning techniques in environmental regulation and highlights the importance of data-driven decision-making for promoting sustainable development.

Dynamic SFC Embedding Algorithm Assisted by Federated Learning in Space–Air–Ground-Integrated Network Resource Allocation Scenario

Traditional terrestrial wireless communication networks cannot support the requirements for high-quality services for artificial intelligence applications such as smart cities. The space-air-ground integrated network (SAGIN) provides a solution to address this challenge. However, SAGIN is heterogeneous, time-varying, and multi-dimensional information sources, making it difficult for traditional network architectures to support resource allocation in large-scale complex network environments. This paper proposes a service provision method based on Service Function Chaining (SFC) to solve this problem. Network Function Virtualization (NFV) is essential for efficient resource allocation in SAGIN to meet the resource requirements of user service requests. We propose a federation learning (FL) based algorithm to solve the embedding problem of SFCs in SAGIN. The algorithm considers different characteristics of nodes and resource load to balance resource consumption. Then an SFC scheduling mechanism is proposed that allows SFC reconfiguration to reduce the service blocking rate. Simulation results show that our proposed FL-VNFE algorithm is more advantageous compared to other algorithms, with 12.9%, 2.52%, and 10.5% improvement in long-term average revenue, acceptance rate, and long-term average revenue-cost ratio, respectively.

How a child entangles empathy and computational thinking in reasoning about fairness

In this work, we argue for expanding the scope of K-12 computational thinking (CT) integration contexts to include everyday scenarios involving moral reasoning. Epistemic overlap between computational thinking practices and moral reasoning suggest that these contexts are potentially rich sites to see “seeds of CT” in children's reasoning and can provide rich educational pathways for children into CT. Taking a case-study approach, we examine the reasoning of a second-grader, Ollie, on a task involving fair allocation of resources to victims of a natural disaster. Our analysis finds that Ollie's reasoning was rich with seeds of CT (e.g., problem formulation, abstraction, complex-systems thinking) and that empathy served as an important supporting role to the CT. This work has implications for curricular design, suggesting that fairness and resource allocation scenarios with built-in opportunities for empathy might provide rich sandboxes for CT integration.

Trace-Driven Scaling of Microservice Applications

The containerized microservices architecture is being increasingly used to build complex applications. To minimize operating costs, service providers typically rely on an auto-scaler to "right size" their infrastructure amid fluctuating workloads. The agile nature of microservice development and deployment requires an auto-scaler that does not require significant effort to derive resource allocation decisions. In this paper, we investigate reducing auto-scaler development effort along a number of dimensions. First, we focus on a technique that does not require an expert to develop a model, e.g., a queuing model or machine learning model, of the system and tweak the model as the underlying microservice application changes. Second, we explore ways to limit the number of workload patterns that need to be considered. Third, we study techniques to reduce the number of resource allocation scenarios that one has to explore before deploying the auto-scaler. To address these goals, we first analyze the workload of 24,000 real microservice applications and find that a small number of workload patterns dominate for any given application. These results suggest that auto-scaler design can be driven by this small subset of popular workload patterns thereby limiting effort. To limit the number of resource allocation scenarios explored, we develop a novel heuristic optimization technique called MOAT, which outperforms Bayesian Optimization often used for such exercises. We combine insights obtained from real microservice workloads and MOAT to realize an auto-scaler called TRIM that requires no system modeling. For each popular workload pattern identified for an application, TRIM uses MOAT to pre-compute a near minimal resource allocation that satisfies end user response time targets. These resource allocations are then used at runtime when appropriate. We validate our approach using a variety of analytical, on-premise, and public cloud systems. From our results, TRIM in consort with MOAT significantly improves the performance of the industry-standard HPA auto-scaler by achieving up to 92% fewer response time violations and up to 34% lower costs compared to using HPA in isolation.

Optimal cross-layer resource allocation in fog computing: A market-based framework

The potential upsides of fog computing are undeniable, including better near-real-time response, reduced transmission costs and analysis for IoT, which will further have a significant impact on operation of factories, enterprises, organizations and even the quality of our life. However, due to the resource-constrained nature, heterogeneity and distributed nodes, how to manage the extraordinarily complex fog computing resources is necessary to improve the QoE of users. This paper investigates the rational resource allocation for fog computing from the cross-layer point of perspective, and formulates the resource allocation scenario as Cloud–Fog–User market which is composed of cloud center devices, fog nodes, and user devices. Firstly, the resource market is divided into Cloud–Fog market containing cloud layer and fog layer, and Fog–User market containing fog layer and user layer based on different scenarios separately. The paper proposes a market-based framework for fog computing networks, which enables the cloud layer and the fog layer to allocate their resources in the form of pricing, payment and supply–demand relationship, while analyzes the relationship among resource allocation participants in the market. Then the paper investigates the utility optimization models from the viewpoints of both the participants of respective markets, so as to optimize optimal payment and optimal resource allocation via convex optimization techniques, and proposes a gradient-based iterative algorithm to optimize the utilities, respectively. Finally, experimental results have displayed the performance of convex analysis results and effectiveness of the proposed algorithm.

Minimising inequality in multiagent resource allocation

We analyse the problem of finding an allocation of resources in a multiagent system that is as fair as possible in terms of minimising inequality between the utility levels enjoyed by the individual agents. We use the well-known Atkinson index to measure inequality and we focus on the distributed approach to multiagent resource allocation, where new allocations emerge as the result of a sequence of local deals between groups of agents who agree on an exchange of some of the items in their possession. Our results show that it is possible to design systems that provide theoretical guarantees for optimal outcomes that minimise inequality, but also that there are significant computational hurdles to be overcome in the worst case. In particular, finding an optimal allocation is computationally intractable and under the distributed approach a large number of structurally complex deals, possibly involving many agents and items, may be required before convergence to a socially optimal allocation. This remains true even in severely restricted resource allocation scenarios where all agents have the same utility function. From a methodological point of view, while much work in multiagent resource allocation relies on combinatorial arguments, here we instead use insights from basic calculus.

Simple deterministic modeling can guide the design of breeding pipelines for self‐pollinated crops

AbstractGenetic gains delivered by plant breeding programs in the developing world must increase to deliver adaptation to a changing climate. Programs should be planned and optimized, with respect to gains per year and per dollar invested, as cyclic population improvement pipelines whose selection response is described by the breeder's equation (BE). We present a spreadsheet‐implemented model for the BE that includes operating costs. The model can be easily used to compare predicted selection response per cycle for different pipeline designs and resource allocation scenarios for self‐pollinated crops or hybrid crops that use inbred lines as parents. We modeled breeding programs that generate and select among F2– to F6–derived or doubled‐haploid (DH) lines, advance populations from one to three generations annually, and conduct agronomic testing in one to three stages during the main cropping season. Reducing cycle time is the most efficient method of increasing genetic gain under most circumstances. Although gains increase with increased population size and selection intensity, clear optima exist, in terms of the cost of a unit of gain, at relatively small population sizes and moderate selection intensities. Investments in reducing cycle length to three or even two years generate large increases in genetic gain and are more cost‐effective than increasing population size and standardized selection differential. High rates of genetic gain can be delivered by small breeding programs that complete cycles quickly.

Collective servicing of heterogenous traffic streams over 3GPP LTE network and application of access control

Due to the progressions in the cellular technologies in recent times, communicating networks have seen vast expansion in the volumes and multiplicities of the data transmitted over the enhanced smart network especially IoT. The regulation and organization of such enormous amounts of data is posing substantial challenges across the various network industries. Because majority of data traffic transferred over such complex networks is comprised of "Big Data" That is continuously growing and evolving. 3GPP have acknowledged such scenarios and have given several recommendations and procedures to manage resources in such situations. But regrettably there are no concrete explanations and techniques on how these resources should be distributed in heterogenous networks. The mathematical modeling that considers the characteristics of traffic flows originating and accepting for servicing is deemed as one of the critical answers to such problems. To address this issue, we have designed a model of resource allocation and sharing strategy for conjoint servicing of real time video traffic referred as "Heavy traffic" of surveillance cameras and low-quality video traffic, referred as "Light traffic" which are transmitted as files over LTE cell facilities in a surveillance operator system. In the model, the access control is incorporated to create the conditions for differentiated servicing of heterogenous traffic incoming sessions. The mathematical model can be utilized to study reservation-based resource allocation scenarios over a shared heterogenous network.

Towards Minimizing Resource Usage With QoS Guarantee in Cloud Gaming

Cloud gaming has been very popular recently, but providing satisfactory gaming experiences to players at a modest cost is still challenging. Colocating several games onto one server could improve server utilization. However, prior work regarding colocating games either ignores the performance interference between games or uses simple performance model to charaterize it, which may make inefficient game colocation decisions and cause QoS violations. In this article, we address the resource allocation issues for colocating games in cloud gaming. We first propose a novel machine learning-based performance model, which is able to capture the complex relationship among the performance interference, the contention features of colocated games and resource partition. Guided by the performance model, we then propose efficient and effective algorithms for two resource allocation scenarios in cloud gaming. We evaluate the proposed solutions through extensive experiments using a large number of real popular games. The results show that our performance model is able to identify whether a colocated game satisfies QoS requirement within an average error of 5 percent, which significantly outperforms the alternatives. Our resource allocation algorithms are able to increase the resource utilization by up to 60 percent compared to the state-of-the-art solutions.

Two Effective Strategies to Support Cross-Organization Emergency Resource Allocation Optimization

Cross-organization emergency resource allocation optimization problem is essential to guarantee a successful emergency disposal, and it has become a research focus of modern emergency management. Generally speaking, there are two possible types of resource allocation scenarios: (1) if the emergency resources are overallocated, on the one hand, parallel execution of independent emergency activities can be supported and the emergency disposal time is reduced; on the other hand, too many idle resources may cause low resource utilization rate, high scheduling overhead, and high cost; and (2) if emergency resources are underallocated, this may lead to resource conflicts and the need for some emergency activities to wait for others to complete, and finally the emergency disposal time may increase. Therefore, reasonable emergency resource allocation strategies are highly desired. To the best of our knowledge, there is no formal approach to support the cross-organization emergency resource allocation issue. To handle this problem, we propose a two-layered framework to facilitate the allocation of limited emergency resources to meet its time constraints with high efficiency. More specifically, a kind of Petri net extended with time, resource, and message information, denoted as CE-net, is presented to model cross-organization emergency response processes. Based on the obtained CE-net, the minimum resource requirements are obtained with corresponding algorithms. Then, Minimum Execution Time (MET) strategy and Minimum Resource Consumption (MRC) strategy with their corresponding estimated execution intervals are introduced to facilitate the stakeholder to determine which strategy is suitable according to the timing requirements. A cross-organization fire emergency case is applied to validate the proposed approaches throughout the whole paper.

Costs of providing HIV care and optimal allocation of HIV resources in Guyana.

Great strides in responding to the HIV epidemic have led to improved access to and uptake of HIV services in Guyana, a lower-middle-income country with a generalized HIV epidemic. Despite efforts to scale up HIV treatment and adopt the test and start strategy, little is known about costs of HIV services across the care cascade. We collected cost data from the national laboratory and nine selected treatment facilities in five of the country's ten Regions, and estimated the costs associated with HIV testing and services (HTS) and antiretroviral therapy (ART) from a provider perspective from January 1, 2016 to December 31, 2016. We then used the unit costs to construct four resource allocation scenarios. In the first two scenarios, we calculated how close Guyana would currently be to its 2020 targets if the allocation of funding across programs and regions over 2017-2020 had (a) remained unchanged from latest-reported levels, or (b) been optimally distributed to minimize incidence and deaths. In the next two, we estimated the resources that would have been required to meet the 2020 targets if those resources had been distributed (a) according to latest-reported patterns, or (b) optimally to minimize incidence and deaths. The mean cost per test was US$15 and the mean cost per person tested positive was US$796. The mean annual cost per of maintaining established adult and pediatric patients on ART were US$428 and US$410, respectively. The mean annual cost of maintaining virally suppressed patients was US$648. Cost variation across sites may suggest opportunities for improvements in efficiency, or may reflect variation in facility type and patient volume. There may also be scope for improvements in allocative efficiency; we estimated a 28% reduction in the total resources required to meet Guyana's 2020 targets if funds had been optimally distributed to minimize infections and deaths. We provide the first estimates of costs along the HIV cascade in the Caribbean and assessed efficiencies using novel context-specific data on the costs associated with diagnostic, treatment, and viral suppression. The findings call for better targeting of services, and efficient service delivery models and resource allocation, while scaling up HIV services to maximize investment impact.

Multi-objective optimal allocation of water resources based on ‘three red lines’ in Qinzhou, China

Abstract Water shortages and pollution emerge because of anthropogenic demands. Since 2011, ‘China's Most Stringent Water Resources Management’ (CMSWRM) has been comprehensively enacted in the country. This paper presents the characteristics of the ‘three red lines’ (TRL) and a multi-objective optimal allocation model based on the TRL constraint, considering the benefits for society, the economy, and the environment. This model had been applied to the reasonable allocation of water supply and demand in Qinzhou for the planning years of 2020 and 2030. Two water resource allocation scenarios for these years were configured by setting different chemical oxygen demand (COD) concentrations for wastewater discharge in the municipal, secondary, tertiary, and agricultural sectors. The gamultiobj function based on the NSGA-II algorithm was used to solve the model in MATLAB. The results indicate that if COD concentrations in each sector are not reduced, then restrictions on domestic water sources will be necessary, both in 2020 and 2030. The two water resource allocation scenarios in 2020 and 2030 can provide a reference for decision-makers in Qinzhou to implement CMSWRM.

Joint offloading decision and resource allocation for mobile edge computing enabled networks

Mobile edge computing (MEC) based solutions are essential and of great significance for a wide range of promising 5G wireless big data services such as remote healthcare systems and AR/VR games. Present research in this area focuses on the downlink resource allocation scenarios from MEC servers to user equipments (UEs). This paper considers a multi-user MEC-enabled wireless communication system, where UEs suffer limited communication and computation resources. To achieve higher energy efficiency and the better experience for UEs, we aim to maximize the number of offloaded tasks for all UEs in uplink communication while maintaining the computation resources of MEC at an acceptable level. The formulated problem is an NP-hard mixed-integer nonlinear programming problem and it is a challenge to solve it efficiently. As such, an efficient low-complexity heuristic algorithm is proposed, which provides a near-optimal solution with a low time cost. The results show that the proposed scheme achieves the higher number of successful offloaded tasks than the existing centralized resource allocation algorithm (CRAA) and centralized decision and resource allocation algorithm that UEs with the largest saved energy consumption accepted first (CAR-E) under different scenarios. Moreover, the relationship between the optimal transmission power and the computation resource of MEC is investigated. The results obtained in this paper can be extended to design a novel framework of communication, computation and smart coded caching MEC networks.

Developing an Interactive Spatial Multi-Attribute Decision Support System for Assessing Water Resources Allocation Scenarios

In water resource management, assessing water resource allocation scenarios (WRASs) is an important multi-attribute decision making (MADM) problem. It involves spatially varied indicators, which interact with each other and impacts of the scenarios. These attributes are often simplified by using conventional Decision Support Systems (DSSs). In present research, a novel interactive spatial DSS for assessment of WRASs was developed. Effects of indicators type, decision matrix structures, and MADM models on priorities and ranks of scenarios were investigated in Aras basin. Sensitivity analysis of results showed that the interactive structure, comprising spatially distributed indicators and analytical network process (SANP), was the most stable model in terms of ranking. Providing more realistic results, the developed SDSS can be applied in other basins or for other MADM problems.

CDF-Based Multiuser Scheduling for Downlink Non-Orthogonal Multiple Access (NOMA)

In this paper, we propose cumulative distribution function (CDF)-based multiuser scheduling (CMS) for downlink non-orthogonal multiple access (NOMA) to accommodate a massive number of IoT devices over limited radio resources and statistically analyze the ergodic capacity of devices. With the proposed CMS algorithm, multiple devices having relatively better channel gain to exploit multiuser diversity are selected while meeting QoS requirements by controlling the amount of resources. We first provide the user selection probability in terms of the weight of each device, which determines the amount of time resources. Based on some selected results, it is shown that equal weights ensure fair resource allocation. Further, for fair resource allocation scenario, the distribution of signal-to-interference-plus-noise ratio (SINR) when a specific device is selected is derived. With this derived statistical result of SINR, we analyze the ergodic capacity of the specific device. Some selected results show that the proposed CMS algorithm allocates the time resource more fairly than proportional fair scheduling. Further, the average throughput and amount of resources can be controlled by adjusting the weights of each device, so different QoS requirements can be guaranteed.

Data envelopment analysis for highway asset investment assessment

Highway agencies have been using many of the elements of asset management with the support of various decision-making tools. To determine the most effective investment strategy with scarce resources, the integration, and hence better utilization, of existing tools and practices across asset classes is generally lacking. This paper applies data envelopment analysis (DEA) to benchmark different highway investment scenarios using existing data or data readily available through existing models. Three asset types, pavements, bridges, and traffic signage, are investigated. Asset investment analysis results from the Highway Economic Requirements System State Version (HERS-ST) application, the PONTIS bridge management system software, and purpose-built traffic signage spreadsheet are obtained to capture the changes of performance measures under various budget scenarios and are further used as the inputs for the DEA process to benchmark investment scenarios for each individual asset. Subsequently, the performance measures and budget levels are assembled in the Asset Manager-NT software, whose results are input into DEA to benchmark cross-assets resource allocation scenarios. Planning for the management of highway network is addressed via case studies in a systematic manner that recognizes the tradeoffs among different funding periods and objectives such as preserving existing investments, safety, roughness and user costs. This study has established a preliminary implementable framework of highway asset management by linking DEA approach and current widely used decision-making tools for more efficient investments within and cross assets, and better understand of the tradeoffs, costs and consequences of various asset management decisions.