Cost-effective Resource Allocation Research Articles

Network traffic prediction by learning time series as images

Network traffic prediction is crucial for cost-effective network management, resource allocation, and security in the emergent software-defined and zero-touch networks. Machine Learning (ML) models such as Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) have shown great promise in traffic prediction. This work proposes a hybrid ML model consisting of pre-trained AlexNet and ResNet models, the 2D Convolutional Neural Network (CNN), Vision Transformer (ViT), and LSTM/GRU models and their variants to predict network traffic. This work explores two approaches for converting the time series data to images by allowing more precise feature extraction and then performing traffic prediction on an image dataset, thus increasing accuracy. Given their proficiency in extracting image features we use three pre-trained models: the AlexNet, the ResNet combined with CNN, and the ViT. Then, we use those models as inputs to the LSTM, Bi-LSTM, and the GRU, Bi-GRU models for predicting network traffic data. The models are evaluated on the GÉANT and Abilene traffic networks and assessed based on the Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Comparison of results with existing network traffic prediction techniques shows considerable improvements in all performance metrics revealed by the Wilcoxon test. Our best model results for the GÉANT and Abilene datasets are respectively; MSE of 0.00058 and 0.03078, RMSE of 0.02415 and 0.17544, and MAE of 0.00774 and 0.13384.

Cost-effectiveness of a multicomponent-adherence intervention in fracture liaison services

ABSTRACT Background This study aims to assess the lifetime cost-effectiveness of a multi-component adherence intervention (MCAI), including a patient decision aid and motivational interviewing, compared to usual care in patients with a recent fracture attending fracture liaison services (FLS) and eligible for anti-osteoporosis medication (AOM). Research design and methods Data on AOM initiation and one-year persistence were collected from a quasi-experimental study conducted between 2019 and 2023 in two Dutch FLS centers. An individual level, state-transition Markov model was used to simulate lifetime costs and quality-adjusted life years (QALYs) with a societal perspective of MCAI vs usual care. One-way and probabilistic sensitivity analyses were conducted including variation in additional FLS and MCAI costs (no MCAI cost in baseline). Results MCAI was associated with gain in QALYs (0.0012) and reduction in costs (-€16) and is therefore dominant. At the Dutch willingness-to-pay threshold of €50,000/QALY, MCAI remained cost-effective when increasing costs of the FLS visit or the yearly maintenance cost for MCAI up to +€60. Probabilistic sensitivity analysis demonstrated MCAI to be dominant in 54% of the simulations and cost-effective in 87% with a threshold of €50,000/QALY. Conclusions A MCAI implemented in FLS centers may lead to cost-effective allocation of resources in FLS care, depending on extra costs.

An efficient healthcare system by cloud computing and clustering-based hybrid machine learning algorithm

Cloud computing, deep learning, clustering, genetic, and ensemble algorithms in healthcare are gaining popularity. This research highlights the relevance and complex repercussions of this integration. Cloud computing is transforming healthcare by providing scalable data storage and application access. It streamlines data exchange between hospitals, researchers, and institutions. Deep learning allows healthcare systems to use artificial intelligence for diagnostics, predictive analytics, and customized medication. Clustering algorithms segment patients, improving therapy and intervention customization. Genetic algorithms can optimize healthcare processes like treatment planning and resource allocation. Ensemble algorithms combine multiple models to improve predicted accuracy, enabling strong healthcare decision-making. This connection has several benefits. Healthcare systems become more efficient and scalable, resulting in cost-effective resource allocation. Access to patient data and apps promotes collaborative research and real-time healthcare. Deep learning algorithms can recognize complex medical data patterns, improving illness diagnosis and treatment results. Clustering algorithms streamline customized healthcare by stratifying individuals by clinical variables. Genetic algorithms optimize resource allocation, assuring healthcare resource efficiency. Ensemble algorithms improve predicted accuracy and clinical decision support system dependability. Its efficiency, accessibility, and prediction accuracy are positives, but security, resource constraints, interpretability, and ethical issues are obstacles.

Cost-effective task partial offloading and resource allocation for multi-vehicle and multi-MEC on B5G/6G edge networks

The Beyond 5th Generation/6th Generation (B5G/6G) wireless communication technology, characterized by ultra-low latency and ultra-multiple connections, and B5G/6G edge networks provide a new approach to solve delay-sensitive and computation-intensive vehicle applications in Intelligent Transportation Systems (ITS). However, due to the high mobility of vehicles, it becomes challenging to provide mobility-enabled resource management and delivery tasks from multiple vehicle users to Base Station (BS) in B5G/6G edge networks. Therefore, we investigate a multi-vehicle user and multi-BS collaborative offloading system in B5G/6G edge networks, and propose a joint optimization scheme for collaborative offloading, unequal task splitting and CPU resource allocation. In this scheme, tasks from vehicle users can be partially offloaded to associated BS, and can be further split and offloaded to adjacent BS with multi-hop network technology, thereby minimizing the weighted sum of latency and energy consumption. Thus, a Mixed Integer Nonlinear Optimization Problem (MINLP) is constructed. To address this issue, we propose a two-level alternating iterative framework based on a two-layer co-offloading architecture and Sequential Quadratic Programming algorithm (SQP). In the upper level, we introduce a multi-BS collaboration algorithm at the edge layer and develop a collaborative offloading strategy between vehicle users and the edge layer, utilizing Game Theory (GT). In the lower level, based on the SQP algorithm, the optimal task splitting ratio and the optimal CPU frequency allocation strategy for each vehicle user task are solved. Simulation results demonstrate that the proposed algorithm not only effectively reduces system costs, but also excels in reducing the system latency or energy consumption when considered separately.

Cost-Effective Traffic Scheduling and Resource Allocation for Edge Service Provisioning

The multi-access edge computing (MEC) paradigm has emerged as a critical solution to address the exponential growth in mobile web services and devices. By implementing an edge-based service provisioning system (EPS) with servers located at the network’s edge, both transmission and computation efficiency can be significantly enhanced. Nevertheless, it is also essential to carefully consider the resource allocation for services, the traffic management of requests, and the path arrangement for data delivery to ensure the cost-effective operation of the EPS. Therefore, we investigate and quantify the relationship between the performance and cost of the EPS in this paper, and model the cost-effective service provisioning problem as a multi-phase convex optimization problem. An online algorithm whose name is RDC based on the Lyapunov framework is proposed to decompose this problem into several sub-problems.Additionally, a heuristic approach that partitions edge servers into several clusters, called RDC-NeP and based on RDC, has also been proposed to reduce computational complexity. A series of experiments were conducted to evaluate the proposed approach. The results demonstrate that RDC can effectively balance expense and performance, while RDC-NeP significantly simplifies the processing of RDC when the problem scale increases.

Cost-Effective Resource Allocation and Optimization Strategies for Multi-Cloud Environments

Abstract: As organizations increasingly adopt multi-cloud strategies to leverage the benefits of multiple cloud service providers, the need for effective cost optimization becomes paramount. This research paper aims to explore and propose innovative approaches for optimizing costs in multi-cloud environments. The study will investigate resource allocation, workload placement, and performance optimization techniques, taking into account factors such as workload variability, service-level agreements (SLAs), and pricing models across different cloud providers. The objective is to develop cost-effective strategies that balance performance and cost across a heterogeneous cloud ecosystem, ultimately helping organizations make informed decisions in their multi-cloud deployments. This research topic addresses the growing interest in multi-cloud architectures and the associated challenges in managing costs while ensuring high-quality service delivery. It provides a valuable contribution to the field of cloud computing and cost optimization, which is increasingly crucial for businesses and enterprises seeking to maximize the benefits of cloud technology.

Functional connectomic profile correlates with effective anterior thalamic stimulation for refractory epilepsy

BackgroundDeep brain stimulation of the anterior nucleus of the thalamus (ANT-DBS) is an effective treatment for refractory epilepsy; however, seizure outcome varies among individuals. Identifying a reliable noninvasive biomarker to predict good responders would be helpful. ObjectivesTo test whether the functional connectivity between the ANT-DBS sites and the seizure foci correlates with effective seizure control in refractory epilepsy. MethodsWe performed a proof-of-concept pilot study of patients with focal refractory epilepsy receiving ANT-DBS. Using normative human connectome data derived from 1000 healthy participants, we investigated whether intrinsic functional connectivity between the seizure foci and the DBS site was associated with seizure outcome. We repeated this analysis controlling for the extent of seizure foci, distance between the seizure foci and DBS site, and using functional connectivity of the ANT instead of the DBS site to test the contribution of variance in DBS sites. ResultsEighteen patients with two or more seizure foci were included. Greater functional connectivity between the seizure foci and the DBS site correlated with more favorable outcome. The degree of functional connectivity accounted for significant variance in clinical outcomes (DBS site: |r| = 0.773, p < 0.001 vs ANT-atlas: |r| = 0.715, p = 0.001), which remained significant when controlling for the extent of the seizure foci (|r| = 0.773, p < 0.001) and the distance between the seizure foci and DBS site (|r| = 0.777, p < 0.001). Significant correlations were independent of variance in the DBS sites (|r| = 0.148, p = 0.57). ConclusionThese findings suggest that functional connectomic profile is a potential reliable non-invasive biomarker to predict ANT-DBS outcomes. Accordingly, the identification of ANT responders could decrease the surgical risk for patients who may not benefit and optimize the cost-effective allocation of health care resources.

Cost-Effectiveness of Hospice Palliative Care for Patients With Cancer and Family Caregivers: A Multicenter Study in Kazakhstan

ObjectivesIn Kazakhstan, palliative care is offered through hospices, cancer centers, general hospitals, and mobile teams to approximately 107 000 patients in need. As a country with a transitional economy and a newly implemented social healthcare insurance system, Kazakhstan seeks a cost-effective allocation of limited resources for end-of-life care. This study aimed to assess cost-effectiveness of hospice-based palliative care for patients with cancer compared with the current standard of care provided in cancer centers across the country and, thereby, provide a better understanding for policy making regarding palliative care. MethodsA total of 182 family caregivers were recruited, 104 from 3 hospices and 78 from 3 palliative care units of cancer centers. Patients’ state of health and family caregivers’ burden were assessed with the Palliative Outcome Scale and the Zarit Burden Interview. Direct medical and nonmedical costs and family caregivers’ out-of-pocket expenses associated with palliative care were collected. One-way and probabilistic sensitivity analysis was conducted by generating 1000 resamples using bootstrapping with Monte-Carlo simulation. ResultsAfter 14 days of inpatient palliative care, patients’ mean Palliative Outcome Scale score was 2.5 points better in the hospice group than the cancer center group. Family caregiver burden was 4.5 points better in the hospice group. Mean treatment costs were $31 lower for the hospice group. There was a statistically significant correlation between the total cost of treatment and patients’ quality of life (r = 0.58). Probabilistic sensitivity analysis showed that hospice-based care has better outcomes and lower costs than care provided in cancer centers in 80% of tested scenarios. ConclusionHospice-based palliative care is cost-effective compared with the care provided in palliative units of cancer centers in resource-limited settings in Kazakhstan.

Global burden of cardiovascular disease attributable to metabolic risk factors, 1990–2019: an analysis of observational data from a 2019 Global Burden of Disease study

ObjectivesAn up-to-date, detailed global analysis of the current status of the metabolic-attributed cardiovascular disease (CVD) burden has not been reported. Therefore, we investigated the global burden of metabolic-attributed CVD and...

Developing a Highly Functional Nursing Resource Team With New Graduates in an Academic Health System

Developing a new graduate (NG) nursing resource team (NRT), also called a float team, requires a well-rounded structured program. NRTs provide flexible, cost-effective resource allocation, with proven positive outcomes in employee engagement and retention. NRT leadership developed a thoughtful approach to onboarding NG RNs, with positive outcomes evident in the reduction of salary costs and vacancies/turnover rates, and the increase in positive staff satisfaction/engagement. This paper describes the development and outcomes associated with an innovative approach in addressing contemporary staffing constraints through the development of a centralized, flexible, and resilient NG NRT in an academic health system.

Spatial distribution and developmental trajectories of crime versus crime severity: do not abandon the count-based model just yet

Purpose/backgroundA new body of research that focuses on crime harm scores rather than counts of crime incidents has emerged. Specifically in the context of spatial analysis of crime, focusing on crime harm suggests that harm is more concentrated than counts, at the level of crime hot spots. It remains presently unclear what drives the concentration distributions, and whether the count-based model should be abandoned.MethodsCross-sectional and longitudinal analysis of 6 year of spatiotemporal crime data in Toronto, Canada, to compare patterns and concentration of crime harm (measured in terms of the Crime Severity Index (CSI) against crime counts. Conditional probabilities, trajectory analyses, power few concentrations, and spatial Global Moran’s I are used to infer generalised trends from the data.FindingsOverall CSI and crime counts tend to exhibit similar concentrations at the spatial micro levels, except against-the-body crimes such as violence which seems to drive nearly all the variations between the two measurement types. Violence harm spots tend to be more dispersed citywide and often do not remain constant year-to-year, whereas overall crime hotspots are more stable over time. Nevertheless, variations in disproportionally high crime hot spots are associated with total variations in crime, with as little as 1% increase in crime levels in these hot spots translating into substantial overall gains in recorded crime citywide.ConclusionsAbandoning count-based models in spatial analysis of crime can lead to an incomplete picture of crime concentrations. Both models are needed not just for understanding spatial crime distributions but also for cost-effective allocation of policing resources.

Cost-Effective and Energy-Aware Resource Allocation in Cloud Data Centers

Cloud computing supports the fast expansion of data and computer centers; therefore, energy and load balancing are vital concerns. The growing popularity of cloud computing has raised power usage and network costs. Frequent calls for computational resources may cause system instability; further, load balancing in the host requires migrating virtual machines (VM) from overloaded to underloaded hosts, which affects energy usage. The proposed cost-efficient whale optimization algorithm for virtual machine (CEWOAVM) technique helps to more effectively place migrating virtual machines. CEWOAVM optimizes system resources such as CPU, storage, and memory. This study proposes energy-aware virtual machine migration with the use of the WOA algorithm for dynamic, cost-effective cloud data centers in order to solve this problem. The experimental results showed that the proposed algorithm saved 18.6%, 27.08%, and 36.3% energy when compared with the PSOCM, RAPSO-VMP, and DTH-MF algorithms, respectively. It also showed 12.68%, 18.7%, and 27.9% improvements for the number of virtual machine migrations and 14.4%, 17.8%, and 23.8% reduction in SLA violation, respectively.

The Cost Effectiveness of Axicabtagene Ciloleucel Versus Best Supportive Care in the Treatment of Adult Patients with Relapsed or Refractory Large B-Cell Lymphoma (LBCL) After Two or More Lines of Systemic Therapy in Canada.

Axicabtagene ciloleucel (axi-cel) received marketing authorisation in Canada for the treatment of relapsed or refractory large B-cell lymphoma after two or more lines of systemic therapy, and the clinical and economic value of axi-cel to patients and the healthcare system should be examined. The objective of this analysis is to determine, from societal and public healthcare payer perspectives, the cost effectiveness of axi-cel versus best supportive care for patients with relapsed or refractory large B-cell lymphoma in Canada. A pharmacoeconomic model was developed and populated with clinical data derived from the ZUMA-1 and SCHOLAR-1 studies using a propensity score-matched comparison. A partitioned survival mixture-cure modelling approach was taken to characterise the potential curative effect of axi-cel therapy in large B-cell lymphoma. Healthcare resource utilisation and adverse event data were based on results from ZUMA-1, and utility values were derived from ZUMA-1 data supplemented with published literature. Costs (in 2021 Canadian dollars) were taken from publicly available Canadian cost databases and published literature. Benefits and costs were discounted at 1.5% per year, and sensitivity analyses were conducted to assess the robustness of the results. In the base case, axi-cel generated an incremental 6.2 life-years compared to best supportive care, corresponding to 4.6 additional quality-adjusted life-years, and was associated with $606,010 in additional costs. The incremental cost-utility ratio was $132,747 per quality-adjusted life-year gained compared with best supportive care from a societal perspective ($106,392 per quality-adjusted life-year gained from a public healthcare payer perspective). Key drivers of the analysis included progression-free survival and overall survival values for axi-cel. The results of this analysis suggest that axi-cel may be considered a cost-effective allocation of resources compared with best supportive care for the treatment of adult patients with relapsed or refractory large B-cell lymphoma in Canada.

The fight against malaria: Diminishing gains and growing challenges.

Since the year 2000, historic reductions in malaria incidence and mortality have been driven by the widespread distribution of bed nets, drugs, and insecticides for the prevention and treatment of malaria. Scale-up of these tools has been enabled by an increase in malaria financing compounded by price reductions, yet these trends are unlikely to continue at the same rate. Rapid population growth in high-endemic areas requires procurement of more of these tools just to maintain current coverage, even as prices are likely to increase as resistance to drugs and insecticides forces shifts to newer products. Further progress toward the long-term goal of malaria eradication requires a combination of greater funding, more cost-effective resource allocation, and fundamental changes to the global malaria control strategy.

Robust and Cost-effective Resource Allocation for Complex IoT Applications in Edge-Cloud Collaboration

Robust and Cost-effective Resource Allocation for Complex IoT Applications in Edge-Cloud Collaboration

Global burden of rheumatic heart disease and its association with socioeconomic development status, 1990-2019.

Rheumatic heart disease (RHD) remains an important health issue, yet global attention to RHD is diminishing. This study aimed to investigate the global burden of RHD and its relationship with socioeconomic development status. Data were obtained from the Global Burden of Disease (GBD) 2019 database. Incidence, prevalence, disability-adjusted life years (DALYs), and mortality numbers and rates for RHD were extracted and stratified by sex, level of socio-demographic index (SDI), country, and territory. In addition, the burden of RHD was compared across age groups. From 1990 to 2019, the age-standardized incidence and prevalence rates of RHD increased by 14.4% (11.2-17.0%) and 13.8% (11.0-16.0%), respectively. Incidence and prevalence rates showed an increasing trend in low SDI and low-middle SDI locations, while high-middle SDI and high SDI locations showed a decreasing trend. The age-standardized DALYs and mortality rates of RHD decreased by 53.1% (46.4-60.0) and 56.9% (49.8-64.7%), and this downward trend was more prominent in high-middle SDI and middle SDI locations. In addition, the age of incidence and prevalence rates were concentrated between 5-24 years and 15-49 years, predominantly in poor regions, and RHD appeared to be more common in women than in men. The burden of RHD is negatively correlated with socioeconomic development status. In particular, the burden of RHD among children, adolescents, and women of childbearing age in poorer regions requires more attention. Policymakers should use the 2019 GBD data to guide cost-effective interventions and resource allocation for RHD.

Future health spending forecast in leading emerging BRICS markets in 2030: health policy implications

BackgroundThe leading emerging markets of Brazil, Russia, India, China and South Africa (BRICS) are increasingly shaping the landscape of the global health sector demand and supply for medical goods and services. BRICS’ share of global health spending and future projections will play a prominent role during the 2020s. The purpose of the current research was to examine the decades-long underlying historical trends in BRICS countries’ health spending and explore these data as the grounds for reliable forecasting of their health expenditures up to 2030.MethodsBRICS’ health spending data spanning 1995–2017 were extracted from the Institute for Health Metrics and Evaluation (IHME) Financing Global Health 2019 database. Total health expenditure, government, prepaid private and out-of-pocket spending per capita and gross domestic product (GDP) share of total health spending were forecasted for 2018–2030. Autoregressive integrated moving average (ARIMA) models were used to obtain future projections based on time series analysis.ResultsPer capita health spending in 2030 is projected to be as follows: Brazil, $1767 (95% prediction interval [PI] 1615, 1977); Russia, $1933 (95% PI 1549, 2317); India, $468 (95% PI 400.4, 535); China, $1707 (95% PI 1079, 2334); South Africa, $1379 (95% PI 755, 2004). Health spending as a percentage of GDP in 2030 is projected as follows: Brazil, 8.4% (95% PI 7.5, 9.4); Russia, 5.2% (95% PI 4.5, 5.9); India, 3.5% (95% PI 2.9%, 4.1%); China, 5.9% (95% PI 4.9, 7.0); South Africa, 10.4% (95% PI 5.5, 15.3).ConclusionsAll BRICS countries show a long-term trend towards increasing their per capita spending in terms of purchasing power parity (PPP). India and Russia are highly likely to maintain stable total health spending as a percentage of GDP until 2030. China, as a major driver of global economic growth, will be able to significantly expand its investment in the health sector across an array of indicators. Brazil is the only large nation whose health expenditure as a percentage of GDP is about to contract substantially during the third decade of the twenty-first century. The steepest curve of increased per capita spending until 2030 seems to be attributable to India, while Russia should achieve the highest values in absolute terms. Health policy implications of long-term trends in health spending indicate the need for health technology assessment dissemination among the BRICS ministries of health and national health insurance funds. Matters of cost-effective allocation of limited resources will remain a core challenge in 2030 as well.

Joint Optimization With DNN Partitioning and Resource Allocation in Mobile Edge Computing

With the rapid development of computing power and artificial intelligence, IoT devices equipped with ubiquitous sensors are gradually installed with intelligence. People can enjoy many conveniences with intelligent devices, such as face recognition, video understanding, and motion estimation. Currently, deep neural networks are the mainstream technology in intelligent mobile applications. Inspired by DNN model partition schemes, the paradigm of edge computing could be utilized collaboratively to improve the effectiveness of intelligent task execution in IoT devices. However, due to the dynamics of the wireless network environment and the increasing number of IoT devices, a DNN partition policy without adequate consideration would pose a significant challenge to the efficiency of task inference. Moreover, the shortage and high rental cost of edge computing resources make the optimization of DNN-based task execution more difficult. To cope with those situations, we propose a joint method by a self-adaptive DNN partition with cost-effective resource allocation to facilitate collaborative computation between IoT devices and edge servers. Our proposed online algorithm can be proved to ensure the overall rental cost within an upper bound above the optimal solution while guaranteeing the latency for DNN-based task inference. To evaluate the performance of our strategy, we conduct extensive trace-driven illustrative studies and show that the proposed method can achieve sub-optimal results and outperforms other alternative methods.

Sustainability Challenge of Eastern Europe—Historical Legacy, Belt and Road Initiative, Population Aging and Migration

The historical legacy of Eastern European and the Balkans’ health systems was mutually interdependent and shaped by local socioeconomic circumstances. Three distinctive systems of risk sharing and health financing developed since the late XIX century were the Bismarck, Beveridge, and Semashko systems. Modern day healthcare systems in these countries are challenged by population aging, accelerated innovation in medical technology, growing purchasing power and rising demand for healthcare services. Supply-side changes contribute to demand-side efficiency bottlenecks in financing, driving up the costs of the already expensive medical care. All of the nations have a large share of citizens experiencing difficulty with affordability and access to medical care, particularly in rural and remote areas. Network of health technology assessment agencies have mushroomed over the past three decades. Principles of health economics theory and cost-effective resource allocation are slowly gaining ground in governing authorities’ mindset and decision-making processes. For many years to come, pharmaceuticals and medical services will remain dependent on out-of-pocket spending. Currently, accelerating and spreading 4.0 Industrial Revolution, together with the Belt and Road Initiative, are likely to substantially impact the further economic development of this vast region. Post-pandemic “green” recovery strategies adopted by many of the Eastern European governments shall also make this transition toward sustainable development more difficult and challenging, given the large dependency of all these economies on traditional carbon fuels.

Integrative Evolutionary-Based Method for Modeling and Optimizing Budget Assignment of Bridge Maintenance Priorities

Recently, the number of deteriorating bridges has drastically increased. As such, enormous amounts of resources are invested yearly to maintain the performance of bridges within acceptable levels. This entails the development of bridge management systems to manage the imbalance between the extensive needs for maintenance, repair and rehabilitation actions, and the limited available funds. In this regard, the present study introduces a three-tier platform to model and allocate limited resources in maintenance projects. The first model involves building a discrete event simulation model to mimic the bridge deck replacement process. The second encompasses structuring an efficient and straightforward surrogate machine learning model for mimicking the computationally expensive discrete event simulation model. In the second phase, a novel hybrid Elman neural-network invasive-weed optimization model is developed for predicting the time, cost, greenhouse gases, and utilization rates of resource allocation plans using a database generated from the previous model. The third constitutes the formulation of a multiobjective differential evolution optimization model subject to the utilization rates of the involved resources and their dispersion. Results manifest superiority in cost prediction accuracies, achieving a mean absolute percentage error, mean absolute error, and root-mean-squared error of 4.873%, 78.466%, and 39.515%, respectively. Additionally, the developed multiobjective optimization model significantly outperformed a set of well-performing metaheuristics, yielding a hypervolume indicator, generational distance, spacing, diversity, spread, and coverage of 81.721%, 0.029%, 0.1881%, 0.5229%, 0.9618%, and 0.4087%, respectively. The results also demonstrate the developed multiobjective optimization model accomplished an improvement in the minimization time, cost, and greenhouse gases by 71.01%, 27.87%, and 39.29%, respectively, when compared against a genetic algorithm. The developed models are automated through the hybrid programming of C#.net and MATLAB. It is expected that the developed method can enable the practitioners and transportation agencies to establish timely-efficient, cost-effective, and sustainable resource allocation plans while accommodating the efficacious utilization of resources.