Budget Allocation Model Research Articles

Development of an Optimization-Based Budget Allocation Model for Seismic Strengthening Based on Seismic Risk Assessment

This study presents a technology used for the prediction of economic losses to facilities in a given area during an earthquake, thereby enabling the efficient application of performance-based maintenance and seismic strengthening. We also propose an algorithm for the establishment of a reinforcement plan that minimizes earthquake-induced economic losses within a constrained budget. The algorithm incorporates fragility functions from prior research and utilizes an optimization technique for budget allocation, leveraging the target damage ratio concept and constrained optimization. Based on the fragility curve, the probability of occurrence for each damage state for a specific PGA value and the damage rate for each damage state are calculated. From these values, the expected damage ratio (EDR) is estimated. An optimization-based budget allocation algorithm is developed to find the elements that would result in the lowest damage rate for a limited cost. To validate the applicability of the model, we created a hypothetical city with a 30 km × 30 km area containing bridges, embankments, and buildings. The estimated pre- and post-reinforcement damage was assessed in two earthquake scenarios, allowing us to test the effectiveness of the optimization-based budget allocation model in reducing damage. These results suggest that the proposed model offers a viable strategy for efficient seismic strengthening within budgetary constraints.

Finance-based Scheduling for Cash-flow Management of Maintenance Portfolios: Multi-objective Optimization Approach

Bridge agencies are often challenged to develop maintenance programs under given budgets. Numerous studies developed budget-allocation models for maintenance programs during defined planning horizons of multiple fiscal years while totally ignoring the crucial aspect of cash flow. The payment schedules (both timing and amount) for contractors might indicate agencies’ cash needs that exceed the available budgets during certain fiscal years, which create cash flow problems. While numerous finance-based scheduling (FBS) models were developed to manage cash flow for contractors, this function was totally overlooked for portfolio owners. Thus, this research reintroduces the FBS from the perspective of portfolio owners. A FBS model is developed to schedule the activities of the portfolio projects, utilize the schedules to define the payment schedules of projects’ contractors, calculate the agencies’ cash needs during the fiscal years, and utilize the multi-objective optimization algorithms of NSGA-II, SPEA-II, and MOPSO to optimize the projects’ schedules to achieve a balance between the cash needs during the fiscal years and the available budgets. The anticipated extensions in projects’ completion represent the conflicting objectives. Finally, the optimized schedules make the contractors’ payment schedules affordable by the agencies’ budgets, which help to complete projects successfully and achieve the programs’ strategic goals.

Increasing the performance of a hospital department with budget allocation model and machine learning assisted by simulation

ABSTRACT The COVID-19 pandemic highlighted the critical need for efficient resource management in healthcare. In this study, the internal medicine outpatient clinic in a hospital is modelled by simulation method. Appropriate statistical distributions of the parameters are derived from past data. The results of a limited number of simulation runs are used as training data for machine learning techniques and an estimation model is selected among them. The estimation results are considered as input to a mathematical model which determines the optimal budget allocation for improving the system performance. Analysis considers patient waiting times and system throughput under varied parameters. A significant amount of time is saved by using machine learning to predict the simulation model outcomes, which had previously taken a total of around 7 hours reduced to 30–40 minutes. Time savings through machine learning are projected to be notably greater for more complex simulations comparing to current case.

Optimization of Online Advertising Decision Combination Based on the Attribution of Consumer Behavior Big Data

In the field of internet advertising, associated products based on various big data algorithms are widely presented on consumers’ browsing pages in order to increase their purchasing interest. With the help of computer big data processing, online advertisers can better grasp the shopping needs of consumers. This study proposes a chance constrained programming optimization model (CC-PR) for budget allocation of advertisers in a dynamic and complex consumer behavior context, based on the premise of analyzing consumers’ shopping journey from the perspective of the attribution of consumer behavior effects. It models different online behaviors generated by consumers’ interest transfer at different shopping stages. The characteristic of this method is to simulate changes in consumer psychology, overcome the difficulty of predicting multi-stage behavior of consumers, and extract a model of dynamic generalizable laws of consumer psychology. It dynamically adjusts advertising strategies based on changes in consumer tendencies at different stages. In order to verify the effectiveness of the proposed strategy, this paper introduces batch data from large shopping platforms and compares our method with the baseline method through experiments. The results show that the CC-PR method proposed in this paper has superior performance. A large amount of consumer behavior data can help advertisers make more accurate advertising decisions, and the method proposed in this article is also a good example of the application of computer big data processing in the field of online advertising.

Incorporation of poverty principles into goal programming

This paper presents a methodology for the incorporation of poverty related concepts into the goal programming paradigm. The concepts of poverty line, absolute poverty and relative poverty are discussed in the context of their potential usage in the field of Operations Research. The synergy between goal programming and poverty lines is examined. Two additional meta-goals are proposed for inclusion in a meta-goal programming framework based around absolute and relative poverty respectively. The resulting poverty incorporating meta-goal programming model is compared against other goal programming variants over a test set of school budget allocation models. The results demonstrate how the incorporation of poverty principles allows for greater modelling flexibility in the goal programming framework. This in turn allows decision makers to avoid solutions that are below threshold levels for a subset of stakeholders in either a relative or absolute sense. Appropriate conclusions and suggestions for future research are given.

Financial Optimization Budget Allocation Model Based On Big Data and Data-Driven Technology

The research on financial planning and budget management methods using big data technology is mainly based on the limitations of traditional financial planning and budget management in processing large amounts of data, real-time analysis, and prediction accuracy. By integrating big data technology, financial planning and budget management can become more dynamic and adaptable. This can enable enterprises to analyze historical data and market trends, and more accurately formulate financial budgets to make the most timely decisions. Through experimental data, it can be concluded that the use in financial planning and budget management can significantly improve the financial efficiency and decision-making quality of enterprises. It can be seen that the budget management effect of the financial planning and budget management system with the addition in the manual review process increased from 53.41% to a maximum of 128.30%, and most of the data was above 114.76%. The accuracy rate of the system's testing plan, improvement success rate, and completely infeasible judgment accuracy were all above 98.35%. It is not difficult to find that enterprises can optimize budget allocation through data-driven methods, better understand market and customer needs, and achieve revenue growth and cost savings, ultimately improving their competitiveness.

Green House and Ḥalāl Budget Allocation for Sustainable Household Finance Realization

This research aims first to describe household budget allocation and the realization of sustainable financial management in the household, both formulations of the greenhouse model and ḥalāl allocation budget in realizing sustainable finance for households in Indonesia. The research method used is a library study approach with secondary data sources. Data was obtained from BPS, OJK and observations in villages where the community allocated a budget for greenhouses and environmental health. The collected data was analyzed using Ibn Khaldūn's thoughts on Trade and Social Change. The results of this research are as follows: first, budget allocation considers the sources of income they have; households tabulate their needs and make a percentage budget allocation. In this budget allocation, not everything is used for consumption but also for savings and righteous charity activities. The realization of sustainable financial management is carried out using the greenhouse model and ḥalāl budget allocation. Greenhouses are realized by the community participating in waste banks, Sustainable Food House Areas or Climate Resilient Integrated Service Post (Postaklim). Through this, community economic empowerment occurs. Second, the greenhouse model collaborates with the ḥalāl budget allocation model so that there is no waste in household expenditure because sustainable finances in the household do not only talk about current financial existence but also financial existence in the future.

Engineering Consulting Project Budget Allocation and Cost Control Model Based on Baseline Productivity

This study developed an engineering consulting project budget allocation and control model based on baseline productivity of staff of various positions for each work item. The research model included two modules, namely the staff budget and time budget. The staff budget module determined the working hours and budgets of staff of various positions for each work item of the new project. The time budget module is to plan an implementation period and budget allocation for each work item of the new project. Four findings were inferred from the obtained results. First, the control chart method was an appropriate method for determining the baseline productivity of engineering consulting project. Second, the root mean square error (RMSE) of the staff budget module was 1.99 – 2.81 NT$/m, and the budget allocation results obtained with this module were similar to those produced by experienced project managers. Third, the RMSE of the time budget module was 2.23%, which is more accurate than are existing practical methods. Fourth, the accuracy of the budget stress index proposed by this study was 81.48%–84.00%. This index can assist project managers in determining projects, work items, and cost control benchmarks for staff working at various positions.

A Numerical Splitting and Adaptive Privacy Budget-Allocation-Based LDP Mechanism for Privacy Preservation in Blockchain-Powered IoT

Blockchain has gradually attracted widespread attention from the research community of the IoT, due to its decentralization, consistency, and other attributes. It builds a secure and robust system by generating a backup locally for each participant node to collectively maintain the network. However, this feature brings some privacy concerns since all nodes can access the chain data, users' sensitive information under risk of leakage. The local differential privacy (LDP) mechanism can be a promising way to address this issue as it implements data perturbation before uploading to the chain. While traditional LDP mechanisms cannot fit well with blockchain since the requirements of a fixed input range, large data volume, and using the same privacy budget, which are practically difficult in a decentralized environment. To overcome these problems, we propose a novel LDP mechanism to split input numerical data and implement perturbation by digital bits, which does not require a fixed input range and large data volume. In addition, we use an iteration approach to adaptively allocate the privacy budget for different perturbation procedures that minimize the total deviation of perturbed data and increase the data utility. We employ mean estimation as the statistical utility metric under the same and randomized privacy budgets to evaluate the performance of our novel LDP mechanism. The experiment results indicate that the proposed LDP mechanism performs better in different scenarios, and our adaptive privacy budget allocation model can significantly reduce the deviation of perturbation function to provide high data utility while maintaining privacy.

A budget allocation model for domestic airport network protection

In order to protect a domestic airport network from terrorist attacks, we investigate how to allocate a limited amount of budget into airports with different characteristics. We propose a minimax optimization model, where the Transportation Security Administration decides the budget allocation of each airport in the upper level, while the terrorist group chooses which flights to attack in the lower level. The objective of the terrorist group is to maximize the expected damage and the objective of the Transportation Security Administration is to minimize the corresponding maximum damage. We adopt a generalized cutting-plane algorithm to solve the proposed bilevel optimization problem. Moreover, we provide an illustrative example, and present some detailed sensitivity analysis and comparison results.

Application of budget allocation models in the management of investment processes in the context of the digital economy development

Within the study, the use of the public-private partnership mechanism in the management of investment processes in the context of digitalization was argued. The methodological basis of the study was a process approach, which allows to study multidirectional investment actions and the interdependent impact of the investment process, which determines the causal links of the development of investment entities at different levels in the collection of resources, in the conditions of the development of the digital economy. Endogenous sources include financial resources of internal and external origin. Exogenous investment resources include financial resources on loan, as well as budgetary allocations. Varieties of budget allocations include government procurement, concession, life cycle contract. Comparativecreativecharacterization of investment resource attraction models was carried out at the expense of budgetary allocations. In conclusion, they highlight the advantages of attracting investment resources at the expense of budgetary allocations, namely the public-private partnership model as a concession. Finally, it carriesout the comparative characteristic of the conditions of the implementation of investment projects in various models of attraction of investment resources at the expense of budgetary allocations.

An analytical model for budget allocation in risk prevention and risk protection

Risk is inherent and inevitable during project execution. Its occurrence often has an adverse impact on the project or even leads to project failure. In order to ensure the successful project completion, risk prevention used for reducing the risk probability and risk protection used for reducing the risk loss should be considered. Given the different effects and costs of these two strategies and the limited project budget, project managers should be able to reasonably divide the budget among these two strategies. This study proposes a budget allocation method consisting of three modules for mitigating the project risks. First, we model the relation between the effect and cost of each strategy as linear and non-linear relations. Second, two mathematical models are built and the corresponding analytical solutions are obtained. Afterwards, a three-step procedure for budget allocation is proposed. To validate the analytical results and investigate the impacts of the characteristics of the project risk and response on the optimal budget allocation, numerical experiments are conducted and managerial insights are drawn. Finally, the budget allocation model is extended with multiple risks, secondary risk and risk transfer, and validated using an empirical analysis.

Performance-Based Budget Allocation Model for Water Networks

Budget distribution over water network segments is one of the challenges that face municipalities worldwide. Most water distribution networks (WDNs) are deteriorating, and thus they require urgent rehabilitation, which costs billions of dollars. Therefore, there is a need to develop a prioritization tool for allocating the budget to the inspected components and selecting the most suitable rehabilitation actions. The present research aims to develop a water network performance–based budget allocation (WNPBA) model to allocate budgets based on the performance assessment optimally. The WNPBA model utilizes Weibull distribution to predict the performance for the components, as well as both genetic algorithm (GA) and greedy heuristics (GH) to allocate the available funds optimally. The data required for this research were collected from experts in Montreal’s water services because the developed model was applied to a subnetwork from Montreal. The model’s recommended actions matched the city water services’ actions, utilizing the AQUAMODEX tool by almost 94%. The developed WNPBA has proven to be a promising tool in allocating budget to water networks. Data from a water subnetwork in Montreal were used to demonstrate the proposed model. Therefore, the model is useful for the decision-making process through any water municipality to facilitate the budget allocation process.

Optimal advertising for a generalized Vidale–Wolfe response model

In this research, we formulate budget allocation decisions as an optimal control problem using a generalized Vidale–Wolfe model (GVW) as its advertising dynamics under a finite time horizon. One key element of our modeling work is that the proposed optimal budget allocation model (called GVW-OB) takes into account the roles of two useful indexes of the GVW model representing the advertising elasticity and the word-of-mouth (WoM) effect, respectively, in determining optimal budget. Moreover, we discuss desirable properties and provide a feasible solution to our GVW-OB model. We conduct computational experiments to assess our model’s performance and its identified properties, based on real-world datasets obtained from advertising campaigns by three e-commerce companies on Google AdWords, Facebook Ads and Baidu Ads, respectively. Experimental results show that (1) our GVW-OB strategy outperforms four baselines in terms of both payoff and ROI in either concave or S-shaped settings; (2) linear budget allocation strategies favor concave advertising responses, while nonlinear strategies support S-shaped responses; (3) a larger ad elasticity empowers higher levels of optimal budget and corresponding market share and thus achieves higher payoff and ROI, so does a larger WoM effect; and (4) as the total budget increases, the resulting payoff by the GVW-OB strategy increases monotonically, but the ROI decreases, which is consistent with the law of diminishing marginal utility. From a methodological perspective, our GVW-OB strategy provides a feasible solution for advertisers to make optimal budget allocation over time, which can be easily applied to a variety of advertising media. The identified properties and experimental findings of this research illuminate critical managerial insights for advertisers and media providers.

Public Policy for Financing the Practice of Physical Education

Purpose: Analyze government policies regarding the financing of physical education. Design/methodology/approach: This research is a descriptive study using qualitative methods. The research subjects were 32 people. The research analysis is data reduction, data presentation, and conclusion drawing. Findings: (1) the regional budget allocation policy for financing education delivery exceeds the minimum limit of 20% of the regional revenue and expenditure budget. (2) the operational implementation of physical education in educational units only relies on school operational assistance funds. (3) the education budget allocation is not proportional to the funding of programs to increase the competence of human resources, only 0.3% is budgeted by the Education Office. The funding for physical education providers is more dominant in the program to improve sports facilities and infrastructure, namely 8% of the budget for education providers. Originality/value: (1) The education financing policy model needs budget allocation management to increase human resources (internal) to strengthen the competence of technical personnel in various fields in the Education Office in the form of training. (2) Model of budget allocation for human resource development (external), the budget allocated for teacher and student activities in the form of technical guidance and training. The realization of this policy model requires policy intervention in the form of political will with an approach of communication, authority, trust, value.

A weighted multi-output neural network model for the prediction of rigid pavement deterioration

ABSTRACT A novel weighted multi-output neural network (NN) model is proposed for predicting the deterioration of rigid pavements based on Iowa pavement management system data. This first-of-a-kind model simultaneously predicts four pavement condition metrics concerning rigid pavements, including IRI, faulting, longitudinal crack and transverse crack. It provides an opportunity to efficiently evaluate pavement conditions and to make treatment decisions based on multi-condition metrics, such as the pavement condition index (PCI) for budget allocation models. Compared to traditional single-output NN models, this multi-output model is capable of incorporating correlations among different condition metrics. During model training, each condition metric is assigned a weight to reflect its relative importance. When the weights equal to those in the formula for the multi-condition metric, the prediction performance for PCI is optimal (13% lower MSE than optimal, single-output models). The multi-output model improves on the prediction performance for three of the four individual condition metrics compared to optimal single-output models. Results show that the consideration of correlations could improve the prediction performance for single and multi-condition metrics. Finally, variable weighting is critical for achieving the optimal balance of prediction performance among the various metrics as dictated by the needs of the decisionmaker.

The Fleet Budget Allocation Model for Transportation Management

The Fleet Budget Allocation Model for Transportation Management

Two-Stage Optimization Model for Life Cycle Maintenance Scheduling of Bridge Infrastructure

As bridge infrastructure ages, the deterioration of materials and hazard events reduce the service quality and compromise the safety of the structure. Therefore, there is a tremendous need for bridge maintenance planning, and such maintenance studies during recent years have focused on the life cycle aspect. To fulfill the budget requirements of life cycle maintenance, an important issue is to ensure that the limited maintenance budget is utilized in an effective way. However, there are few studies that have aimed to assess the topic of budget allocation and the adjustment of bridge life-cycle maintenance issues. In order to resolve such issues, a two-stage optimization model based on constraint programming (CP) is proposed in this study to deal with maintenance scheduling problems. This is facilitated by adopting the resource-constrained project scheduling problem (RCPSP) framework, in which, three plans according to the maintenance time point are considered (i.e., early, middle, and late plans). According to the RCPSP concepts, this study views the budget ceiling as the resource limit, and maintenance plans as activities, so that the feasibility of each maintenance plan depends on the sufficiency of the budget. As the first stage, Model-I (the life cycle lifespan evaluation model) takes a life cycle perspective, evaluating how long it will take to keep all bridges in a serviceable condition with minimum expenditure over the planning cycle, and evaluates the annual budgets that can be used as a reference for users to draft a budget plan. Based on the planning result from Model-I and the actual annual budget approved for the current year, the second stage, Model-II (the annual budget allocation model) then reallocates the actual budget to take into account the importance of all bridges and different costs and benefits of maintenance plans, and revises the suggested annual budget values obtained by Model-I for the following years. Through a case study, the optimized result demonstrates that annual recursive implementation of this two-stage model satisfies the need to adjust existing budgetary data, and provides management personnel with optimized and realistic maintenance decision support for bridge infrastructure.

Fuzzy Mathematical Models for Maximizing Contractors’ and Clients’ Satisfaction by Considering Flexible Start Date of the Project

Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) is considered a significant topic in project management studies and many kinds of research have been carried out in this field which have been proposed numerous approaches. However, in most of them, the viewpoints of clients and contractors, two important stakeholders of the project are not directly considered. The current research tries to introduce a new approach of RCMPSP in order to schedule the project portfolio and allocate the budget as a limited resource simultaneously. In this way, first, the client's and contractors' budget satisfaction is defined. Then some budget allocation models have been proposed to maximize the clients' and the contractors’ satisfaction. These models consider constraints such as the minimum cost required for each project, the maximum budget for each period, and the flexibility for the start date of each project. To illustrate the proposed models, a real case of the project portfolio is considered.

Optimized planning of repair works for pipelines in water distribution networks using genetic algorithm

AbstractOne of the main reasons for pipeline breakage and leakage is aging. This results in a decline in system efficiency, and associated levels of service, which in turn may endanger public health and safety. Since the most expensive and significant part of a water supply system is the distribution network, repair strategies are necessary to protect the value of the assets when a pipeline reaches the end of its useful life. Usually, repair works are taken as a reaction to the detection of a leak, pressure improvement and other factors that eventually results in an inefficient management of allocated funds. Therefore, a careful computational analysis should be performed to efficiently utilize the allocated budge. This paper presents a near‐optimized budget allocation model that is able to find the near‐optimal scheduling plan for renewal and/or replacement. The aim of this paper is to suggest a new model in order to minimize the number of breaks over a given planning horizon and the overall cost of repair of water distribution networks (WDNs), including indirect damage cost, direct damage cost, and failure repair cost while fulfilling functional requirements. The model will identify the pipe segment that needs replacement, time of replacement, and the necessary interventions that should be carried out for the network. It also identifies those pipelines that need repair. The solution is usually constrained by the yearly limited budget. The optimization procedure results in a solution alternative that decision‐makers could use for their operational requirements. The outcome of the developed model is validated based on the available leakage and breakage data from the city of Montreal database and an existing validated model. The model forecast that, in the next 20 years, 19.7% of the network will need open trench repair, 25.3% could be repaired using trenchless techniques, while 50.9% of the pipelines will remain intact.