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Both natural turfgrass and synthetic turf fields have distinct advantages and disadvantages and present unique challenges. The challenges evolve over time because of climate change, players' ever-changing needs, and the development of technologies. It is imperative to identify these challenges and devise effective solutions to overcome them. We conducted a survey of 97 administrators and managers from various organizations in the United States who were responsible for managing community sports fields. Our findings identified budget constraints as the biggest challenge for natural turfgrass field management, followed by issues related to use/scheduling and weather/climate. For synthetic turf field management, the top three challenges included budget constraints, use/scheduling, and other challenges (mainly safety issues). Additionally, administrators and managers consistently indicated increased funding as a solution for addressing challenges of both natural turfgrass and synthetic turf field management. We discuss the implications of these findings and provide potential ways to address these challenges. [ABSTRACT FROM AUTHOR]

Abstract Bridges serve as critical links in road networks, requiring continuous maintenance to ensure proper functionality throughout their lifespan. Given their pivotal role in the urban landscape, connecting various parts of a city, this research presents a multi-objective optimization model for the maintenance and repair of bridges in Babolsar, Iran. The model takes into account budget constraints and aims to minimize the total life cycle and user costs, encompassing traffic delays and vehicle expenses, while maximizing the reliability of the bridge network. Recognizing the inherent complexity of this problem, a multi-objective particle swarm optimization algorithm has been developed for an accurate solution. The study further conducts sensitivity analysis on the objective function concerning the available budget, evaluating key parameters such as hourly costs and the time value of vehicles. The results show that with an increase in the budget level, the number of repairs related to the most costly maintenance has significantly risen. In other words, as the budget expands, the model tends to favor repairs with higher costs because their impact on the bridge’s performance is more substantial.

Consumers encounter costs and inconvenience in the purchase and consumption of goods. Time, effort, training, and expertise, also referred to as access costs, are examples of factors that constrain the ability of consumers to derive utility from offerings beyond the price of a good. Economies of scope exist when household production of these factors share commonalities among choice alternatives, making it more likely that some products would be purchased and consumed in conjunction with others. For example, consumers planning out-of-town travel might consider visiting multiple tourist attractions during their trip, and course offerings might appeal more to students who are already in the mindset of taking courses. We develop a model with economies of scope that captures a leveraging effect where the purchase of one good makes the purchase of other goods more likely. Our model offers an alternative explanation of complementary purchases that are driven by cost reductions rather than utility enhancements. The model is applied to four conjoint datasets where we find that economies of scope and access costs play an important role in consumer purchases and have high economic value relative to the role played by price. This paper was accepted by Eric Anderson, marketing. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4841. [ABSTRACT FROM AUTHOR]

Modern requirements necessitate the establishment of sustainable transportation systems, considering the substantial growth in transportation activities over recent years, which is expected to continue. Companies are facing the challenge of modeling their system transport to align with green principles. Sustainable transport relied on involving diverse stakeholders, particularly scientific research, in the development of this field. In light of this, maintaining sustainable transport quality involves conducting thorough investigations into an innovative study focusing on an uncertain interval programming model for a multi-stage, multi-objective, multi-product transportation challenge within budget constraints and safety measures in a green supply chain. Human languages often contain imperfect or unknown information, inherently lacking certainty; achieving precision in describing existing states or future outcomes is frequently unattainable. In probability theory, sufficient historical information is crucial for estimating probability distributions; while in fuzzy theory, determining a reliable membership function proves challenging; hence, there is often a hesitant estimation of the degree of belief in the occurrence of each condition. Addressing such uncertainties, the theory of uncertain intervals proves highly valuable. Given these considerations, the elements of the specified problem are recognized as uncertain intervals. To manage this lack of assurance, a fusion of interval theory and methods from uncertain programming is used to formulate two distinct models: an expected value model and a chance-constrained model. The equivalent deterministic models are then formulated and solved utilizing Weighted Sum Method, fuzzy programming, and goal programming. Following this, a numerical example is utilized to assess the model's performance, and the results obtained are compared. Finally, the document concludes with a sensitivity analysis and outlines future directions. [ABSTRACT FROM AUTHOR]

To investigate the effects of various government subsidies on the decision of agricultural products supply chain under adverse weather, this paper depicts the random profit function of adverse weather affecting the output of fresh agricultural products. Then, both the centralised and decentralised decision-making models are set with various government subsidies, i.e. no subsidy, output subsidy, procurement subsidy, one-time subsidy to company and one-time subsidy to farmer. Through comparative analysis and numerical analysis, we explore the effects of adverse weather and different government subsidies on the supply chain decisions including planting effort, procurement price and retail price. [ABSTRACT FROM AUTHOR]

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A new product's introduction to the market is greatly influenced by effective and enough advertising. In this paper, we have considered a problem in which the firm partitions the market into various segments to reduce costs associated with advertising and targets to maximize the total profit. By integrating single channel and differentiated advertising to segmented market, we extend Nerlove-goodwill Arrow's dynamic model in which advertising variables are control variables. It is assumed that the whole available budget is imprecise and fuzzy in nature in order to create a realistic model. Using necessity and possibility constraints, the optimal control model with fuzzy parameters is transformed into crisp form, and the Pontryagin Maximum principle is then used to solve the problem. Numerical examples are provided to support the theoretical analysis.

In this paper, we study a two-product, multi-period stationary newsvendor problem with budget constraint. In the problem, the inventory of each product is not allowed to be carried between one period and another. At the beginning of each period, the newsvendor has to determine the order quantities of the two products with budget constraint. We apply the weak aggregating algorithm (WAA) to study this problem. WAA, developed in the field of computer science, is a competitive online prediction method of combining expert opinions. By regarding each fixed stock level as an expert opinion and updating its weight according to the cumulative loss it achieves, we first provide an online ordering strategy to decide each period's order quantity. Furthermore, we theoretically prove that this online ordering strategy possess competitive property, i.e., the cumulative loss it achieves is as small as that achieved by the optimal expert. The salvage value and stock-out cost are further considered to obtain extended results. Finally, the numerical analysis illustrates that the online ordering strategies proposed in this paper are competitive whether with or without salvage value and stock-out cost. [ABSTRACT FROM AUTHOR]

Ad targeting technology has enabled a highly personalized delivery of online ads. Behind this development is the belief that better targeting will lead to more relevant ads. This paper challenges this lay belief by showing that irrelevant advertising can arise not necessarily from technological imperfection but also from the incentive problem embedded in the ad agency-advertisers relationship. We first demonstrate that the ad agency serving multiple advertisers may strategically allocate an ad impression to a lesser-matched, sometimes totally irrelevant, niche advertiser because future impressions can match better with the mainstream advertiser. We further find that, without a contractual obligation to serve both advertisers, the agency may not deliver completely irrelevant ads to consumers. However, another type of inefficiency can arise where the agency may not send any ad to potentially interested consumers who have a strictly positive match probability with advertisers. These inefficiencies arise due to contractual restrictions, either contractual obligations or budget constraints, when the agency serves multiple advertisers. As such, we endogenize the advertisers' contractual requirement choices and show how the contractual obligation(s) can arise in equilibrium. Finally, we show that irrelevant ads will not disappear simply because more impressions are available in the market. Our analysis suggests that as the number of impressions increases, the irrelevant ads can persist, but the probability of receiving irrelevant ads decreases. This paper was accepted by Dmitri Kuksov, marketing. Funding: W. Shin gratefully acknowledges financial support from the Brian R. Gamache Endowed Professorship at the University of Florida. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4605. [ABSTRACT FROM AUTHOR]

A new product's introduction to the market is greatly influenced by effective and enough advertising. In this paper, we have considered a problem in which the firm partitions the market into various segments to reduce costs associated with advertising and targets to maximize the total profit. By integrating single channel and differentiated advertising to segmented market, we extend Nerlove-goodwill Arrow's dynamic model in which advertising variables are control variables. It is assumed that the whole available budget is imprecise and fuzzy in nature in order to create a realistic model. Using necessity and possibility constraints, the optimal control model with fuzzy parameters is transformed into crisp form, and the Pontryagin Maximum principle is then used to solve the problem. Numerical examples are provided to support the theoretical analysis. [ABSTRACT FROM AUTHOR]

Nowadays, business connection between firms becomes rather common so that one firm stores not only its information asset but also some of other firms' information asset. The management of information security is vital for these resource-sharing firms. This paper constructs a game-theoretic model between two resource-sharing firms and one hacker to examine their strategic interaction when the firms face budget constraint on security investment. We consider security information sharing between the firms, which can improve their overall security effort but meantime facilitate the hacker's learning to reduce attack costs. We find that although a tight budget constraint can help save investment cost, the firms always suffer from a poorly secure environment. It shows that although security information sharing is usually encouraged, the firms may be hurt when security information sharing is excessive so that fierce cyber-attacks are induced. We finally design an optimal compensation mechanism, in which the compensation fund is shown to increase with the degree of resource sharing. [ABSTRACT FROM AUTHOR]

This paper investigates the collaborative scheduling problem of research and development (R&D) and manufacturing processes in the context of high-end equipment, and the objective is to minimize the makespan. A unique feature is the dual usage of a limited resource budget, which can increase the quantity of both researchers and assembly lines. This brings new challenges to resource allocation and related collaborative scheduling. A mixed-integer programming model is formulated and further simplified based on the network structure, and the CPLEX solver is used to obtain the optimal solutions for the simplified model with the small-scale cases. Furthermore, an improved Variable Neighborhood Search (IVNS) algorithm is developed to obtain the near-optimal solutions for small-scale and large-scale cases. The neighborhood structure operates the R&D and manufacturing parts of solution. Variable neighborhood descent with different amount and sequence of neighborhood structures is designed to enhance the effectiveness and efficiency of the proposed algorithm. Computational experiments are conducted to evaluate the performance of the proposed algorithm. The experimental results show that the proposed algorithm performs well in terms of the solution quality and running time. [ABSTRACT FROM AUTHOR]

Introduction: Access to credits to cover the costs needed to buy production inputs at the optimal level is necessary, and budget restrictions can be an effective factor in not using the optimal economic level of production inputs. Due to the importance of the poultry industry in providing a major part of the food and protein needs of the country, the aim of this study is to investigate the effect of budget restrictions on the production of meat poultry farms in Masjid Soleyman city. Materials and Methods: In this study, the indirect production function approach was used and the required information was collected by completing 43 questionnaires at the city level in 2018. Findings: The results of the study showed that 62% of poultry units had budget restrictions of 26.62%, which caused a decrease in chicken meat production by 32.12% in this city. The return to scale was equal to 1.30, which indicates an increasing return to scale for the studied units. Conclusion: Removing the budget limit of poultry farming units can be considered an effective step towards increasing production and productivity. In this regard, it is recommended that the price policies of inputs and products, as well as the import of inputs, should be given special attention in order to reduce the problems related to the budget limitations of broiler chicken production units. For example, by determining the appropriate import tariff and subsidizing the import of these inputs, the inputs can be provided to poultry farmers cheaper. [ABSTRACT FROM AUTHOR]

Healthcare decision-makers face difficult decisions regarding COVID-19 booster selection given limited budgets and the need to maximize healthcare gain. A constrained optimization (CO) model was developed to identify booster allocation strategies that minimize bed-days by varying the proportion of the eligible population receiving different boosters, stratified by age, and given limited healthcare expenditure. Three booster options were included: B1, costing US $1 per dose, B2, costing US $2, and no booster (NB), costing US $0. B1 and B2 were assumed to be 55%/75% effective against mild/moderate COVID-19, respectively, and 90% effective against severe/critical COVID-19. Healthcare expenditure was limited to US$2.10 per person; the minimum expected expense using B1, B2, or NB for all. Brazil was the base-case country. The model demonstrated that B1 for those aged <70 years and B2 for those ≥70 years were optimal for minimizing bed-days. Compared with NB, bed-days were reduced by 75%, hospital admissions by 68%, and intensive care unit admissions by 90%. Total costs were reduced by 60% with medical resource use reduced by 81%. This illustrates that the CO model can be used by healthcare decision-makers to implement vaccine booster allocation strategies that provide the best healthcare outcomes in a broad range of contexts. [ABSTRACT FROM AUTHOR]

This paper proposes a new concept in transportation problem in which if the negligible amount quantity is transported through a distribution center, then the decision maker (DM) cannot deliver that negligible amount quantity through the particular distribution center and the DM puts some restriction on transportation problem. Here, we develop six transportation models with budget at each destination, of which three models are without restriction and another three models are with restriction. Also, apart from source, demand and capacity constraints, an extra constraint on the total budget at each destination is also imposed. Here, all the parameters in Models 1 and 2 are crisp in nature and are solved in crisp environment, whereas all the parameters in Models 3–6 are interval type-2 fuzzy and random in nature, respectively, and are solved in uncertain environment. To reduce Models 3–6 into its crisp equivalent, we use the expected value of fuzzy number and chance constraint programming technique, respectively. Weighted sum method is also used to give the preference of the objective function and a gradient-based optimization technique-generalized reduced gradient (GRG) method are applied and using LINGO-13 software to get the optimal solutions. A numerical example is provided to illustrate the models and programming. Finally, a sensitivity analysis is presented for Models 1 and 2 with respect to the weight function. [ABSTRACT FROM AUTHOR]

The spread of the COVID-19 pandemic has caused severe damage to the Chinese cruise market since 2020. It is crucial for the local government to reformulate the subsidy policy to respond to the changing environment. We propose a cruise supply chain system to investigate the choice of subsidy recipients and the setting of optimal subsidy levels with a budget-constrained government during the access restriction period and post-epidemic period. We find that in both periods, as long as the subsidy achieves the optimal level, either the cruise lines, the travel agency, or the passengers as recipients of the subsidy policy can maximize the market demand and recover the cruise market after the COVID-19 outbreak. However, as the budget increases, subsidizing passengers can improve the "low price dilemma" of the Chinese cruise market. Compared with the access restriction period, the local government should adjust the subsidy level in the post-epidemic period. Interestingly, the subsidy policy does not always positively impact the international cruise line's profit in the post-epidemic period. • Mark off two periods of recovery for the cruise market under the impact of the COVID-19. • Government budget restrictions and competition between cruise lines are considered. • The optimal subsidy policies under the two periods are derived. • Supply chain performances under the two periods are compared. • The effects of the subsidy policies are examined. [ABSTRACT FROM AUTHOR]

Budget-constraint admissible output consensus tracking design and analysis for the intermittent-interaction singular multiagent networks with given cost budgets are addressed in this paper. Two critical characteristics of many practical multiagent networks are focused. The first one is that the dynamics of each agent contains dynamical modes, as well as impulsive modes and static modes, and interactions among agents are intermittent. The second one is that the network is constrained by the cost budget, and the weighted design between tracking performances and energy consumptions should be guaranteed under budget constraints. Firstly, in virtue of the decomposition of observability, a new singular consensus tracking protocol with the dynamic output feedback is proposed by intermittent protocol states. Meanwhile, a cost index is constructed by considering both the tracking performance and the energy consumption. Then, criteria of budget-constraint admissible output consensus tracking analysis and design are given. By designing the gain matrices of consensus tracking protocols, not only multiagent networks with dynamical modes and static modes reach output consensus tracking while eliminating impulsive modes, but also the upper bound of the cost index can be kept within the cost budget; that is, the weighted design with the budget constraint is achieved. Finally, theoretical results are demonstrated by numerical simulations. • With the cost budget given previously, budget-constraint output consensus tracking can be achieved. • The dynamics of each agent is singular, which includes impulsive modes, static modes and dynamical modes. • The interaction among agents is intermittent. [ABSTRACT FROM AUTHOR]

Motivated by the practical applications in solving plenty of important combinatorial optimization problems, this paper investigates the Budgeted k-Submodular Maximization problem defined as follows: Given a finite set V, a budget B and a k-submodular function f : (k + 1) V ↦ R + , the problem asks to find a solution s = (S 1 , S 2 , ... , S k) ∈ (k + 1) V , in which an element e ∈ V has a cost c i (e) when added into the i-th set S i , with the total cost of s that does not exceed B so that f (s) is maximized. To address this problem, we propose two single pass streaming algorithms with approximation guarantees: one for the case that an element e has only one cost value when added to all i-th sets and one for the general case with different values of c i (e) . We further investigate the performance of our algorithms in two applications of the problem, Influence Maximization with k topics and sensor placement of k types of measures. The experiment results indicate that our algorithms can return competitive results but require fewer the number of queries and running time than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Recently, taking more than one means of transportation to work or to deliver goods has become common for citizens in big cities or logistics companies. A transport system that contains various transport means is called a multimodal transport system. In each route, there is carrier: airline, railway or bus company who has contracted to carry passengers with a specific schedule. As each carrier may have other customers, the number of passengers that it can serve is stochastic. Hence, this study formulates a stochastic multimodal transport network (SMTN) to model a multimodal transport system. The transfer time and fee are necessary when changing the means of transportation. This study proposes a complete algorithm to evaluate the reliability of the SMTN that is the probability that the requested number of passengers can be sent successfully from the origin to the destination under time and budget constraints. Since time and budget are the passengers considered most before traveling, the reliability in this study evaluates the SMTN regarding meeting travel demand and passengers' requirements simultaneously. A case study of a travel agent is presented to demonstrate the solution procedure. [ABSTRACT FROM AUTHOR]

For the most part, vendor adopts quantity discount to the buyer to motivate retailers to make bigger orders. The subject of this paper is the problem of observing ideal recharging strategy for a stock, dependent upon quantity discount and deterioration. It is shown that considering restriction truly influences the course of action of the issue. At long last, mathematical model is displayed to show the ordinary use of the proposed model. [ABSTRACT FROM AUTHOR]

Determination of regional deteriorating bridges’ maintenance strategies for minimizing life-cycle risks and costs constructs a complex optimization problem. Improper maintenance strategies lead to budget waste and ineffective maintenances. To optimize the life-cycle maintenance strategies of regional bridges, this study develops a deep reinforcement learning (DRL)-based framework to enable the agent to learn better maintenance actions in an interactive environment by trial and error. It is able to generate several optimal maintenance strategies to match with different budget constraints under the premise of increasing the maintenance cost-effectiveness to the greatest extent possible. The framework contains the whole optimization process from data collection to regional functions establishment to reinforcement learning training. The regional structural deterioration features and the effect of maintenance actions are determined by years of regional inspection reports. The regional probabilistic models are incorporated to simulate the stochastic process. The regional life-cycle maintenance strategies are optimized with the deep Q-networks model. The proposed framework is substantiated by developing 100-year maintenance strategies for part of highway bridges. The results show that the trained optimal maintenance strategies match well with the given budget constraints and maximize the life-cycle cost-effectiveness of maintenance actions. By employing the trained maintenance strategies, the conditions of regional bridges are controlled at a good level. Besides, the trained optimal DRL-based strategies have better performance than traditional condition-fixed strategies. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of the moderate growth of government borrowing on debt sustainability in 11 Russian regions over about 10 years, starting in 2010. The current study aims to assess the debt sustainability of the Russian region's budget by determining Euclidean distance budget constraints and cluster analysis. This study is based on the methodology of hierarchical cluster analysis, which makes it possible to isolate regions of accumulation of objects from the aggregate data and combine them into homogeneous segments. The central hypothesis of this study is that by using this method, it is possible to increase the accuracy of the values that limit budget constraints in a region's financial system. This study, using open data from the Federal State Statistics Service, is based on a database of statistical, financial, and economic indicators of the Russian economy. The calculations include about 45 macroeconomic indicators, which reflect the ratios of socio-economic development of the region's financial system. The methodology described in the paper for assessing the debt sustainability of budget policy proves the need to calculate six indicators and determine the debt limits for the regions of each cluster. It finds a need to reduce the high debt burden of 46% of the regions belonging to the Northwestern Federal District. Confidence intervals for the debt limit suggest that the negative growth effect of high debt may start from levels of around 5% of the debt-to-GDP ratio and about 43% of the debt-to-revenue ratio. The results indicate that regions with a high level of debt sustainability include St. Petersburg city, the Leningrad region, and the Kaliningrad region. From a state debt policy perspective, the results provide additional arguments for debt reduction for the Republic of Komi, the Republic of Karelia, the Arkhangelsk region, and the Pskov region. The general conclusion of the study boils down to the need to reduce the debt burden of the budgets of some regions of the SFZO, as well as to the need to change the upper limits of debt, which are equally set for all regions by the Budget Code of the Russian Federation, to differentiated values of public domestic debt, taking into account the results obtained in the study. [ABSTRACT FROM AUTHOR]

The resource provisioning and workflow execution in a multi-cloud environment using a pay-as-you-use framework have recently gained the attention of the cloud computing research community. Scheduling of workflows in the multi-cloud platform is challenging due to the cloud dynamics, particularly, heterogeneous resource types, multiple billing mechanisms, elasticity, on-demand provisioning, and systems reliability. In addition, these workflow applications have a runtime constraint—the most typical being the execution time and the execution cost. Another vital Quality of Service (QoS) metric that is of critical concern is reliability. This paper proposes a Normalization based Reliable Budget constraint Workflow Scheduling (NRBWS) algorithm to improve the workflow execution reliability and reduce the makespan under the budget constraint specified by the user. This scheme undergoes a min–max normalization process that is trailed by the computation of the expect reasonable budget ( erb ) to assign the tasks to one of the computational resources. The NRBWS algorithm lowers the makespan by assigning each workflow task to the most reliable computing resource with the earliest finish time under the allocated budget. Simulation results demonstrate that the proposed NRBWS algorithm outperforms existing state-of-the-art heuristics. [ABSTRACT FROM AUTHOR]

In the tourism sector, travel agent's managers often set meeting customers' demand as a top priority and often get discounts from airlines and the discounted fare on each flight depends on their booking, whereas, customers have their request in terms of travel time and cost. This study addresses the network reliability, the probability that the given demand can travel from an origin to a destination within the time threshold and the travel cost after discount does not exceed the budget, to indicate travel agents' ability on satisfying customers' demand under specific constraints. To calculate the network reliability, this work develops an algorithm, which determines all lower boundary points via the minimal concept. An air transportation system of the travel agent is constructed as a flow-network consisting of sets of nodes (airports) and arcs (flights). On each flight, there exists a contracted airline providing the service with stochastic capacity and corresponding probability. The flow-network in this study is typical stochastic, called a stochastic-flow air transportation network. A real case for the proposed algorithm demonstration and some management suggestions of the network reliability are provided. [ABSTRACT FROM AUTHOR]

The economic production quantity method is one of the simplest models to control production and inventory. However, the assumptions on which it is based make its application difficult in practical situations. In most companies, set-up and inventory costs are unknown, or may not even exist as such, although other data are known, such as available capacity in machining hours and limits set by the financing subsystem. The present work offers a multi-item inventory model that sets reference values using known values such as inventory investment limits and production capacity. In order to confer the proposed model’s larger practical content, a spreadsheet available to be downloaded has been implemented. Finally, the model dynamics is illustrated as a numerical example obtained from a real company. [ABSTRACT FROM PUBLISHER]

In this paper, we develop solutions to the problem of identifying the k most vital cut-sets of a network under limited attacking resources in a network interdiction context. We treat three different versions of the problem, each having a different objective: namely, the highest probability of a successful attack, the least cost attack, and the least expected cost attack combining both success probability and attacking cost. We introduce a budget constraint to reflect the resource limitation of the attacker. We consider both the case of disjoint and non-disjoint cut-sets to target. We develop a simulation-optimization approach accounting for various techniques including the shortest path problem and the min-cut problem. The solution method is found to be very efficient even for large-scale networks. We implement the suggested algorithms for illustration purposes.

Healthcare decision-makers face difficult decisions regarding COVID-19 booster selection given limited budgets and the need to maximize healthcare gain. A constrained optimization (CO) model was developed to identify booster allocation strategies that minimize bed-days by varying the proportion of the eligible population receiving different boosters, stratified by age, and given limited healthcare expenditure. Three booster options were included: B1, costing US $1 per dose, B2, costing US $2, and no booster (NB), costing US $0. B1 and B2 were assumed to be 55%/75% effective against mild/moderate COVID-19, respectively, and 90% effective against severe/critical COVID-19. Healthcare expenditure was limited to US$2.10 per person; the minimum expected expense using B1, B2, or NB for all. Brazil was the base-case country. The model demonstrated that B1 for those aged 2 for those ≥70 years were optimal for minimizing bed-days. Compared with NB, bed-days were reduced by 75%, hospital admissions by 68%, and intensive care unit admissions by 90%. Total costs were reduced by 60% with medical resource use reduced by 81%. This illustrates that the CO model can be used by healthcare decision-makers to implement vaccine booster allocation strategies that provide the best healthcare outcomes in a broad range of contexts.

Budget constraints become an important consideration in modern predictive modeling due to the high cost of collecting certain predictors. This motivates us to develop cost-constrained predictive modeling methods. In this article, we study a new high-dimensional cost-constrained linear regression problem, that is, we aim to find the cost-constrained regression model with the smallest expected prediction error among all models satisfying a budget constraint. The nonconvex budget constraint makes this problem NP-hard. In order to estimate the regression coefficient vector of the cost-constrained regression model, we propose a new discrete first-order continuous optimization method. In particular, our method delivers a series of estimates of the regression coefficient vector by solving a sequence of 0-1 knapsack problems. Theoretically, we prove that the series of the estimates generated by our iterative algorithm converge to a first-order stationary point, which can be a globally optimal solution under some conditions. Furthermore, we study some extensions of our method that can be used for general statistical learning problems and problems with groups of variables. Numerical studies using simulated datasets and a real dataset from a diabetes study indicate that our proposed method can solve problems of fairly high dimensions with promising performance. [ABSTRACT FROM AUTHOR]

In mobile crowdsensing, it is a challenge to assign tasks to appropriate smartphones. Existing task allocation mechanisms mainly aim at optimizing the global system performance, while ignoring the personal preferences of individual crowdsensing tasks and smartphone users. Nevertheless, in an open crowdsensing system, a task assignment is prone to be unstable if smartphone users or tasks have incentives to deviate from the global assignment, and seek for alternative choices to improve their own utilities. Besides that, during task competition, the rational smartphone users might choose to adjust their payments after the first few failures, which however, brings new challenges in achieving the stability. To address these issues, this paper constructs a distributed many-to-many matching model to capture the interaction between crowdsensing tasks and smartphone users, taking into account the budget constraints of tasks. Then, we design a stable matching algorithm to allocate the tasks to the users, and determine their payments. We prove that the proposed algorithm achieves several desirable properties including individual rationality, stability, and convergency. It is also proved that the proposed scheme achieves at least half of the optimal system efficiency when each smartphone provides homogeneous service quality. Finally, simulation results confirm the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Marketing managers allocate extraordinary amounts of budgets for supporting their organization's marketing plans. Choosing the right measure for allocation of promotional budgets to customers has always been a challenging issue and consequently has drawn considerable attention in the past few years. Related studies emphasize using Customer Lifetime Value or Customer Equity (CE) as a measure of customer's profitability, based on which a firm can efficiently allocate its marketing budget to customers. In this study, the focus is on allocating the limited promotion budgets to customers to maximize CE. Using Markov decision process (MDP), the firm's actions are linked to the customers' behavior and the corresponding values. To solve MDP with a budget constraint, a heuristic method is proposed for finding the optimal or near-optimal policies for each customer segment. To confirm the validity of the method, the decision model is implemented in a business-to-business context to maximize CE by determining the optimal sales promotions to customers. The result of the proposed heuristic method is compared with a stochastic dynamic programming model. The result of experiments shows that the discount factor increases the difference between the obtained solutions becomes more significant. The difference approves the importance of accounting the optimal level of promotional budget for each customer segment. The average improvement gained by the optimal budget allocation ranges from 2 to 5.5%. The findings of this research indicate the essential of adopting different marketing policies for each customer segment. [ABSTRACT FROM AUTHOR]

Infrastructure as a service model of cloud computing provides a tremendous amount of high-performance computing systems for the execution of scientific workflow applications. However, due to explosive growth in energy consumption and high charge cost of using these cloud systems, energy-efficient workflow scheduling under budget constraints becomes the most challenging issue. Very few research works have been done that consider the stated issue. Most of them mainly focus on the minimization of schedule length under user-specified budget constraints or energy consumption constraints. In this article, we propose an energy-efficient workflow scheduling algorithm named reducing energy consumption using fair pre-assignment of available budget (RECFPAB) that reduces energy consumption under client-specified budget constraints. The RECFPAB introduces a flexible mechanism to save energy consumption with the inclusion of energy and cost coefficient factor that enables fair distribution of available budget for unscheduled tasks of the workflow application. In order to compare the performance of the proposed algorithm, an energy-efficient version of the popular existing algorithms such as heterogeneous budget constrained scheduling and minimizing schedule length using budget level are introduced. The experimental evaluation based on Genome, LIGO, and Montage applications shows that RECFPAB gives significant results in comparison with considered algorithms. [ABSTRACT FROM AUTHOR]

Software reliability growth is observed by investing not only the testing-time but also the testing-effort in the testing-phase of software development process. If the testing-time (testing-effort) is reduced to some extent, it is possible to observe the software reliability growth by investing the amount of testing-effort (testing-time) which can compensate the insufficiency of the testing-time (testing-effort). However, most of the existing software reliability growth models (SRGMs) are constructed as univariate models and the substitutability between the testing-time and testing-effort is not considered. Additionally, it is necessary to remove many faults efficiently within the budget. In this paper, we develop bivariate Weibull type SRGMs under budget constraint based on the Cobb-Douglas type and CES (constant elasticity of substitution) type testing-time functions. Simultaneously, we evaluate the substitutability between the testing-time and testing-effort factors which are software reliability growth factors. Finally, we conduct the sensitivity analysis and show numerical examples by using actual data sets

Abstract Background With modern methods in biotechnology, the search for biomarkers has advanced to a challenging statistical task exploring high dimensional data sets. Feature selection is a widely researched preprocessing step to handle huge numbers of biomarker candidates and has special importance for the analysis of biomedical data. Such data sets often include many input features not related to the diagnostic or therapeutic target variable. A less researched, but also relevant aspect for medical applications are costs of different biomarker candidates. These costs are often financial costs, but can also refer to other aspects, for example the decision between a painful biopsy marker and a simple urine test. In this paper, we propose extensions to two feature selection methods to control the total amount of such costs: greedy forward selection and genetic algorithms. In comprehensive simulation studies of binary classification tasks, we compare the predictive performance, the run-time and the detection rate of relevant features for the new proposed methods and five baseline alternatives to handle budget constraints. Results In simulations with a predefined budget constraint, our proposed methods outperform the baseline alternatives, with just minor differences between them. Only in the scenario without an actual budget constraint, our adapted greedy forward selection approach showed a clear drop in performance compared to the other methods. However, introducing a hyperparameter to adapt the benefit-cost trade-off in this method could overcome this weakness. Conclusions In feature cost scenarios, where a total budget has to be met, common feature selection algorithms are often not suitable to identify well performing subsets for a modelling task. Adaptations of these algorithms such as the ones proposed in this paper can help to tackle this problem.