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Sequential incentive marketing is an important approach for online businesses to acquire customers, increase loyalty and boost sales. How to effectively allocate the incentives so as to maximize the return (e.g., business objectives) under the budget constraint, however, is less studied in the literature. This problem is technically challenging due to the facts that 1) the allocation strategy has to be learned using historically logged data, which is counterfactual in nature, and 2) both the optimality and feasibility (i.e., that cost cannot exceed budget) needs to be assessed before being deployed to online systems. In this paper, we formulate the problem as a constrained Markov decision process (CMDP). To solve the CMDP problem with logged counterfactual data, we propose an efficient learning algorithm which combines bisection search and model-based planning. First, the CMDP is converted into its dual using Lagrangian relaxation, which is proved to be monotonic with respect to the dual variable. Furthermore, we show that the dual problem can be solved by policy learning, with the optimal dual variable being found efficiently via bisection search (i.e., by taking advantage of the monotonicity). Lastly, we show that model-based planing can be used to effectively accelerate the joint optimization process without retraining the policy for every dual variable. Empirical results on synthetic and real marketing datasets confirm the effectiveness of our methods., Comment: Published at CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

In the new competitive world, companies several types of tools and strategies are used to differentiate their products with competitors' product, one of which is promotional. Companies spend a large amount of their promotional budget on advertising. For increase the effectiveness of advertising budgeting, media planning must be properly developed and determine the manner allocation advertising over a company's programming horizon. In this paper, investigates advertising media planning and budgeting for several products. Important aspects including life cycle stage, BCG matrix class, price, competitors’ reaction and budget constraint is considered in our model given uncertainty and with the aim of maximizing profits at the end of the time horizon. We formulate this problem as a stochastic dynamic program and utilized approximate dynamic programming (ADP) algorithm to overcome the huge dimensionality and considerable uncertainties existed in our problem. approximate Dynamic Planning (ADP) is a powerful technique for solving discrete time problems under multistage stochastic control processes. A numerical example was carried out on two products over the course of one year (12 monthly periods) with five different advertising packages. The results showed that 5 million iterations would be suitable to converging. Remaining budget analysis shows the percentage of selective offensive packages in higher budgets for product 2 and the more often selection of such packages in midterms for product 1. The process of the life cycle shows that product 1 does not most likely complete its life stages, but product 2 completes its life cycle stages. Moreover, the BCG matrix confirms the results and product 2 is in the final stages of dogs, while product 1 is more likely in Cash Cows. Also, the total budget was examined in different quantities, which showed that as the amount of the budget increased, the target amount increased slowly. The presented model offers the opportunity to managers by which they are able to compare different media for making advertising decisions for various products in an uncertain environment with different budgets.

In the consensus reaching process (CRP), decision-makers (DMs) often present behaviors of tolerance and limited compromise when adjusting their opinions, which may lead to the actual decision deviating from the solution recommendation obtained by applying the current minimum cost consensus theory. In this paper, we propose a consensus-reaching approach based on maximum satisfaction under budget constraints for DMs with these two behaviors. To obtain a feasible budget constraint of the CRP, a reference budget consensus model (RBCM) incorporating both of these behaviors is first developed. Under the constraint of the limited budget, we propose a maximum satisfaction consensus model (MSCM) to maximize the satisfaction level of all DMs based on the goal programming theory. To further achieve different levels of consensus, we propose a soft MSCM under a certain threshold using soft consensus measures. Moreover, the proposed consensus models are illustrated by the data of China’s Taihu Lake pollution control. The results suggest that: First, the individual tolerance behavior has a higher impact on the consensus outcome than the compromise limit, whereas the latter is more likely to affect the satisfaction of each DM. Second, as the budget increases, the consensus opinion and group satisfaction level will increase toward a higher average cost until they remain constant. Last, a higher consensus threshold will reduce DMs’ satisfaction levels, and an appropriate threshold should be selected as the control baseline of the CRP in the real application.

In this paper, we present a new model and two mechanisms for auctions in two-sided markets of buyers and sellers, where budget constraints are imposed on buyers. Our model incorporates polymatroidal environments, and is applicable to a wide variety of models that include multiunit auctions, matching markets and reservation exchange markets. Our mechanisms are build on polymatroidal network flow model by Lawler and Martel, and enjoy various nice properties such as incentive compatibility of buyers, individual rationality, pareto optimality, strong budget balance. The first mechanism is a simple "reduce-to-recover" algorithm that reduces the market to be one-sided, applies the polyhedral clinching auction by Goel et al, and lifts the resulting allocation to the original two-sided market via polymatroidal network flow. The second mechanism is a two-sided generalization of the polyhedral clinching auction, which improves the first mechanism in terms of the fairness of revenue sharing on sellers. Both mechanisms are implemented by polymatroid algorithms. We demonstrate how our framework is applied to internet display ad auctions.

How firms use workforce size adjustment as a strategic response to external shocks such as exchange-rate fluctuations is an important but not yet fully understood topic. To address the void in the literature, we apply the strategy tripod theoretical framework, which sees the external shocks as an industrial impact, the budget constraints as an institutional force, and the firm leverage as an organisational capability to examine the firms’ strategic response in terms of workforce size adjustment in the Chinese economy context. Based on longitudinal data of Chinese manufacturing firms, our analysis finds that facing exchange rate shocks, firms with hard budget constraint are more responsive in adjusting their workforce than firms with soft budget constraint. Furthermore, highly levered firms, with hard budget constraint, will adjust their workforce more aggressively than firms with less debt in their financial structure.

The urban demographic in most developing countries often depend on fossilized fuel generators to meet the electricity supply gap from the national grid. Due to health and environmental concerns with fossilized fuel, local renewable energy resources offer a tentative replacement but often involve high initial upfront costs for their deployment. This work addresses the initial cost barrier issue with renewable energy deployment by focusing on Nigeria as a case study. More specifically, this work proposes a Solar Nano grid concept as an alternative to fossilized generators. The proposed Solar Nano grid concept presents the flexibility to deploy PV systems based on budgetary constraints and the option of upgrading the system (adding newer units to existing ones) in the future. While adding new units to old ones gives rise to compatibility and aging issues in the PV systems, simulation studies show that it is possible to eliminate such concerns by applying a set of design protocols and principles in the proposed Solar Nano grid. An assembled lab-scale Solar Nano grid system validated the simulation findings. With the validated model, this work puts forth possible strategy for reducing the upfront cost of PV system deployment to about 10% of the initial value.

Problem definition: Operational disruptions can impact a firm’s risk, which manifests in a host of operational issues, including a higher holding cost for inventory, a higher financing cost for capacity expansion, and a higher perception of the firm’s risk among its supply chain partners. Academic/practical relevance: Although disruptions have been studied extensively in the operations management literature, the emphasis has been on mitigating the deleterious impact on the firm’s production potential rather than its risk. We examine whether implementing and credibly attesting to having effective internal control systems will meaningfully influence the impact of operational disruptions on the firm’s risk and market valuation. Methodology: We exploit a 2004 regulatory change that required certain firms to attest to having control systems in place. Our triple-difference regression specification takes advantage of a regulatory anomaly that excluded a well-defined set of firms from complying with the control system requirement. Results: We find that firms that were obligated to comply with this regulatory requirement experienced a materially smaller increase in their risk and a smaller decrease in their market value in the aftermath of an operational disruption. Firms that were not obligated to comply with this requirement did not experience such benefits in the aftermath of a disruption and instead, experienced a larger increase in their risk. Managerial implications: Fostering a better understanding of whether credible control systems can reduce the impact of disruptions on the firm’s risk and value is important as it identifies a broader set of mitigation strategies available to operations managers. This can help managers achieve a better fit between their risk mitigation initiatives and their objectives and budget constraints.

This paper investigates the impact of financial liberalization on the relationship between consumption and total wealth (i.e., the sum of asset wealth and human wealth). Financial liberalization is persistent and may signal changes in expected future consumption growth rates and/or in rates of return on wealth that, through the inter-temporal budget constraint, affect the current consumption-wealth ratio. We estimate the long-run relationship between consumption, total wealth and financial liberalization by state space methods using quarterly US data. The results show that the trend in the consumption-wealth ratio is well-captured by our baseline liberalization indicator. We find that the increase in this indicator over the sample period has increased the consumption-wealth ratio with about ten to sixteen percent. Investigating the responsible channel, additional estimates show that financial liberalization has predictive power for aggregate consumption growth rather than for returns, a result that supports an incomplete markets interpretation of the link between liberalization and the consumption-wealth ratio.

In this work, the possibility of cross-border activities between two regions in the framework of the investment contract is viewed as optimal allocation problems. The problems of determining the optimal proportion of funds to be invested in liquidity and technology are analyzed in two different environments. In the first case, we consider a two-region and two-technology economy in which both regions possess the same productive technology or project, but a different stream of return. While in the second case, we examine an economy where two regions (i.e., Indonesia and Malaysia) hold different Islamic productive projects with identical returns. Allocation models are formulated in terms of investors’ expected utility maximization problem under budget constraints with respect to regional and sectoral shocks. It is revealed that optimal parameters for liquidity ratio, technological investment profile, and bank repayment are analytically characterized by the return of a more productive project and the proportion of impatient and patient investors in the region. Even though both cases employ different assumptions, they provide the same expressions of optimal parameters. The model suggests that cross-border Islamic investment activities between two regions might be realized, provided both regions hold productive projects with an identical stream of return. This paper also shows that by increasing the lower return of the project approaching the higher return, a room for inter-region investment can be created. An analytical framework of an investment contract in terms of optimal allocation model is provided.

Artificial Intelligence of Things (AIoT) is an emerging trend that integrates artificial intelligence into the Internet of Things, which enables intelligent IoT operations and smart industrial applications. AIoT can generate a large amount of data from the network edge. Due to the concerns of bandwidth and privacy, it is often impractical to move the collected data to the cloud. To address this issue, a collaborative distributed learning has been proposed to let the clients collaboratively train a machine learning model together with their local data in a distributed manner. In this paper, we study the free market incentive mechanism for collaborative distributed learning, where multiple parameter servers (PSs) compete with each other to motivate clients to contribute model training. Specifically, the Stackelberg game with multiple leaders and multiple followers has been designed to analyze the incentive mechanism. Different experiments have been performed to illustrate the efficiency of the proposed approach. In particular, compared with the state-of-the-art under the same budget constraint, the final average utility of the PSs can be increased by at least 94.3%.

Cost optimization of heating, ventilation and air conditioning systems (HVAC) in buildings has been approached by using sophisticated methodologies such as model predictive control (MPC) and real-time optimization. However, such techniques are not usually user-friendly to individuals without a process control background. Namely, it can be very difficult for building managers to estimate the economic impact of set point changes. For this reason, it is proposed an economic nonlinear model predictive control (eMPC) using a budget constraint. Using this concept, building managers will be able to balance the trade-off between operational cost and thermal comfort. To demonstrate the proposed approach, a first-principles model of a HVAC system is extended from a previous authors contribution to consider two new features: (1) thermal lag due to energy accumulation in the building envelope; (2) energy consumption by the fans. Then, the implemented HVAC system is tested in open- and closed-loop conditions. The closed-loop simulations showed the advantage for building managers of using the budget-constrained eMPC over the classical approach and On–Off control when using two different budget strategies. This approach ensures an user-friendly interface between building managers (i.e., non-expert users) and eMPC.

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.

Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are as...

With the goal of informing federal and state policy makers in a time of budget constraints, we used a systematic review methodology to identify and summarize findings from studies that examined the effects of losing grant aid due to policy changes and students’ failure to meet renewal requirements. Studies reviewed in this policy brief show negative effects on student outcomes when grant aid is reduced or eliminated. While results vary, this general conclusion applies when grant aid is reduced or eliminated from programs that differ in scope (federal and state), eligibility requirements (merit and need), and award amounts. This brief illuminates the importance of maintaining grant aid funding for college student enrollment, persistence, achievement, and completion. Especially in context of other pandemic-related stressors, reducing need-based grant aid will likely exacerbate declines in college enrollment, progression through college, and degree completion for vulnerable students.

With recent breakthroughs in technology and digitalization, omnichannel retailing has now become the norm, and shoppers are able to seamlessly make the switch between different channels for one purchase. Retailers can leverage the blurred lines between the channels and adopt a business model that best suits the industry, such as having a virtual online showcase and letting the customers pick up the products in person or having an offline showroom and having products delivered to customers. The latter concept, in which information about products is gathered in a store or a showroom and fulfillment is made via delivery to customers, is particularly suitable when the customers prefer to experience the products in person to gain sufficient confidence in their potential purchase. This is often the case for high-value large products with high shipping costs. In this context, we propose a quantitative approach to optimize the showcasing portfolio for a given retailer to maximize the exposure of the features that customers expect to experience from a visit to a showroom. The problem is formulated as a mixed-integer optimization problem to maximize the expected customer showcasing utility through module and product showcasing and product testing. To demonstrate the practicality of our approach, we conduct a case study based on real data obtained from 17 dealerships of our industrial partner, a manufacturer of recreational vehicles. The numerical results of this case study show that the expected showcasing utility for a retailer can significantly increase, even in the presence of spatial and/or budget constraints.

Given a network as well as a prescribed vertex s in which a facility is located, the aim of solving reverse 1-centre problems is to decrease edge lengths under a budget constraint in a way that the...

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.

Background. The article investigates the principles of budget financing, the principles of enterprise and corporate financing, and the principles of social entrepreneurship. The scientific approaches on the interpretation of the given principles of financing are generalized and systematized. Expediency of the separate principle’s application in the context of the municipal enterprise functioning is substantiated. The principles of the municipal utility enterprises financing are proposed with consideration to social significance of the municipal enterprises in the system of economic activity of the territorial community. Materials and methods. The historical method, analysis and synthesis, the method of deduction were used in theoretical systematization of the budget financing and enterprise financing principles. Derivation of the principles of financing utilities on the basis of scientist’s opinions systematization is performed using the inductive method. The conclusions of the study are formed by the abstract-logical method. Results. The principles of municipal utilities financing should be based on the principles of budget financing, as these enterprises are recipients of funds from local budgets.However, the utility enterprise operates on the commercial basis, the principles of its financing should take into account the principles of private sector enterprises financing. We have developed the principles of a municipal utility company financing, which are grouped into two categories – general and specific principles. Conclusion. We have proposed the principles of utilities financing with their division into universal and specific, taking into account the commercial nature of such enterprises and their social significance. And furthermore, these principles allow to justify the functions, methods and approaches to utility enterprise financing, which will be a topic for future research. Keywords: municipal utility enterprise, financing principles, budget financing, corporate financing.

Timely diagnostic testing for active SARS‐CoV‐2 viral infections is key to controlling the spread of the virus and preventing severe disease. A central public health challenge is defining test allocation strategies with limited resources. In this paper, we provide a mathematical framework for defining an optimal strategy for allocating viral diagnostic tests. The framework accounts for imperfect test results, selective testing in certain high‐risk patient populations, practical constraints in terms of budget and/or total number of available tests, and the purpose of testing. Our method is not only useful for detecting infections, but can also be used for long‐time surveillance to detect new outbreaks. In our proposed approach, tests can be allocated across population strata defined by symptom severity and other patient characteristics, allowing the test allocation plan to prioritize higher risk patient populations. We illustrate our framework using historical data from the initial wave of the COVID‐19 outbreak in New York City. We extend our proposed method to address the challenge of allocating two different types of diagnostic tests with different costs and accuracy, for example, the RT‐PCR and the rapid antigen test (RAT), under budget constraints. We show how this latter framework can be useful to reopening of college campuses where university administrators are challenged with finite resources for community surveillance. We provide a R Shiny web application allowing users to explore test allocation strategies across a variety of pandemic scenarios. This work can serve as a useful tool for guiding public health decision‐making at a community level and adapting testing plans to different stages of an epidemic. The conceptual framework has broader relevance beyond the current COVID‐19 pandemic.

We introduce a multiple linear regression model methodology for selecting best subsets of independent variables subject to active budget constraints. Computational studies on multiple data sets to ...

The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

The value of travel time savings (VTTS) representing the willingness to pay to reduce travel time, consists of two components: the value of liberating time [equal to the value of leisure (VoL)] and the value of time assigned to travel (VTAT), representing the travel conditions of a trip. Their relative values indicate which dimension to emphasize when investing in transport: speed or comfort. In this paper, we formulate and estimate a framework aimed at the improvement in the estimation of the VoL. By introducing a novel treatment of time assigned to domestic work, we consider that unpaid labor should be assigned a wage rate as a measure of the expenses avoided when assigning time to those chores. We use state-of-the-art data on time use and expenses as well as online data on gig workers collected in Austria, and apply the time-use and expenditure model of Jara-Diaz et al. (Transp Res Part B 42(10):946–957, 2008). The wage rates for paid and unpaid work were combined to re-formulate the budget constraint, which affected women more than men due to the higher involvement of the former in domestic activities. Compared against the original estimation, the VoL changed from €10/h for men and €6/h for women to €9/h for both genders, which in turn yields a larger average VTAT, which becomes positive for public transport. As a conclusion, the novel treatment of domestic labor contributes to closing the gap in the VoL between genders and highlights the power of unveiling the components behind the VTTS. The empirical findings imply that investments in travel time reductions rather than in comfort should be prioritized, given the very good conditions of public transport in Austria., Transportation, 49, ISSN:0049-4488, ISSN:1572-9435