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Resource reservation is an essential step to enable wireless data networks to support a wide range of user demands. In this paper, we consider the problem of joint resource reservation in the backhaul and Radio Access Network (RAN) based on the statistics of user demands and channel states, and also network availability. The goal is to maximize the sum of expected traffic flow rates, subject to link and access point budget constraints, while minimizing the expected outage of downlinks. The formulated problem turns out to be non-convex and difficult to solve to global optimality. We propose an efficient Block Coordinate Descent (BCD) algorithm to approximately solve the problem. The proposed BCD algorithm optimizes the link capacity reservation in the backhaul using a novel multipath routing algorithm that decomposes the problem down to link-level and parallelizes the computation across backhaul links, while the reservation of transmission resources in RAN is carried out via a novel scalable and distributed algorithm based on Block Successive Upper-bound Minimization (BSUM). We prove that the proposed BCD algorithm converges to a Karush-Kuhn-Tucker solution. Simulation results verify the efficiency and the efficacy of our BCD approach against two heuristic algorithms.

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.

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%.

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.

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.

Based on the perspective of new structural economics, this paper reviews each stage of China's state-owned enterprise reform, summarizes their main features, and argues that the root of state-owned enterprise's low efficiency and soft budget constraints is their strategic and social policy burdens. According to the state-owned enterprise's existing situation, the paper provides suggestions for deepening its reform.

Tourism transport is becoming a crucial part of the tourism industry. Determining the reliability of tourism transport networks is required by travel agencies and other practitioners. However, much of recent reliability assessments cannot utilize directly for tourism transport networks. Moreover, there exists a lack of passengers-oriented reliability evaluation that involves the impacts of latency. Therefore, this study assesses reliability regarding carrying the required number of passengers to their destination under budget and time constraints when considering late arrivals. For reliability assessment, an algorithm, which combines the concept of the minimal path adding a searching procedure with the Recursive Sum of Disjoint Products method, is proposed. An illustrative example is adopted to provide travel agencies with an intuitive visualization of the proposed algorithm. Reliability analysis suggests improving the performance of tourism transport networks.

Cloud Infrastructure as a Service (IaaS) has been known as a suitable platform for the execution of workflow applications. Quality of service (QoS) in such platforms is considered a challenging problem from both customers’ and service providers’ perspectives to perform workflow schedules. This paper proposes Budget Deadline Delicate Cloud (BDDC) and Budget Deadline Cloud (BDC) algorithms to consider both budget and deadline constraints for scheduling scientific workflows on cloud IaaS platforms. Methods for distribution of budget and deadlines under task leveling are proposed. Four metrics (success rate, time ratio, cost ratio, and utilization rate) are utilized to evaluate the proposed algorithms’ performance. Results of our proposed algorithms are compared with the BDHEFT, DBCS, and BDSD algorithms under various scenarios. Simulation results demonstrate that BDDC outperforms other algorithms in achieving cheaper costs while earning a higher success rate and utilization rate, and BDC accomplishes higher success rates and faster makespan. The performance of the proposed methods is confirmed using a real cloud environment.

Most of the complex systems consist of multiple subsystems and can be modeled as multilayer networks. These networks are prone to random or strategical attacks and end up disintegrating the entire multilayer network. These attacks can’t be avoided, but a model can be proposed to restore the network to make it secure and robust. In multilayer networks, each inter-layer link has its own cost (in terms of money) associated with it. The total cost of the inter-layer links is considered as the available budget. In the present work, a method is proposed to introduce the optimal number of inter-layer links (under budget constraints) to maintain the robustness of the multilayer network. For the simulation purpose, three variants of the artificial multilayer networks and data set EU-Air Transport Networks are considered. Simulation results reveal that for the multilayer network constructed with random network layers, approx. 65% of the available budget is utilized. Nearly 70% of the total nodes are connected to a mutually connected giant component (MCGC) via inter-layer links. However, for the Configuration model, using the almost total available budget, nearly all the nodes from the considered network layers are present in MCGC. Finally, in the case of the empirical dataset, by using approx. 75% of the available budget, almost 80% of the total nodes from the considered network layers are connected to MCGC via inter-layer links.

With the advent of Network Function Virtualization (NFV), network services that traditionally run on proprietary dedicated hardware can now be realized using Virtual Network Functions (VNFs) that are hosted on general-purpose commodity hardware. This new network paradigm offers a great flexibility to Internet service providers (ISPs) for efficiently operating their networks (collecting network statistics, enforcing management policies, etc.). However, introducing NFV requires an investment to deploy VNFs at certain network nodes (called VNF-nodes), which has to account for practical constraints such as the deployment budget and the VNF-node capacity. To that end, it is important to design a joint VNF-nodes placement and capacity allocation algorithm that can maximize the total amount of network flows that are fully processed by the VNF-nodes while respecting such practical constraints. In contrast to most prior work that often neglects either the budget constraint or the capacity constraint, we explicitly consider both of them. We prove that accounting for these constraints introduces several new challenges. Specifically, we prove that the studied problem is not only NP-hard but also non-submodular. To address these challenges, we introduce a novel relaxation method such that the objective function of the relaxed placement subproblem becomes submodular. Leveraging this useful submodular property, we propose two algorithms that achieve an approximation ratio of $\frac {1}{2}(1-1/e)$ and $\frac {1}{3}(1-1/e)$ for the original non-relaxed problem, respectively. Finally, we corroborate the effectiveness of the proposed algorithms through extensive evaluations using trace-driven simulations.

Data envelopment analysis (DEA) measures the efficiency score of a set of homogeneous decision-making units (DMUs) based on observed input and output. Considering input-oriented, the inverse DEA models find the required input level for producing a given amount of production in the current efficiency level. This article proposes a new form of the inverse DEA model considering income (for planning) and budget (for finance and budgeting) constraints. In contrast with the classical inverse model, both input and output levels are variable in proposed models to meet income (or budget) constraints. Proposed models help decision-makers (DMs) to find the required value of each input and each output’s income share to meet the income or budget constraint. We apply the proposed model in the efficiency analysis of 58 supermarkets belonging to the same chain. However, these methods are general and can be used in the budgeting and planning process of any production system, including business sectors and firms that provide services.

PurposeBudget constraints faced by social housing projects (SHPs) developers can lead to poor value delivery to the end-users. Without a structured method to guide the decision-making processes during product development, SHP often fails to meet user needs and aspirations. Function analysis (FA) is an essential step in a value management (VM) study. Still, FA practice in SHP has been treated with secondary importance. This paper aims to propose a method for improving FA practice in the VM process for a Brazilian SHP to achieve optimum benefits.Design/methodology/approachThe research followed Design Science Research (DSR) in a specific single case study context, focusing on solving practical problems while contributing to theory. The method has 11 steps and guides a complete VM exercise. Its development was based on prior VM research. Focus group meetings were held to get feedback about the initial method's version to increase its relevancy and applicability in the studied context.FindingsThe proposed method suggests that scarce financial resources in SHP do not necessarily lead to low-value delivery and low-design quality. When FA is fully implemented, opportunities for cost reallocations to enhance value are identified.Originality/valueVM prior research in construction has not provided a specific method for improving FA practice for social housing developers to enhance value in the context of scarce financial resources.

In opportunistic mobile crowdsensing, the objective of service requesters is to have as many of their sensing tasks completed as possible within their budget constraints, whereas that of participants (workers) is to collect the highest monetary reward possible on their trajectories. However, these objectives can conflict and may result in unhappy service requesters or workers if the matching between them is not handled carefully. In this paper, we study the problem of finding task assignments that fulfill both coverage-aware preferences of service requesters and profit-based preferences of workers in a budget-constrained, opportunistic mobile crowdsensing system. Since this is a matching problem with bilateral preferences, we aim to find a matching in which everyone is satisfied with their assignment based on their preference profile. We first propose a polynomial-time approximation algorithm for general settings, and then show that a slightly modified version of this algorithm has a constant approximation ratio when the rewards offered to workers by service requesters are proportional to the coverage capability of workers for corresponding tasks. Through extensive simulations, we evaluate the performance of our algorithms in different settings, and show that they mostly provide substantially better task assignments in terms of user happiness and coverage quality while having a few orders of magnitude lower running times compared to the benchmark algorithms.

Owing to budget constraints and the significant numbers of deficient bridges in various countries, a systematic approach is needed to more efficiently allocate limited resources to bridge maintenan...

Sample Average Approximation (SAA) is used to approximately solve stochastic optimization problems. In practice, SAA requires much fewer samples than predicted by existing theoretical bounds that ensure the SAA solution is close to optimal. Here, we derive new sample-size bounds for SAA that, for certain problems, are logarithmic (existing bounds are polynomial) in problem dimension. Notably, our new bounds provide a theoretical explanation for the success of SAA for many capacity- or budget-constrained optimization problems.

Crowdsourcing has become a popular paradigm to leverage the collective intelligence of massive crowd workers to perform certain tasks in a cost-effective way. Task assignment is an essential issue in crowdsourcing platforms owing to heterogeneous tasks and work skills. In this article, we focus on assigning workers with diversified skill levels to crowdsourcing tasks with different quality requirements and budget constraints. Task assignment is fundamentally a many-to-one matching problem, where one task is allocated to multiple users who can meet the minimum quality requirement of the task within the limited budget. While most existing works try to maximize the utility of the crowdsourcing platform, we take into account the individual preferences of crowdsourcers and workers toward each other to ensure the stability of task assignment results. In this article, we propose task assignment mechanisms that can guarantee stable outcomes for the many-to-one matching problem with lower and upper bounds (i.e., quality requirement and budget constraint) in regard to heterogeneous worker skill levels. Extensive simulation results show that the proposed algorithms can greatly improve the success ratio of task accomplishment and worker happiness compared with existing algorithms.

This paper proposes a multiple discrete continuous (MDC) choice modelling framework with the following features for analyzing individuals’ time use decisions: (a) accommodates non-additively separable (NAS) utility functions with respect to time allocation across different non-work activities, (b) considers both time and money constraints by integrating the two constraints into a single economic constraint, and (c) allows corner solutions for (i.e., non-participation in) discretionary, non-work activities. The proposed framework is applied to analyze weekly time use of employed individuals in Netherlands using a data set derived from the Time use and Consumption survey of the Longitudinal Internet Studies for the Social Sciences (LISS) panel. The empirical results of the proposed model are compared to those from two simpler models – (a) a model that ignores the monetary budget constraint but employs the NAS utility function and (b) a model that considers both time and money budget constraints but uses a simpler, additively separable utility function. The results suggest that considering both time and money constraints, when combined with the NAS utility function, facilitates in capturing rich substitution patterns in time allocation to non-work activities, in addition to revealing demographic heterogeneity in satiation effects. Further, the proposed model offers a superior goodness of fit when compared to that from the simpler models that ignore either the monetary constraint or the NAS utility function. Finally, the model results provide important policy insights for reducing demographic inequities in time allocation among employed individuals.

This research analyses the re-organization of a blood supply chain organization. Blood supply chain network design is a hard problem. Uncertainties of the blood supply and demand, perishability of blood over time and compatibility of blood types are some factors that make the problem difficult. This paper presents a novel multi-objective mixed-integer location-allocation model for a blood supply chain design problem. Unlike many studies on blood supply chain design in the literature, supply chain network consisting of mobile and permanent units is planned together effectively with our mixed-integer programming model. Multi-objective structure of the model minimizes distances between the blood supply chain elements and the length of the mobile unit routes. The objectives are prioritized by experts using the Analytical Hierarchical Process. Finally, the model is implemented on a real life case study using real data from the Eastern Anatolia region of Turkey for various supply demand scenarios. The solutions offered by the model are compared with the current situation in the region. It is shown that the proposed model gives at least %25 more effective solutions. Moreover, sensitivity analysis on the budget constraint is conducted, and robustness of the model is empirically illustrated.

A stochastic flow network composed of multistate arcs can be utilized to describe several practical systems such as computer networks, where transmission time taken for sending data to a sink is an important index. Determining a path with minimum transmission time is known as the quickest path problem (QPP). All algorithms addressing the QPP assume that the determined minimal paths (MPs) are disjoint. Further, for the general case of intersectional MPs, if a congestion phenomenon occurs during the transmission process, these algorithms will lead to an incorrect outcome. Moreover, in practical scenarios, as a budget limit is considered, spare routing is applied to consolidate the system. The objective is to develop an algorithm based on Monte Carlo simulations (MCSs) for evaluating the system reliability while considering the congestion phenomenon. The system reliability is the probability that a specific amount of data can be transmitted successfully through multiple MPs under both time and budget constraints. Furthermore, spare routing to increase the system reliability is established in advance to specify the main and spare MPs. Experiments validate the evaluation of system reliability based on MCSs. The credibility and efficiency of the proposed algorithm are also discussed.

Without proper flow channelization, congestion and overcrowding in pedestrian traffic may lead to significant inefficiency and safety hazards. Thus, the design of guideway networks that provide a fine balance between traffic congestion and infrastructure construction investment is vital. This paper presents a mathematical formulation and topology optimization framework for paved pedestrian guideway design under physics-based traffic equilibrium in a continuous space. Pedestrians are homogeneous, and their destination and path choices under the Nash equilibrium condition are described by a set of nonlinear partial differential equations. The design framework optimizes the deployment of pavement, which alters the road capacity and directly affects pedestrians’ free flow travel speed. A maximum crowd density constraint is included in the design model to address public safety concerns (e.g., over stampede risks). A series of numerical experiments are conducted to illustrate the effectiveness of the proposed model as well as solution techniques. The proposed framework, which builds on the traffic equilibrium theory, produces optimized guideway designs with controllable maximum pedestrian density, accounts for budget constraints (through an adjustable multiplier that balances pavement construction and travel costs), and allows for control of the spatial configuration of road branches. Comparison with lamellar structures and more conventional guideway designs demonstrates better performance of the outcomes from the proposed modeling and optimization framework.

A highway in poor conditions can raise transportation costs. Due to budgetary constraints, pavement maintenance programming is considered a difficult decision-making problem. In this article we pro...

Selecting a set of requirements to implement in the next software release is an NP-Hard problem known as NRP. We propose multi-objective versions of grey wolf optimizer and whale optimization algorithm for solving bi-objective NRP. We used these two algorithms and three other evolutionary algorithms to solve NRP problem instances from four datasets. The cost-to-score ratio and the roulette wheel are used to satisfy constraints of the NRP problem. We compare obtained Pareto fronts based on eight quality indicators. In addition to four general multi-objective optimization quality indicators, the three aspects of fairness among clients and also uncertainty are reconfigured as quality indicators. These quality indicators are computed for a Pareto front. Results show that MOWOA performs better than others and makes requirement selection fairer. MOGWO works better than the rest when budget constraints are reduced.

Workflow scheduling is crucial to the efficient operation of cloud platforms, and has attracted a lot of attention. Up to now, many algorithms have been reported to schedule workflows with budget constraints, so as to optimize workflows' makespan on cloud resources. Nevertheless, the hourly-based billing model in cloud computing is an ongoing challenge for workflow scheduling that easily results in higher makespan or even infeasible solutions. Besides, due to data constraints among workflow tasks, there must be a lot of idle slots in cloud resources. Few works adequately exploit these idle slots to duplicate tasks' predecessors to shorten their completion time, thereby minimizing workflow's makespan while ensuring its budget constraint. Motivated by these, we propose a task duplication based scheduling algorithm, namely TDSA, to optimize makespan for budget-constrained workflows in cloud platforms. In TDSA, two novel mechanisms are devised: 1) a dynamic sub-budget allocation mechanism, it is responsible for recovering unused budget of scheduled workflow tasks and redistributing remaining budget, which is conducive to using more expensive/powerful cloud resources to accelerate completion time of unscheduled tasks; and 2) a duplication-based task scheduling mechanism, which strives to exploit idle slots on resources to selectively duplicate tasks' predecessors, such advancing these tasks' completion time while trying to ensuring their sub-budget constraints. At last, we carry out four groups of experiments, three groups on randomly generated workflows and another one on actual workflows, to compare the proposed TDSA with four baseline algorithms. Experimental results confirm that the TDSA has an overwhelming superiority in advancing the workflows' makespan (up to 17.4%) and improving the utilization of cloud computing resources (up to 31.6%).

We propose a method, whose purpose is to combine a set of GPS traces collected by bicyclists with a set of notifications of problematic situations to determine an optimal action plan for solving safety related problems in a t raffic network. In particular, we use optimization to determine which problem locations to resolve under a given budget constraint in order to maximize the number of impediment free trips. The method aims to suggest a priority of impediments to resolve, which would be manually infeasible. %A set of GPS traces for bicyclists and a set of notifications by bicyclists of problematic situations (spots identified by GPS records) had been collected independently. The data collection periods did not coincide but overlapped and none was contained in the other one. The aim of our work is to use both datasets to determine an optimal action plan for problem solving given a limited budget. The proposed method consists of two steps. First, problematic locations are clustered, where each cluster corresponds to a so-called impediment. Each impediment is associated with trips nearby using a distance function. The trip set is partitioned by matching each trip with the largest set of its affecting impediments. Solving all impediments associated with such a part induces a cost and makes the associated part of trips impediment free. The second step aims to find the set of impediments that can be solved with a given budget and that makes the maximum number of trips impediment free. A branch-and-bound optimizer for the second step is presented and evaluated. The clustering parameters affect the set of identified impediments and the extent of each of them. In order to evaluate the sensitivity of the result to the clustering parameters a technique is proposed to consistently estimate the impediment resolution cost. Our study aims to support the interactive urban designer to improve the urban bicycle road infrastructure. By providing a method to prioritize between impediments to resolve, it also aims to contribute to a safer and more attractive traffic situation for bicyclists. The research leading to this paper was partially supported by the Smarta Ofentliga Miljöer II (SOM II) project of the Lund (Sweden) municipality by supplying data.

Dedicated infrastructures are commonly used for urgent computations. However, using dedicated resources is not always affordable due to budget constraints. As a result, utilizing shared infrastructures becomes an alternative solution for urgent computations. Since the infrastructures are meant to serve many users, the urgent jobs may arrive when regular jobs are using the necessary resources. In such a case, it is necessary to preempt the regular jobs so that urgent jobs can be executed immediately. Most conventional methods for job scheduling have focused on reducing the response times and waiting times of all jobs. However, these methods can delay urgent jobs and hinder them from being completed within a stipulated deadline. Furthermore, in heterogeneous systems with coprocessors, preemption becomes more difficult because coprocessors rely on several system software functionalities provided by the host processor. In this paper, we propose a parallel job scheduling method to effectively use shared heterogeneous systems for urgent computations. Our method employs an in-memory process swapping mechanism to preempt jobs running on the coprocessor devices. The results of our simulations show that our method can achieve a significant reduction in the response time and slowdown of regular jobs without substantial delays of urgent jobs.

Given a linear dynamical system affected by noise, we study the problem of optimally placing sensors (at design-time) subject to a sensor placement budget constraint in order to minimize the trace of the steady-state error covariance of the corresponding Kalman filter. While this problem is NP-hard in general, we consider the underlying graph associated with the system dynamics matrix, and focus on the case when there is a single input at one of the nodes in the graph. We provide an optimal strategy (computed in polynomial-time) to place the sensors over the network. Next, we consider the problem of attacking (i.e., removing) the placed sensors under a sensor attack budget constraint in order to maximize the trace of the steady-state error covariance of the resulting Kalman filter. Using the insights obtained for the sensor placement problem, we provide an optimal strategy (computed in polynomial-time) to attack the placed sensors. Finally, we consider the scenario where a system designer places the sensors under a sensor placement budget constraint, and an adversary then attacks the placed sensors subject to a sensor attack budget constraint. The resilient sensor placement problem is to find a sensor placement strategy to minimize the trace of the steady-state error covariance of the Kalman filter corresponding to the sensors that survive the attack. We show that this problem is NP-hard, and provide a pseudo-polynomial-time algorithm to solve it.

According to the fiscal theory of the price level, in a non-Ricardian regime, high public debt should lead to a price-level increase so as to fulfill intertemporal government budget constraint. Forward-looking agents should anticipate this rise by pushing up their inflation expectations. This study tests the above hypothesis by analyzing the impact of fiscal sustainability on the formation of inflation expectations by private-sector agents – consumers and professional forecasters – in European countries. In the study, we estimate models of inflation expectations, and we include public debt among the explanatory variables. The novelty of this study lies in our analysis of whether the relationship between public debt and inflation expectations is affected by the degree to which fiscal policy is sustainable in European economies. We find that fiscal stance influences the inflation expectations of consumers and professional forecasters. Contrary to intuition, the reaction of consensus economic forecasts to public debt in countries with sustainable fiscal policies seems stronger than in economies with less disciplined fiscal policies. This may suggest that fiscal authorities are constrained by the way in which private-sector agents form their expectations, conducting more responsible fiscal policy in countries where the reaction of economic agents to fiscal variables is stronger.

In recent decades, the number of schools in Norwegian rural municipalities has been reduced substantially, often accompanied by much contestation in the local community. In Norway's decentralized governance system, the municipal-level authorities have autonomy over decisions on school structure. However, municipal school administrations may have considerable indirect influence, inter alia in preparing the cases prior to meetings of the local council. This article examines experiences with, perceptions of and main concerns with school-closure processes among those handling school issues in the municipalities. It builds on an online survey of the local school administrations in Norwegian municipalities with fewer than 15,000 inhabitants, as well as case studies in three municipalities where school closure was or had recently been high on the agenda. Although the municipal school administrations recognize the importance of local schools for the communities, their primary concern is to secure a good learning and social environment for pupils, within the defined budgetary constraints. In contested cases, this usually leads them to side with politicians favoring closure. However, acknowledging the importance of the school for the community and the risks of local tensions, municipal school administrations also stress the importance of transparent and open discussions among those involved, before a decision is made.

Roadside unit (RSU) planning is vital for the operation of an intelligent transportation system (ITS). RSUs provide ground coverage limited by obstacles. Unmanned aerial vehicles (UAVs) can complement RSU coverage by providing flexible connectivity capable of adapting coverage for traffic fluctuations, energy consumption, and budgetary constraints that all have effects on ITS operations. This article proposes a general RSU/UAV joint planning solution, where complex dynamic parameters are investigated. The objective is to maximize the effective coverage of placed RSUs and UAV docks given: a budget comprised of periodic operating expenses and capital expenditures, limitations of the ground transceivers and UAVs, and use of renewable energy to offset the on-grid electricity cost. We formulate a mixed-integer quadratically constrained problem that can determine the optimal placement of RSUs and UAV stations, RSU activation schedules, if solar panels are attached, and their coverage during each time period. Due to NP-hard complexity of such a planning problem, we design a heuristic algorithm that produces suboptimal solutions in less time. Afterward, we perform a sensitivity analysis and show that changes to the parameters lead to logical shifts in infrastructure coverage. Additionally, we visualize the algorithm's performance on a large setting—Manhattan Island.

The extant literature has inquired the effect of several socio-economic individual characteristics on green purchasing, showing that education, social status and other individual characteristics may enhance virtuous behaviours, but has paid few attention to parenthood and to the presence of children. Theoretical models of microeconomic choices show that if the parents’ utility includes that of children, then their presence should affect parents’ green purchases. Indeed, parents should desire a sustainable world for their children to have access to enough resources. Using data from 61 countries, this paper inquires whether parenthood and the number of children affect people’s decisions about green consumption; in addition the presence of a gender effect (i.e. mothers being more prone to green choices than fathers) exists. The empirical evidence shows that having children decreases the probability of buying green products; an increasing number of children have the same effect. However, parenthood and number of children have opposite effects on fathers and on mothers. Policymakers and marketing divisions of firms may benefit from this empirical evidence. In addition, analyses are performed on homogeneous clusters of countries to account for possible differences due to different environmental engagement or level of economic development. The results show that, while the gender effect is persistent, that of parenting and the number of children is not statistically significant in affluent countries. This last result suggests that budget constraints are a major factor affecting green purchases.

With the proliferation of sensor-rich smart devices (smartphones, ipads, etc.), Mobile Crowd Sensing (MCS) has gradually attracted much attention in the research community recently. Worker recruitment is a crucial research issue in MCS system, in which platform recruits workers and assigns sensing tasks to them. While previous studies focus on either opportunistic-sensing-based worker recruitment or participatory-sensing-based worker recruitment separately, we proposed a two-phase hybrid worker recruitment framework named HySelector, which recruits workers in two phases. First, in the offline phase, borrowing the idea of influence propagation in communication and social network, we proposed algorithm to recruit opportunistic workers during their daily routines which can alleviate the cold start problem in traditional MCS system. Then, in the online phase, in order to reduce the computational complexity, we devised algorithm to incentivize participatory workers to move to specific subareas obtained by subareas clustering to fulfil sensing tasks. In both phases, we considered guaranteeing the incentive cost and time constraint. Experimental results on two open datasets demonstrated that compared with other methods, HySelector had better performance in terms of spatial coverage and running time under budget constraints.

One of the major causes of non-recurrent traffic congestion in urban areas is the implementation of transport infrastructure projects on city roads. The seeming ubiquity of work zones in cities causes road user frustration and safety hazards, and public relations problems for the transport agency. For this reason, transport agencies seek strategic ways to not only select urban projects but also schedule them in a manner that minimizes the effort associated with these functions. In other words, they seek to exploit the synergies between the tasks of project selection and project scheduling while duly accommodating the project interdependencies. This study introduces a general framework that simultaneously and optimally selects and schedules urban road projects subject to budgetary constraints over a given planning horizon. The project classes considered in this study are lane addition, new road construction, and road maintenance. Through a mimicry of the classic Stackelberg leader-follower game, this problem is formulated herein as a bi-level program. In the upper level, the leader (transport agency decision-makers) determines an optimal set of projects from a larger pool of candidate projects and decides an optimal schedule for their implementation. In the lower level, the followers (road users) seek to minimize their travel delays based on the two decisions made by the leader in the upper level. The numerical experiments show that if the decision-makers do not consider the peri-implementation capacity reduction, the resulting set of selected projects and their construction schedule can lead to significant travel delay cost for the road users.

This paper proposes an optimization model of supply chain resilience strategy for large passenger aircraft. A quality function deployment (QFD) framework is conducted to analyze the resilience of the large passenger aircraft supply chain, and the key parameters are characterized based on the probabilistic linguistic term. Then based on the output of the QFD framework an optimization model of the resilience strategy considering the stochastic disturbance faced by the supply chain is constructed. Taking the supply chain for large aircraft cockpit control display module as an example to illustrate the application steps and feasibility of the model, the results demonstrate that change of supply chain management responsibilities, implementing hierarchical management of suppliers, seeking coordinated implementation of inventory management mode, and improving the pre-risk identification system, play prominent roles in enhancing supply chain resilience, and the combination of different strategies can indeed enhance the supply chain resilience under the budget constraint.

We consider two covering variants of the network design problem. We are given a set of origin/destination pairs, called O/D pairs, and each such O/D pair is covered if there exists a path in the network from the origin to the destination whose length is not larger than a given threshold. In the first problem, called the Maximal Covering Network Design problem, one must determine a network that maximizes the total fulfilled demand of the covered O/D pairs subject to a budget constraint on the design costs of the network. In the second problem, called the Partial Covering Network Design problem, the design cost is minimized while a lower bound is set on the total demand covered. After presenting formulations, we develop a Benders decomposition approach to solve the problems. Further, we consider several stabilization methods to determine Benders cuts as well as the addition of cut-set inequalities to the master problem. We also consider the impact of adding an initial solution to our methods. Computational experiments show the efficiency of these different aspects., SCOPUS: ar.j, info:eu-repo/semantics/published

This paper formulates duopoly competition between two non-cooperative heterogeneous ride-sourcing platforms considering the adoption of electric vehicles (EV) and government subsidies on EVs. One ride-sourcing platform adopts an EV asset-heavy strategy by undertaking EV depreciation costs, while the other ride-sourcing platform adopts an asset-light strategy by hiring drivers who own vehicles. The first-order conditions of each platform's pricingunder competitive equilibrium are derived based on a game-theoretical model that involves the stakeholders (i.e., riders, drivers, and ride-sourcing platforms). Based on the modeling framework ofduopoly competition between two heterogeneous ride-sourcing platforms, this paper proposes an optimization model with social welfare maximization for the determination of governmentsubsidy strategies. We conduct numerical illustrations to demonstrate how governmental subsidies on EV purchase and charging stations impact the endogenous variables in equilibrium (e.g., price for riders, wage for drivers, and market share for each platform) under different formulations of riders' waiting time cost function, which are increasing returns to scale. Also, a specialmodel with a fixed commission ratio is discussed. The results provide suggestions for decision-makers on how to allocate subsidy within the budget constraint.

Assortment planning deserves much attention from practitioners and academics due to its direct impact on retailers’ commercial success. In this paper we focus on the increasingly popular retail practice to use combined product assortments with both “standard” and more fashionable and short-lived “variable” products for building up store traffic of “loyal” and “non-loyal” heterogeneous customers and enlarging the sales due to the potential cross-selling effect. Addressing the assortment planning as a bilevel optimization problem, we focus on decision-dependent uncertainties: the retailer’s binary decision about product inclusion influences the distribution of the product’s demand. Furthermore, our model accounts for customers’ optimal purchase quantities, which depend on budget constraints limiting the basket that a customer is able to purchase. We propose iterative heuristics using optimal quantization of demand and customers budget distributions to define the total assortment and the inventory level per product. These heuristics provide lower bounds on the optimal value. We conduct a comparison to other existing lower bounds and we formulate upper bounds via linear (LP) and semidefinite (SDP) relaxations for the performance evaluation of the heuristics and for an efficient numerical solution in high-dimensional cases. For managerial insights, we compare the proposed approach with three assortment planning strategies: (1) the retailer does not carry variable products; (2) the retailer ignores the cross-selling effect; and (3) the maximum space allocated to each product is fixed. Our results suggest that variable assortment boosts the retailers profits if the cross-selling effect is not neglected in the decision about products quantities.

Disruption-tolerance networks (DTNs) are suitable for applications that may lack continuous network connectivity. Examples of such applications include coupon distribution, crisis relief, traffic notification, and broadcasting news from a website. Generally, these contents have a temporal constraint, so that their value will be decreased over time. DTNs have to utilize mobile relay nodes to transmit messages from the sender to the destination. These relay nodes often have selfish behavior, leading to a lack of cooperation. To improve the overall routing functionality, one must motivate relay nodes to share their resources. Thus, different incentives and rewarding mechanisms must be devised to encourage cooperation. We believe that microeconomics theories are appropriate mathematical tools to model the interactions between the DTN nodes. In microeconomics, buyers aim at maximizing their utility concerning their budget constraints. In this paper, the demand–supply theory is deployed to mitigate nodes’ selfishness and to create incentives among them. Each user can receive multiple sub-messages each of which containing special benefits for his/her. In this way, nodes are motivated to forward messages, which in turn leads to greater profitability for them and maximizing the social welfare of the society. The simulation of the proposed algorithm illustrates its superiority in terms of significant criteria such as delivery ratio, end-to-end delay, number of dropped messages, buffering time, number of hops, overhead ratio, etc.

PurposeEmergency service departments face changing mission requirements, budget constraints and a demanding work environment. This study examined the perceptions of fire chiefs, officers and firefighters who attended the National Professional Development Symposium on the use of a tiered approach when responding to calls, the continued increase in medical calls and mental health services available to fire service personnel.Design/methodology/approachThis study examined the perceptions of fire chiefs, officers and firefighters who attended the National Professional Development Symposium on the use of a tiered approach when responding to calls, the continued increase in medical calls and mental health services available to fire service personnel.FindingsSurvey respondents indicated that they either are currently or would consider using a tiered approach to sending a fire engine and crew or a lighter vehicle to medical or other calls based requirements identified using a tiered approach.Research limitations/implicationsThis idea has future implications regarding the vehicle mix of fire stations as administrators seek to meet the needs of the public most effectively. Survey responses also noted the need for mental health services arguing that care seeking firefighters should have the option of getting mental health services within the station or at an external location. Calls involving babies or young children were overwhelming cited as the most difficult. Additionally, 95% of respondents indicated a belief that most firefighters suffer from PTSD.Practical implicationsRecommendations include: A larger scale survey and analysis of first responder perceptions based on this study. Identifying “best practices” of the most effective “tiered response” approaches to deploying emergency services resources to calls. Studying Mental Health services combating PTSD to identify best practices. Lastly, emergency services administrators should consider changes to the “vehicle mix” when equipping or reequipping stations.Social implicationsSocial implications include use of a “tiered response” approach to emergency calls and focusing how best to support the mental health needs of firefighters.Originality/valueFire Departments are only beginning to explore the idea of using a tiered response to respond to emergencies. This study identifies both short and long term implications of using a tired approach. A secondary emphasis of this study explores difficult calls and PTSD issues faced by firefighters.

In this article, we propose and study the problem of trajectory-driven influential billboard placement: given a set of billboards U (each with a location and a cost), a database of trajectories T , and a budget L , we find a set of billboards within the budget to influence the largest number of trajectories. One core challenge is to identify and reduce the overlap of the influence from different billboards to the same trajectories, while keeping the budget constraint into consideration. We show that this problem is NP-hard and present an enumeration based algorithm with (1-1/e) approximation ratio. However, the enumeration would be very costly when | U | is large. By exploiting the locality property of billboards’ influence, we propose a partition-based framework PartSel. PartSel partitions U into a set of small clusters, computes the locally influential billboards for each cluster, and merges them to generate the global solution. Since the local solutions can be obtained much more efficiently than the global one, PartSel would reduce the computation cost greatly; meanwhile it achieves a non-trivial approximation ratio guarantee. Then we propose a LazyProbe method to further prune billboards with low marginal influence, while achieving the same approximation ratio as PartSel. Next, we propose a branch-and-bound method to eliminate unnecessary enumerations in both PartSel and LazyProbe, as well as an aggregated index to speed up the computation of marginal influence. Experiments on real datasets verify the efficiency and effectiveness of our methods.

The Ecuador Planning Secretariat distributes its regions in 9 administrative planning areas, allowing the decentralization and decentralization of benefits, responsibilities and economic resources of the central government to all public entities; Higher Education Institutions IES receive financial resources from the state and finance their permanent expenses including scientific research expenses that are limited to 6% of the budget allocated to each IES, the objective is to analyze the budget constraints of four state universities located In the Zone 3, 5 and 8 of Ecuador's planning, using a qualitative empirical methodology, the impact of the last three years, 2015, 2016 and 2017 was determined both at the level of execution and scientific production. An article template and instructions on the editorial process and the text, figure and references format are provided.

Coupon advertisement is everywhere in people's daily lives, and it is a common marketing strategy adopted by merchants. A problem, Budget Profit Maximization with Coupon Advertisement, is formulated in this article, which is a branch of classical profit maximization problem in social networks. Profit maximization has been researched intensively before, but its theoretical bound is not satisfactory usually because of NP-hardness, budget constraint and non-monotonicity. Learned from the lastest results, we proposed the B-Framework, which combines the ideas of Random Greedy and Continuous Double Greedy to obtain a more acceptable approximation ratio for this problem. For Continuous Double Greedy, it can be implemented by multilinear extension and discretized techniques. In addition, most of existing researches consider only maximizing total profit, however, in real scenarios, the diffusion probability is hard to determine due to the uncertainty. Then, we study the robustness for budgeted profit maximization, which can be used as a general strategy to analyze the robustness of non-monotone submodular function. It aims to obtain the best solution maximizing the ratio between the profit of any feasible seed set and the optimal seed set. We design LU-B-Framework first, and then we apply the method of uniform sampling to improve the robustness by reducing the uncertainty. The effectiveness and correctness of our algorithms are evaluated by heavy simulation on real-world social networks eventually.

Big data is the fuel for powering pervasive artificial intelligence (AI) applications. Aiming at promoting efficient cooperation in data market for big data, in this paper we propose a novel cooperative data purchase framework, by leveraging the power of the data user crowd and their intrinsic trustworthy collaboration relationships. For achieving efficient cooperative data purchase, we develop a comprehensive approach consisting of both data purchase group formation and selection. For the data purchase group formation, we partition the users into multiple data purchase groups for the purpose of budget pooling, by taking into account their data interest and budget levels, meanwhile respecting their underlying collaboration relationships and the maximum allowable group size for data sharing enforced by the data market platform. For the data purchase group selection, we construct a data purchase flow network formulation and devise a minimum cut based solution for selecting the proper set of data purchase groups to fully support their demands subject to the budget constraints. We extensively evaluate the performance of cooperative data purchase framework using both Erdos-Renyi and scale-free collaboration graphs. The numerical results demonstrate that the proposed framework can achieve superior performance, with more than ${40}\%$ and ${100}\%$ performance gain over the case without cooperation in terms of the total received payment and the number of satisfied users, respectively.

Mobile edge computing is a new paradigm that can enhance the computation capability of end devices and alleviate communication traffic loads during transmission. Mobile edge computing is highly useful for emerging resource-hungry mobile applications. However, a key challenge for mobile edge computing systems is multiple resources allocation between Mobile Edge Clouds (MECs) and End Users (EUs), especially for multiple heterogeneous MECs and EUs. To address this problem, we propose a Stackelberg game-based framework in which EUs and MECs act as followers and leaders, respectively. The proposed framework aims to compute a Stackelberg equilibrium solution in which each MEC achieves the maximum revenue while each EU obtains utility-maximized resources under budget constraints. We decompose the multiple resources allocation and pricing problem into a set of subproblems in which each subproblem only considers a single resource type. The Stackelberg game framework is constructed for each subproblem wherein each player (i.e., an EU) can selfishly maximize its utility by selecting an appropriate strategy in the strategy space. We prove the existence of the subgame Stackelberg equilibrium and develop algorithms to determine the Stackelberg equilibrium for each resource type, including an optimal demand computation algorithm, to determine the best resource demand strategy for an EU and an iterative algorithm to find an equilibrium price. The equilibrium solutions of all subgames constitute the equilibrium solution of the original problem. We also conduct simulation experiments of our game, such as numerical data for the Stackelberg equilibrium, numerical data for the convergence of the Stackelberg equilibrium, and numerical data as the system size increases. Finally, we demonstrate that an EU with idle resources can play the role of an MEC.

The toll design problem (TDP) provides a quantitative approach to the design of road pricing schemes. Its practical use, however, can be computationally challenging if the formulated TDP requires time-consuming computer models to evaluate candidate designs, especially if such designs must account for multiple objectives. For TDPs to be of practical relevance to the real-world planning of sustainable transportation networks, efficient TDP solution heuristics must be developed. To this end, two surrogate-based solution heuristics for multi-objective TDPs are proposed in this paper. Surrogate-based optimization uses simple approximations to computationally expensive models in order to accelerate the discovery of good solutions. The general search strategy of the proposed heuristics is as follows. In each iteration of the heuristics, a pool of candidate pricing schemes with unique sets of tolling locations and associated tolling levels is generated. From this pool of designs, the heuristics use the surrogate models to screen for solutions that are expected to be nondominated and that meet a specified selection criterion. Then, these promising designs are evaluated by the computationally expensive models, and the outputs obtained from these evaluations are used to update the surrogate models. Both heuristics repeat this general process until a maximum number of iterations are completed, at which point the best TDP solutions are returned. In addition to the solution heuristics, this paper presents a transportation network paradox that highlights how transportation network interventions intended to reduce traffic emissions could have unintended effects on a population’s exposure to pollutants. The paradox also is used to illustrate the practical complexity of accounting for environmental inequality objectives, as well as the relevance of multi-objective analysis approaches to transportation planning. Formulations of multi-objective TDPs that consider both travel and pollutant exposure-related objectives are also presented, including the objectives of reducing human intake of vehicle-generated air pollutants and of minimizing environmental inequality. The Sioux Falls and Chicago Sketch networks were used in tests that examined the relative performance of the heuristics, as well as the characteristics of pricing configurations obtained under different budget constraints. Among other results, the tests show that a pricing configuration could decrease total pollutant intake and environmental inequality, while at the same time producing an increase in pollutant concentrations in a significant number of pollutant receptor points.

In teacher-student framework, a more experienced agent (teacher) helps accelerate the learning of another agent (student) by suggesting actions to take in certain states. In cooperative multiagent reinforcement learning (MARL), where agents need to cooperate with one another, a student may fail to cooperate well with others even by following the teachers' suggested actions, as the polices of all agents are ever changing before convergence. When the number of times that agents communicate with one another is limited (i.e., there is budget constraint), the advising strategy that uses actions as advices may not be good enough. We propose a partaker-sharer advising framework (PSAF) for cooperative MARL agents learning with budget constraint. In PSAF, each Q-learner can decide when to ask for Q-values and share its Q-values. We perform experiments in three typical multiagent learning problems. Evaluation results show that our approach PSAF outperforms existing advising methods under both unlimited and limited budget, and we give an analysis of the impact of advising actions and sharing Q-values on agents' learning., 31 pages, 16 figures, submitted to ACM Transactions on Autonomous and Adaptive Systems (TAAS)

Bitcoin is one of the most popular cryptocurrency in the world. Miners in the Bitcoin network reduce their risks through participating in mining pool. Existing mining pool systems do not consider the cost and strategy of miners. In this paper, we study two mining models: public cost model and private cost model. For the public cost model, we design an incentive mechanism, called Mining game, using a Stackelberg game. We show that Mining game is individually rational, profitable, and has the unique Stackelberg Equilibrium. For the private cost model, we formulate the Budget Feasible Reward Optimization (BFRO) problem to maximize the reward function under the budget constraint, and design a budget feasible reverse auction to solve the BFRO problem, which is computationally efficient, individually rational, truthful, budget feasible, and constant approximate. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.

Many large-scale natural and human-created disasters have drawn the attention of researchers towards the solutions of evacuation planning problems and their applications. The main focus of these solution strategies is to protect the life, property, and their surroundings during the disasters. With limited resources, it is not an easy task to develop a universally accepted model to handle such issues. Among them, the budget-constrained network flow improvement approach plays significant role to evacuate the maximum number of people within the given time horizon. In this paper, we consider an evacuation planning problem that aims to shift a maximum number of evacuees from a danger area to a safe zone in limited time under the budget constraints for network modification. Different flow improvement strategies with respect to fixed switching cost will be investigated, namely, integral, rational, and either to increase the full capacity of an arc or not at all. A solution technique on static network is extended to the dynamic one. Moreover, we introduce the static and dynamic maximum flow problems with lane reversal strategy and also propose efficient algorithms for their solutions. Here, the contraflow approach reverses the direction of arcs with respect to the lane reversal costs to increase the flow value. As an implementation of an evacuation plan may demand a large cost, the solutions proposed here with budget constrained problems play important role in practice.

International audience; With the increase of transport demands, more pressureand challenges are being imparted into efficient transportation.As a conventional and direct congestion alleviation strategy,constructing new roads and lanes is increasingly restricted bylimited land resources and high costs, making full use of existingtransport network via appropriate management is thus critical torealize the sustainable development of transportation systems. Asa flexible management strategy, lane reservation strategy has beenwidely adopted in real life. The reserved lanes can improve theefficiency of special transports, while they bring negative impactsuch as travel delay for general-purpose transports. In addition,the setting and operating of reserved lanes require a certainamount of cost. This paper proposes a new dual-objective integerlinear programming model for optimally determining reservedlanes on a network for time-guaranteed special transports inorder to simultaneously maximize the benefits and minimize thenegative impact brought by reserved lanes, which incorporatesroad residual capacity and limited budget to the actual decision.Moreover, an iterative weighted sum based algorithm is proposedto solve it, in which a new relax-and-optimize algorithm is developedto exactly solve the single-objective optimization problems.Results of extensive numerical experiments show the effectivenessand efficiency of the proposed model and approach.

Mobile crowd sensing (MCS) acts as a key component of Internet of Things (IoT), which has attracted much attention. In an MCS system, participants play an important role, since all the data are collected and provided by them. It is challenging but essential to recruit credible participants and motive them to contribute high-quality data. In this article, we propose a learning-based credible participant recruitment strategy (LC-PRS), which aims to maximize the platform and participants' profits at the same time via MCS participation. Specifically, the LC-PRS consists of two mechanisms, that a learning-based reward allocation mechanism (L-RAM) first calculates the maximum offered reward for different locations based on the number of participants in each location. Under a budget constraint, the proposed L-RAM prefers to collect sensing data from locations in which relatively few data have so far been collected. Furthermore, for each location, we develop a credible participant recruitment mechanism (C-PRM), whichemploys semi-Markov model and game theory to predict the quality of data provided by each participant and to recruit participants based on the predictions and the maximum offered reward calculated by L-RAM. We formally show LC-PRS has the desirable properties of computational efficiency, selection efficiency, individual rationality, and truthfulness. We evaluate the proposed scheme via simulation using three real data sets. Extensive simulation results well justify the effectiveness of the proposed approach in comparison with the other two methods.