Optimal Deadline Research Articles

Deadline Differentiated Dynamic EV Charging Price Menu Design

The increasing number of electric vehicles (EVs) on the road brings both opportunities and challenges to the power system. For the EV charging stations (EVCSs), it is often difficult to conduct effective operations due to the incomplete information in EVs’ departure times and the opacity of their preference information. To tackle this challenge, we seek to design the optimal deadline differentiated dynamic price menu that offers multiple choice-pairs of deadlines and charging prices. We prove that such price menus can incentivize EVs to truthfully reveal their departure time. We then analyze the properties of the optimal price menu with complete EV information, i.e., social optimality and first-degree price discrimination. For the incomplete information case, we first design a systematic method to estimate the utility and demand information for a large population of EVs based on EV behavior data. Then, we employ mixed-integer quadratic programming for the efficient optimal price menu design. The numerical study based on field data in California verifies the remarkable performance of our designed price menu.

Designing Deadlines

I study how an organization should manage a project of uncertain scope, when it is advised by a privately informed expert who prefers to prolong his employment. The optimal long-term contract combines a deadline for project completion and incentive payments which decline as the deadline approaches. When the firm can additionally learn about the project's state from output, the optimal deadline exhibits variable sensitivity to output, with a hard deadline at the outset of the project and increasingly soft deadlines as the project's performance declines. (JEL D21, D82, D86, G31, J33, M52, M54)

Willingness to delay charging of electric vehicles

Coordinated electric-vehicle charging can produce optimal, flattened loads that would improve reliability of the power system as well as reduce system costs and emissions. Optimal deadline scheduling of residential charging would require customers to defer charging their vehicles and to accept less than a 100% target for battery charge. To analyze the necessary incentives for customers to accept giving up control of when charging of their vehicles takes place, we use data from a choice experiment implemented in an online survey of electric-vehicle owners and lessees in upstate New York (N=462). The choice microdata allowed us to make inference on the willingness to pay for features of hypothetical coordinated electric-vehicle charging programs, exploiting Variational Bayes (VB) inference. Our results show that individuals negatively perceive the duration of the timeframe in which the energy provider would be allowed to defer charging. A negative monetary valuation is evidenced by an expected average reduction in the annual fee of joining the coordinated charging program of $2.66 per hour of control yielded to the energy provider. Our results also provide evidence of substantial heterogeneity in preferences, probably due to early-stage attitudes toward coordinated charging. For example, the 25% quantile of the posterior distribution of the mean of the willingness to accept an additional hour of control yielded to the utility is $4.72. However, the negative valuation of the timeframe for deferring charging is compensated by positive valuation of emission savings coming from switching charging to periods of the day with a higher proportion of generation from renewable sources. Customers also positively valued discounts in the price of energy delivery.

Designing and analysis of a Wi-Fi data offloading strategy catering for the preference of mobile users

<p style='text-indent:20px;'>In recent years, offloading mobile traffic through Wi-Fi has emerged as a potential solution to lower down the communication cost for mobile users. Users hope to reduce the cost while keeping the delay in an acceptable range through Wi-Fi offloading. Also, different users have different sensitivities to the cost and the delay performance. How to make a proper cost-delay tradeoff according to the user's preference is the key issue in the design of the offloading strategy. To address this issue, we propose a preference-oriented offloading strategy for current commercial terminals, which transmit traffic only via one channel simultaneously. We model the strategy as a three-state M/MMSP/1 queueing system, of which the service process is a Markov modulated service process (MMSP), and obtain the structured solutions by establishing a hybrid embedded Markov chain. Our analysis shows that, given the user's preference, there exists an optimal deadline to maximize the utility, which is defined as the linear combination of the cost and the delay. We also provide a method to select the optimal deadline. Our simulation demonstrates that this strategy with the optimal deadline can achieve a good performance.</p>

Optimal deadline scheduling for electric vehicle charging with energy storage and random supply

Motivated by the potential of utilizing used electric vehicle (EV) batteries as the battery energy storage system (BESS) in EV charging stations, we study the joint scheduling of BESS operation and deferrable EV charging load (with the same deadline) in the presence of random renewable generation, EV arrivals, and electricity prices. We formulate the cost-minimizing scheduling problem faced by an EV charging station operator as a dynamic program. When the number of EVs is large, the formulated dynamic program cannot be exactly solved by brute-force methods due to the curse of dimensionality. We characterize a complete, optimal priority rule on energy allocation among EVs. For an important special case with full charging/discharging efficiency and all EVs available for charging at the initial period, we propose a new methodological approach to establish full characterizations of an optimal scheduling policy that enable the development of scalable computational approaches. The proposed approach achieves close-to-optimal performance in numerical experiments with real-world electricity pricing and solar generation data.

Optimal project deadlines for mean-variance incentive contract designs

This paper studies contracts that incorporate incentives in the form of bonuses and penalties on top of the contract’s fixed payment and investigates their impact on the project completion time. Its aim is to derive an optimal deadline that maximizes the mean-variance utility function for a risk-averse contractor. By maximizing his payoff utility function, the contractor becomes more motivated to bid for the project, as well as to meet its deadline. It is shown in this paper that under appropriate risk-aversion levels for the contractor, and when the bonus rate in the contract is bigger than that of the penalty, then an optimal deadline that maximizes the risk-averse mean-variance utility function of the contractor exists and is less than the expected mean of the project random duration. This result is in line with many empirical studies and research reports found in the literature which concluded that the bonus in the contract works better than the penalty to incite the contractor to complete the project earlier than planned. In the other case where the bonus rate is less than or equal to the penalty rate, the utility payoff function for the risk-averse contractor is shown to be strictly increasing in terms of the deadline, and therefore, there exists no limit for it. The latter fact cannot therefore work in favor to the contractor. Special cases of famous probability distributions for the project duration were also investigated and their optimal deadlines were derived either in a closed form formula or computed numerically.

Stochastic Analysis of Delayed Mobile Offloading in Heterogeneous Networks

Mobile cloud offloading that migrates heavy computation from mobile devices to powerful cloud servers through communication networks can alleviate the hardware limitations of mobile devices thus providing higher performance and saving energy. Different applications usually give different relative importance to response time and energy consumption. If a delay-tolerant job is deferred up to a given deadline, or until a fast and energy-efficient network becomes available, the transmission time will be extended, which can save energy because a more energy-efficient communication channel and a less energy-restricted computation platform may become available. However, if the reduced service time fails to cover the extra waiting time, this policy may not be competitive. In this paper, we investigate two types of delayed offloading policies, the partial offloading model where jobs can leave from the slow phase of the offloading process and be executed locally on the mobile device, and the full offloading model, where jobs can abandon the WiFi Queue and be offloaded via the Cellular Queue . In both models, we minimize the Energy-Response time Weighted Product (ERWP) metric. Not surprisingly, we find that jobs abandon the queue often when the availability of the WiFi network is low. In general, for delay-sensitive applications the partial offloading model is preferred under a suitable reneging rate, while for delay-tolerant applications the full offloading model shows very good results and outperforms the other offloading model when selecting a large deadline. From the perspective of energy consumption, the full offloading model will always be best, even if the deadline must be extremely long. Only if job response time is of high importance an optimal deadline to abort offloading in the partial offloading model or the WiFi transmission in the full offloading model can be found. For reduction of the energy consumption it will always be better to wait longer rather than compute locally or use the cellular network.

ECONOMIC AND MATHEMATICAL MODELS FOR IMPLEMENTATION OF MULTIPLE STRATEGIES FOR DEVELOPMENT OF RADIOELECTRONIC WARFARE SYSTEM

The article presents economic and mathematical models for replacement of prototype of electronic warfare that allow to determine the optimal deadlines for replacement of prototypes in the aspect of costs associated with combat missions. The models corresponding to various strategies of development of the system of electronic warfare of the RF Armed Forces are reviewed, including traditional, innovative and breakthrough models. Implementation of the presented models in substantiating the prospects for development of the electronic warfare system makes it possible to rationally use the allocated resources.

Distributed Timely Throughput Optimal Scheduling for the Internet of Nano-Things

Nanotechnology is enabling the development of miniature devices able to perform simple tasks at the nanoscale. The interconnection of such nano-devices with traditional wireless networks and ultimately the Internet enables a new networking paradigm known as the Internet of Nano-Things (IoNT). Despite their promising applications, nano-devices have constrained power, energy, and computation capabilities along with very limited memory on board, which may only be able to hold one packet at once and, thus, requires packets to be delivered before certain hard deadlines. Toward this goal, a fully-distributed computation-light provably-correct scheduling/MAC protocol is introduced for bufferless nano-devices, which can maximize the network throughput, while achieving perpetual operation. More specifically, the proposed scheduling algorithm allows every nano-device to make optimal transmission decisions locally based on its incoming traffic rate, virtual debts, and channel sensing results. It is proven that the proposed algorithm is timely throughput optimal in the sense that it can guarantee reliable data delivery before deadlines as long as the incoming traffic rates are within the derived maximum network capacity region. This feature not only can lead to high network throughput for the IoNT, but also guarantees that the memory of each device is empty before the next packet arrives, thus addressing the fundamental challenge imposed by the extremely limited memory of nano-devices. In addition, the optimal deadline is derived, which guarantees that all the nano-devices can achieve perpetual communications by jointly considering the energy consumption of communications over the terahertz channel and energy harvesting based on piezoelectric nano-generators.

Control-Scheduling Codesign Exploiting Trade-Off between Task Periods and Deadlines

A control task’s performance heavily depends on its sampling frequency and sensing-to-actuation delay. More frequent sampling, that is, shorter period, improves the control performance. Similarly, shorter delay also has a positive effect. Moreover, schedulability is also a function of periods and deadlines. By taking into account the control performance and schedulability at the same time, this paper defines a period and deadline selection problem for fixed-priority systems. Our problem is to find the optimal periods and deadlines for given tasks that maximize the overall system performance. As our solution, this paper presents a novel heuristic algorithm that finds a high-quality suboptimal solution with very low complexity, which makes the algorithm practically applicable to large size task sets.

Optimal deadlines for agreements

Costly delay in negotiations can induce the negotiating parties to be more forthcoming with their information and improve the quality of the collective decision. Imposing a deadline may result in stalling, in which players at some point stop making concessions but switch back to conceding at the end, or a deadlock, in which concessions end permanently. Extending the deadline hurts the players in the first case, but is beneficial in the second. When the initial conflict between the negotiating parties is intermediate, the optimal deadline is positive and finite, and is characterized by the shortest time that allows efficient information aggregation in equilibrium.

Delay and Deadlines: Freeriding and Information Revelation in Partnerships

We study two sources of delay in teams: freeriding and lack of communication. Partners contribute to the value of a common project, but have private information about the success of their own efforts. When the deadline is far away, unsuccessful partners freeride on each others' efforts. When the deadline draws close, successful partners stop revealing their success to maintain their partners' motivation. We derive comparative statics results for common team performance measures and find that the optimal deadline maximizes productive efforts while avoiding unnecessary delays. Welfare is higher when information is only privately observable rather than revealed to the partnership.(JEL D82, D83, L26, M54, O30)

Delay and Deadlines: Freeriding and Information Revelation in Partnerships

We analyze the interaction between the incentives for free-riding and information revelation among partners in a dynamic setting. Partners contribute to the value of a common project, but have private information about the success of their own contribution efforts. The desire to maintain a partner's motivation to exert effort leads to a reluctance to share information which in turn affects the incentives to exert effort. Both free-riding and the lack of information revelation inefficiently delay project implementation. When the deadline for project implementation is far away, unsuccessful partners freeride on each other's efforts. When the deadline draws close, successful partners stop revealing their success in an attempt to incentivize other group members to continue to exert effort. Surprisingly, setting a tighter deadline may increase the expected time until the project is implemented, but may also increase the expected value of the project. We show that there is a unique finite optimal deadline that maximizes beneficial productive efforts while avoiding unnecessary delays. As long as the deadline is set optimally, welfare is higher when information is only privately observable rather than revealed to the entire partnership.

Ultimatum Deadlines

An important characteristic of any offer is the deadline at which it expires. We consider an ultimatum deadline game in which the proposer's decision variable is the offer deadline, while the responder faces a standard finite-horizon search problem. We show that the responder's strategy is characterized by a shortest acceptable deadline: at the time of deadline, he accepts an offer if the deadline is longer than his shortest acceptable deadline, and rejects it otherwise. If the proposer has all information available to the responder, the optimal deadline is the responder's shortest acceptable deadline. If the proposer is uncertain about the responder's situation, the optimal deadline gets longer, unless this uncertainty is very large. After normative analysis of the deadline setting problem, we present results from a behavioral study of the game. The average shortest acceptable deadline set by the responders equals the one that would maximize the expected value, whereas the proposers tend to set deadlines that are too short. The prescriptive conclusion for a proposer, emerging from the model and the experiment, is that in case of uncertainty it is better to set a deadline longer than what would be optimal if uncertainty were ignored.

Period sensitivity analysis and D–P domain feasibility region in dynamic priority systems

In the design of a real-time application it is fundamental to know how a change in the task parameters would affect the feasibility of the system. Relaxing the classical assumptions on static task sets with fixed periods and deadlines can give higher resource utilisation and better performance. But the changes on task parameters have to be done always maintaining feasibility. In practice, period and deadline modifications are only necessary on single tasks. Our work focuses on finding the feasibility region of deadlines and periods (called D–P feasibility region) for a single task in the context of dynamic, uniprocessor scheduling of hard real-time systems. This way, designers can choose the optimal deadline and period pairs that best fit application requirements. We provide an exact and an approximated algorithm to calculate this region. We will show that the approximated solution is very close to the exact one and it takes considerably less time.

A theory of optimal deadlines

This paper sets forth a model of contracting for delivery in an environment with time to build and adverse selection. The optimal contract is derived and characterized and it takes the form of a deadline contract. Such a contract stipulates a deadline for delivery for each possible type of agent efficiency. The optimal contract induces inefficient delay by using delivery time as a screening device. Furthermore, rents are increasing in the agent's efficiency. In meeting the deadline, the agent's effort is strictly increasing over time, due to discounting. It is shown that increasing the project's gross value decreases optimal delivery time, while increasing the scale or difficulty of the project increases it. Last, it is shown that the agent's rents are increasing in both project difficulty and gross project value.

Optimal deadline assignment for scheduling soft aperiodic tasks in hard real-time environments

We present a novel scheduling approach for servicing soft aperiodic requests in a hard real time environment, where a set of hard periodic tasks is scheduled using the Earliest Deadline First algorithm. The main characteristic of the proposed algorithm is that it achieves full processor utilization and optimal aperiodic responsiveness, still guaranteeing the execution of the periodic tasks. Another interesting feature of the proposed algorithm is that it can easily be tuned to balance performance versus complexity for adapting it to different application requirements. Schedulability issues, performance results, and implementation complexity of the algorithm are discussed and compared with other methods, such as Background, the Total Bandwidth Server, and the Slack Stealer. Resource reclaiming and extensions to more general cases are also considered. Extensive simulations show that a substantial improvement can be achieved with a little increase of complexity, ranging from the performance of the Total Bandwidth Server up to the optimal behavior.

On the role of patch density and patch variability in central-place foraging

Existing models of the foraging behavior of single-prey loaders in patchy environments differ on whether the optimal forager is predicted to stay in a patch until a prey is found, or to leave a patch for a next one if a prey is not found by a certain “deadline.” This article examines conditions on the probability distribution of prey density across patches that are necessary or sufficient for the existence of a finite, optimal deadline. It is shown that, for environments in which prey density is variable but never falls below some strictly positive level, a finite, optimal deadline exists when and only when the spatial density of patches is “high.” Also, a characterization is given of a large class of distributions (including the gamma distribution) for which a finite, optimal deadline exists for all levels of spatial density of patches.