Pre-specified Deadline Research Articles

Bicriteria Nash flows over time

Flows over time are a natural way to incorporate flow dynamics that arise in various applications such as traffic networks. In this paper we introduce a natural variant of the deterministic fluid queuing model in which users aim to minimize their costs subject to arrival at their destination before a pre-specified deadline. We determine the existence and the structure of Nash flows over time and fully characterize the price of anarchy for this model. The price of anarchy measures the ratio of the quality of the equilibrium and the quality of the optimum flow, where we evaluate the quality using two different natural performance measures: the throughput for a given deadline and the makespan for a given amount of flow. While it turns out that both prices of anarchy can be unbounded in general, we provide tight bounds for the important subclass of parallel path graphs.

Distributed Device-to-Device Offloading System: Design and Performance Optimization

In task offloading systems, it is imperative to guarantee that an offloaded task is completed within a pre-specified deadline. In this paper, we propose a distributed device-to-device (D2D) offloading system (DDOS) in which a task owner opportunistically broadcasts an offloading request that includes its mobility level and task completion deadline. After receiving the request, mobile devices in the vicinity of the task owner employ a constraint stochastic game to decide, in a distributed manner, whether to accept the request or not. We devise a best response dynamics-based algorithm (BRDA) to obtain a multi-policy constrained Nash equilibrium. Evaluation results demonstrate that DDOS can guarantee a high on-time task completion probability, as well as a low energy consumption.

A heuristic procedure for solving discrete time-cost tradeoff problems in repetitive projects

This paper studies a discrete time-cost tradeoff problem in repetitive projects, in which each activity has several alternative modes and must be scheduled in sequence from the first unit to the last unit. The objective is to minimize the total project cost consisting of direct costs and idle costs of all activities while meeting a pre-specified deadline. A heuristic procedure based on the linear programing relaxation is presented to solve this problem with the goal of finding high-quality solutions for medium-or large-size problems within a short period of time. Computational experiments are conducted to verify the effectiveness of the proposed procedure.

Joint optimal checkpointing and rejuvenation policy for real-time computing tasks

Performance of a software system can deteriorate from higher to lower levels due to software aging. To counteract the aging effect, software rejuvenation is widely implemented to restore the performance of a degraded system before the system crash actually takes place. To facilitate an effective system function restoration after each rejuvenation action, it is desirable to apply checkpointing to occasionally save the system state on a reliable storage so that the mission task can be resumed from the last saved checkpoint (instead of being restarted from the very beginning). As both rejuvenation and checkpointing procedures incur system overhead while bringing these benefits, it is significant to determine the optimal rejuvenation and checkpointing scheduling policy optimizing the system performance measures of interest. This paper makes new contributions by modeling and optimizing the joint maintenance policy involving state-based rejuvenation and periodic checkpointing schedule for software systems performing real-time computing tasks. The system can undergo multiple performance degradation levels or states, and transition time between different states can assume arbitrary types of distributions. The proposed solution methodology encompasses an efficient numerical algorithm for evaluating the probability of task completion (PTC) by a pre-specified deadline. The joint optimal rejuvenation and checkpointing policy is further determined to maximize the PTC of the considered real-time task. Examples are provided to illustrate applications of the proposed methodology as well as effects of system parameters on the optimization solution.

Design and implementation of an automated network monitoring and reporting back system

Abstract The task of network monitoring becomes tedious with increase in the network size, heterogeneity and complexity. The available network monitoring and management solutions are not only expensive but also difficult to use, configure and maintain. Manually pin pointing a faulty device in the large complex network is very tricky and time consuming for network administrators. Thus, an automated system is needed that immediately reports to network administrator about fault type and location as soon as it arises. This paper presents design and implementation of an intelligent network monitoring and reporting back system for large size organizations/industries. It is based on programming open-source tools (such as Nagios and RT) and intelligently integrating them to monitor network devices such as switches and routers. To monitor end-user devices in the network, a software package has been developed using Universal Plug & Play (UPnP) technology. The developed monitoring system immediately notifies network administrator as soon as a network problem arises. The notification message clearly pin points the fault location in network topology, its type and impact on rest of the network. If the network problem is not resolved within the pre-specified deadline, a second notification is sent out to the next responsible person. Thus, all people in the priority list are notified one by one with pre-specified deadlines until the problem is resolved.

Deadlines and infrequent monitoring in the dynamic provision of public goods

We consider a dynamic game of private provision of a discrete public good. In our model, a group of agents contributes to a project over time, which is completed once the cumulative contributions reach a threshold. Provided that this occurs prior to a prespecified deadline, each agent receives a lump-sum payoff. We show that a shorter deadline can induce the agents to raise their efforts, but no matter the length of the deadline, effort provision is inefficient due to the agents' frontloading incentives. Only if the agents do not monitor progress until the deadline are their frontloading incentives eliminated, so by committing to a deadline equal to the first-best completion time, it is possible to restore efficiency. Recognizing that deadlines are not renegotiation proof, we show that by committing to monitor progress to date at the first-best completion time, and then again at a sufficiently later date, efficiency can be attained. In this case, that monitoring date acts as a self-enforcing deadline.

A resource investment problem with time/resource trade-offs

In this study, we consider a Resource Investment Problem with time/resource trade-offs in project networks. We assume that there is a single renewable resource and the processing requirement of an activity can be reduced by investing extra resources. Our aim is to minimize the maximum resource usage, hence, the total amount invested for the single resource, while meeting the pre-specified deadline. We formulate the problem as a mixed integer linear model and find optimal solutions for small-sized problem instances. For large-sized problem instances, we propose a heuristic solution procedure. We develop several lower bounds and use them to evaluate the performance of our heuristic procedure. The results of our computational experiments have revealed the satisfactory behaviour of our optimality properties, lower bounds and heuristic procedure.

Team Research at the Biology–Mathematics Interface: Project Management Perspectives

The success of interdisciplinary research teams depends largely upon skills related to team performance. We evaluated student and team performance for undergraduate biology and mathematics students who participated in summer research projects conducted in off-campus laboratories. The student teams were composed of a student with a mathematics background and an experimentally oriented biology student. The team mentors typically ranked the students' performance very good to excellent over a range of attributes that included creativity and ability to conduct independent research. However, the research teams experienced problems meeting prespecified deadlines due to poor time and project management skills. Because time and project management skills can be readily taught and moreover typically reflect good research practices, simple modifications should be made to undergraduate curricula so that the promise of initiatives, such as MATH-BIO 2010, can be implemented.

Project time–cost trade-off scheduling: a hybrid optimization approach

This paper develops a hybrid approach for stochastic time–cost trade-off problem (STCTP) in PERT networks of project management, where activities are subjected to linear cost functions. The main objective of proposed approach is to improve the project completion probability in a prespecified deadline from a risky value to a confident predefined probability. To this end, we construct a nonlinear mathematical program with decision variables of activity mean durations, in which the objective function is concerned with minimization of project crashing direct cost. In order to solve the constructed model, we present a hybrid approach based on cutting plane method and Monte Carlo (MC) simulation. To illustrate the process of proposed approach, the approach was coded using MATLAB 7.6.0 and two illustrative examples are discussed. The results obtained from the computational study show that the proposed algorithm is an effective approach for the STCTP.

Scheduling deteriorating jobs to minimize the makespan on a single machine

Machine scheduling problems with deteriorating jobs have received increasing attention in recent years, mostly focusing on the linear deterioration models. However, if certain maintenance procedures fail to be completed prior to a prespecified deadline, jobs will require extra time for successful accomplishment in some situations. Therefore, this paper addresses a single-machine problem where the objective is to minimize the makespan under the piecewise linear deterioration model. A branch-and-bound algorithm and two heuristic algorithms are provided to search for the optimal solution and near-optimal solutions, respectively. Computational results are also presented to evaluate the performance of the proposed algorithms.

Efficient Repetitive Scheduling for High-Rise Construction

A new scheduling and cost optimization model for high-rise construction is presented in this paper. The model has been formulated with a unique representation of the activities that form the building's structural core, which need to be dealt with carefully to avoid scheduling errors. In addition, the model has been formulated incorporating: 1 the logical relationships within each floor and among floors of varying sizes; 2 work continuity and crew synchronization; 3 optional estimates and seasonal productivity factors; 4 prespecified deadline, work interruptions, and resource constraints; and 5 a genetic algorithms-based cost optimization that determines the combination of construction methods, number of crews, and work interruptions that meet schedule constraints. A computer prototype was then developed to demonstrate the model's usefulness on a case study high-rise project. The model is useful to both researchers and practitioners as it better suits the environment of high-rise construction, avoids scheduling errors, optimizes cost, and provides a legible presentation of resource assignments and progress data.

Scheduling jobs with step-deterioration; Minimizing makespan on a single- and multi-machine

Certain maintenance procedures fail to perform prior to a pre-specified deadline and need extra time for successful accomplishment. Processing times under this specific deterioriation are represented by a step function. We study the problem of makespan minimization of step-wise deteriorating jobs. We introduce compact integer programming formulations. We then focus on heuristic methods, since even the case of single machine single-step deterioration is NP-complete. These methods are extended to settings of multi-machine and multi-step deterioration. Our numerical tests indicate that the proposed algorithms are highly accurate and efficient in the context of a numerical study discussed in this paper.

Multi-stage scheduling for banks by mathematical programming

Despite moves toward electronic funds transfer systems, the United States banking industry must continue to cope with the paper processing requirements associated with a payments system still heavily reliant upon checks. Through the development of MICR (magnetic ink character recognition), computers have been utilized to sort documents and post customer accounts. However, a labor intensive key encoding function is required to prepare documents for processing. An attempt to eliminate the labor intensity is the recent development of optical scanning capture equipment which ‘reads’ printed or hand-written documents and inscribes them with MICR characters. However, the utilization of optical scanning technology requires the scheduling and coordination of five processing activities. A mathematical programming model has been developed which minimizes the combination of payroll and float costs while recognizing machine capacity constraints in the scheduling of optical capture systems. The formulation was specifically designed to respond to the bank check processing scheduling problem. However, with minor modifications, the model is directly transferable to any multi-echelon processing system where penalties are incurred when the units of output fail to meet prespecified deadlines.