Proactive Scheduling Research Articles

Investigation of surrogate measures of robustness for project scheduling problems

Uncertainties in systems and data necessitate plans that are flexible enough to be applicable in a real-time production environment. Such plans find a compromise between optimality and flexibility, frequently termed as robustness. Time is a well-known criterion for evaluating optimality of a schedule, but in the case of robustness, additional surrogate measures are required. These surrogate measures of robustness aid in identifying proactive schedules, which can provide flexible yet efficient plans with minimum redundancy. This study suggests new robustness measures and proposes a test bench for the systematic analysis of such criteria. Regression analysis was used for comparison of the suggested measures with traditional slack-based measures. The results show that float index (a novel measure) has a strong correlation with tardiness and is an effective measure to determine the robust nature of any schedule.

Proactive and Reactive Multi-Project Scheduling in Uncertain Environment

In this paper, a proactive and reactive multi-project scheduling problem is addressed. This problem is related to the influences of uncertain factors, which leads to a deviation between actual scheduling and baseline scheduling, and a recovery strategy is established in order to generate a baseline scheduling scheme. This paper introduces a proactive multi-project scheduling sub-model. When the activity is interrupted, the proactive scheduling scheme is used as the baseline scheduling scheme, which is embedded in the reactive scheduling, and then, the reactive scheduling sub-model is established. The proposed model can be used to generate alternative schedules, and to meet this need, a genetic simulated annealing algorithm is proposed. A buffer change operator (SC) and a crossover operator are designed in a genetic simulated annealing algorithm so that in the early stages of the algorithm, an optimum individual is produced and protected. The performance comparison shows that the genetic simulated annealing algorithm significantly outperforms the previous algorithms.

A Generic Spatiotemporal UAV Scheduling Framework for Multi-Event Applications

In this paper, a generic scheduling framework to manage a fleet of micro unmanned aerial vehicles (UAVs) is proposed. The objective is to employ multiple UAVs in sequential and parallel ways to cover spatially and temporally distributed events in a geographical area of interest over a long period of time. The proactive scheduling framework considers several constraints and challenges, including the technical specifications of the UAVs and the limited battery capacities. In addition, the platform considers the necessity to regularly send back the UAVs to a docking station for battery recharging. A mixed integer nonlinear programming problem aiming at minimizing the total energy consumption and the number of employed UAVs is formulated to guarantee the non-redundant exploitation of the resources. Afterward, a series of linearization steps are introduced to convert the problem into a mixed integer linear programming one so that it can be optimally solved. To reduce the complexity of the problem, a dynamic time horizon discretization approach adapted to the characteristics of the problem is performed beforehand. The proposed UAV scheduling framework is formulated in a generic manner and can be applied in multiple domains comprising short- and/or long-term UAV missions while ensuring uninterrupted service.

P4-TPG: Accelerating Deterministic Parallel Test Pattern Generation by Preemptive, Proactive, and Preventive Schedulings

According to the prior research, a deterministic parallel test pattern generation (TPG) engine was realized and generated the same test pattern set the serial automatic test pattern generation does during acceleration. However, for retaining the determinism, tremendous idle time is observed when different tasks (either dependent or independent) were synchronized among threads. Therefore, a new deterministic parallel TPG engine called P4-TPG is developed and incorporates preemptive, proactive, and preventive schedulings to further save/reuse the idle time during acceleration. In P4-TPG, preemptive scheduling first modifies the thread flow and brings forward, as many latter tasks as possible, to the idle time. Next, proactive scheduling inserts prospective TPG tasks of unprocessed faults to the remaining idle time and increases the overall utilization of threads. Last, preventive scheduling dynamically skips faults incompatible with the working pattern per thread and shortens the fault list during fault compaction. The experimental results show that P4-TPG not only generates the same test pattern set as the serial TPG does but also achieves averagely $10.36\times $ speedups, is 96.6% better than the prior research, using 12 threads on 18 benchmark circuits.

Future UAV-Based ITS: A Comprehensive Scheduling Framework

Multi-rotor unmanned aerial vehicles (UAVs) have been recently recognized as one of the top emerging technologies to be utilized in various smart city domains such as intelligent transportation systems (ITS). They represent an innovative mean to complement existing technologies to surveil transportation network, control traffic, and monitor incidents. The UAVs usually operate for time-limited missions due to their limited battery capacity. Hence, they need to frequently return to their docking stations to recharge their batteries, which handicaps their mission coverage and performance. When designing a UAV-based ITS infrastructure, it is crucial to leverage the UAV fleet effectively. In this paper, a generic management framework of UAVs for ITS applications is developed. The problem of docking/charging station placement is first investigated to find optimized locations for a given number of stations to be installed by the ITS operator. To this end, two fundamental criteria are taken into account: i) the flying time required by the UAV to reach the mission/incident location and ii) the risk of battery failure during the UAV operation. The two algorithms, namely a penalized weighted k-means algorithm and the particle swarm optimization algorithm, are designed for this purpose. Once the docking stations are placed, a UAV scheduling program is formulated to optimally cover the pre-known missions while minimizing the total energy consumption of the fleet and respecting a coverage efficiency targeted by the ITS operator. The proposed proactive scheduling approach employs multiple UAVs in sequential and parallel ways to cover spatially and temporally distributed events in the geographical area of interest over a long period.

Towards Proactive Scheduling of Microgrids Against Extreme Floods

This letter proposes a proactive scheduling for resilience enhancement of microgrids (MGs). Just ahead of the flood arrival, the MG is shifted to a state less impacted and stressed by the upcoming event. To do this, vulnerable components are first recognized. Tripping out all vulnerable components, proactive measures are employed to minimize the preventive load curtailment. The solution is then applied as the proactive schedule of MG which is robust against the flood threat. The effectiveness of the proposed method is examined on a real-scale MG.

A Correlation Driven Approach with Edge Services for Predictive Industrial Maintenance.

Predictive industrial maintenance promotes proactive scheduling of maintenance to minimize unexpected device anomalies/faults. Almost all current predictive industrial maintenance techniques construct a model based on prior knowledge or data at build-time. However, anomalies/faults will propagate among sensors and devices along correlations hidden among sensors. These correlations can facilitate maintenance. This paper makes an attempt on predicting the anomaly/fault propagation to perform predictive industrial maintenance by considering the correlations among faults. The main challenge is that an anomaly/fault may propagate in multiple ways owing to various correlations. This is called as the uncertainty of anomaly/fault propagation. This present paper proposes a correlation-based event routing approach for predictive industrial maintenance by improving our previous works. Our previous works mapped physical sensors into a soft-ware-defined abstraction, called proactive data service. In the service model, anomalies/faults are encapsulated into events. We also proposed a service hyperlink model to encapsulate the correlations among anomalies/faults. This paper maps the anomalies/faults propagation into event routing and proposes a heuristic algorithm based on service hyperlinks to route events among services. The experiment results show that, our approach can reach 100% precision and 88.89% recall at most.

An agent-based simulation system for multi-project scheduling under uncertainty

This work proposes an agent-based simulation system to address challenges in resource constrained multi-project scheduling under uncertainty. The system aims at helping users develop robust schedules by taking care of intra- and inter-dependencies between projects, conflicting demands for limited resources, and the potential knock-on effect of unpredictable events. With the interactive graphical user interface embedded in the system, users are enabled to investigate project process, evaluate modified schedule, forecast impact resulted from unexpected disruptions, and conduct various what-if analysis. Reactive and proactive scheduling algorithms have been developed in the simulation system to resolve unforeseen disruptions and increase the robustness of the solutions. Experiments on a real-world case study of a technology-intensive industrial product development program have been conducted to evaluate the effectiveness of the proposed algorithms.

Risk-Aware Proactive Scheduling via Conditional Value-at-Risk

In this paper, we consider the challenging problem of riskaware proactive scheduling with the objective of minimizing robust makespan. State-of-the-art approaches based on probabilistic constrained optimization lead to Mixed Integer Linear Programs that must be heuristically approximated. We optimize the robust makespan via a coherent risk measure, Conditional Value-at-Risk (CVaR). Since traditional CVaR optimization approaches assuming linear spaces does not suit our problem, we propose a general branch-and-bound framework for combinatorial CVaR minimization. We then design an approximate complete algorithm, and employ resource reasoning to enable constraint propagation for multiple samples. Empirical results show that our algorithm outperforms state-of-the-art approaches with higher solution quality.

Does knowledge on diabetes management influence glycemic control? A nationwide study in patients with type 1 diabetes in Brazil.

ObjectiveThe purpose of this study is to establish demographic and clinical data associated with the knowledge on diabetes management and its influence on glycemic control in patients with type 1 diabetes.MethodsThis was a retrospective, observational, multicenter study conducted with 1,760 patients between August 2011 and August 2014 in 10 cities of Brazil.ResultsOverall, 1,190 (67.6%) patients knew what glycated hemoglobin (HbA1c) means. These patients were older, had longer disease duration, longer follow-up in each center, reported lower frequency of self-reported hypoglycemia, and were more frequently Caucasians and at glycemic goal. Multivariate analysis showed that knowledge on what HbA1c means was related to more years of school attendance, self-reported ethnicity (Caucasians), severe hypoglycemia, economic status, follow-up time in each center, and participation on diabetes educational programs. Good glycemic control was related to older age, more years of school attendance, higher frequency of daily self-monitoring of blood glucose, higher adherence to diet, and knowledge on what HbA1c means.ConclusionPatients with a knowledge on what HbA1c means had a better chance of reaching an adequate glycemic control that was not found in the majority of our patients. Diabetes care teams should rethink the approaches to patients and change them to more proactive schedules, reinforcing education, patients’ skills, and empowerment to have positive attitudes toward reaching and maintaining a better glycemic control. Finally, the glucocentric approach to diabetes management should be changed to actions that include patients’ psychosocial aspects aiming to reduce the stress of living with diabetes, improving glycemic control, and avoiding adverse outcomes.

Metaheuristic Algorithms for Proactive and Reactive Project Scheduling to Minimize Contractor’s Cash Flow Gap under Random Activity Duration

This paper investigates the project scheduling problem to minimize the maximum cash flow gap of contractors under random activity duration. A comprehensive optimization model with time buffers added to the baseline schedule by project proactive scheduling and the schedule adjustment cost determined by project reactive scheduling is constructed to manage disruptions caused by the randomness of activity durations. Due to the problem’s intractability, two hybrid metaheuristic algorithms, namely, tabu simulated annealing (tabu-SA) and variable neighborhood tabu search (VNTS), are developed, and several improvement measures are proposed to enhance the algorithms’ performance. Based on a randomly generated data set, a computational experiment is performed to evaluate the algorithms, with the effects on results of certain key parameters also analyzed. The conclusions are as follows: tabu-SA outperforms VNTS for large-scale problems, however, the reverse holds for small-scale problems. The key parameters, including the project deadline, the cost per time buffer, the activity instability weight, the activity duration variability, the number of payments, and the payment proportion can exert important effects on the contractor’s maximum cash flow gap.

The proactive and reactive resource-constrained project scheduling problem

Uncertainty has become an inevitable aspect of project scheduling. We study the resource-constrained project scheduling problem with stochastic durations. One of the most studied approaches to deal with stochastic durations is that of proactive and reactive scheduling. Previous researches often studied proactive and reactive scheduling rather separately and ignored the fact that proactive scheduling and reactive scheduling are closely connected. In this paper, we address this ignored aspect by formulating an integrated proactive and reactive scheduling problem with a combined cost function which includes a baseline schedule cost as well as costs of a series of reactions. We introduce solutions to this integrated problem as proactive-and-reactive policies (PR-policies). We discuss that PR-policies are more powerful and informative than their traditional counterparts (i.e., a combination of a baseline schedule and a reactive policy), provide better stability and robustness, and are more flexible when extra constraints are added to the problem. We also propose four dynamic programming based models (Models 1–4) that solve the problem to optimality over different classes of PR-policies. We compare our models with each other and with a combination of a traditional proactive approach (namely, the starting time criticality heuristic) and a reactive approach (namely, the robust parallel schedule generation scheme). Computational results show that Model 2 outperforms the traditional solution only when reaction costs are greater than zero. Moreover, Model 3 and Model 4 clearly outperform Model 1 and Model 2 in all settings and the traditional solution in most of the settings.

Proactive and reactive resource-constrained max-NPV project scheduling with random activity duration

This paper addresses the resource-constrained project problem in which activity durations are stochastic variables and the objective is to maximize the net present value of cash flow in the project. First, using the two classical time buffering methods, proactive scheduling optimization models are constructed to generate the robust schedules. Then, two reactive scheduling models with different objectives are proposed to adjust the baseline schedules when disruptions occur during their execution. For the NP-hardness of the studied problem, three heuristic algorithms, including tabu search (TS), variable neighbourhood search (VNS), and a mixed version of VNS and TS, are developed and compared with the multi-start iteration improvement algorithm through a computational experiment conducted on a randomly generated data set. In addition, based on the computational results obtained, the effects of several key parameters on the proactive and reactive scheduling results are analysed, and some managerial insights are obtained. The research in this paper has practical implications for contractors to improve the project profit in an uncertain environment.

Joint Stochastic Geometry and Mean Field Game Optimization for Energy-Efficient Proactive Scheduling in Ultra Dense Networks

In this paper, we investigate the energy efficiency performance of optimal proactive scheduling strategies in the context of ultra-dense networks. The network consists of a superposition of homogeneous Poisson point process, whereas the users requests follow a space-time homogeneous point process. The objective is to define the optimal transmission powers at any time, that allows to completely serve every user request, while minimizing the total consumed energy. We also assume the system has predictive knowledge about the future transmission contexts. The problem is cast as a dynamic stochastic game which is hard to solve in ultra-dense networks, due to a complex coupling in the interference term, the large number of elements interacting, as well as uncertainties on the channel dynamics, interference, and future requests. Our contribution first lies in addressing the inherent complexity issue of the optimization, by transitioning into an equivalent and more tractable mean field game. Second, we propose to combine this mathematical framework with elements of stochastic geometry. The numerical simulations provide good insights on notable performance gains in terms of energy efficiency, compared to reference scheduling strategies. Additional simulations harnessing the impact of future knowledge uncertainty on the performance of the proposed strategies are also provided.

RePro-Active: a reactive–proactive scheduling method based on simulation in cloud computing

Cloud computing is a scalable computing infrastructure in which the number of resources and requests change dynamically. There are usually a huge number of tasks and resources in cloud computing. A scheduler does allocating resources to tasks, which is an operation with a large number of parameters that is of NP-hard problems. Approaches such as metaheuristic, simulation-based optimization (SBO), predictive algorithms, etc. are applied to mitigate the complexity of scheduling. Reactive scheduling methods are able to adapt their behavior based on a feedback loop from runtime environment, while proactive scheduling methods try to predict future events to adapt their behavior as well. These algorithms suffer from two problems: (1) they require additional information, like the execution time of tasks that are not usually available in practice and (2) they use the history of past activities that is not easy to maintain and process in order to make future decisions. To address the problems, this paper presents a reactive/proactive scheduling framework, dubbed RePro-Active that presents an iterative reactive/proactive scheduling algorithm called RePro-Active.SB runs periodically. It includes the algorithm called ReactiveScheduling to support reactive behavior, and the algorithms called ProactiveSSLB/ProactiveSSELB to reinforce proactive actions in which the algorithm called Simulate predicts future by simulating possible prospective events. First, the presented scheduling algorithms have the least dependence on prior knowledge about tasks. They are extracted from the category of round-robin methods that are more realistic and do not need extra information about the tasks that is not available in practice. Second, they also begin from current conditions (rather than relying on the history of data) and use SBO techniques that try to simulate possible prospective events to make better decisions. In order to realize the idea, RePro-Active is used to improve both task scheduling and load balancing in the cloud-computing environment. In comparison to the base methods, the results indicate that the completion time of tasks decreased by 30%, and average resource utilization ratio increased by 20%; while, throughput increased by 19%.

Proactive Scheduling in Cloud Computing

Autonomic fault aware scheduling is a feature quite important for cloud computing and it is related to adoption of workload variation. In this context, this paper proposes an fault aware pattern matching autonomic scheduling for cloud computing based on autonomic computing concepts. In order to validate the proposed solution, we performed two experiments one with traditional approach and other other with pattern recognition fault aware approach. The results show the effectiveness of the scheme.

A Framework for Proactive Resource Provisioning in IaaS Clouds

Cloud computing is an emerging technology for rapidly provisioning and releasing resources on-demand from a shared resource pool. When big data is analyzed/mined on the cloud platform, the efficiency of resource provisioning would affect the system performance. This work proposes a framework for proactive resource provisioning in IaaS (Infrastructure as a Service) clouds to improve system performance. The proposed framework consists of the virtual cluster computing system, the profiling system, the resource management system, and the monitoring system. In this framework, the over-commit mechanism is applied to improve resource utilization. Furthermore, a proactive task scheduling approach is also present to prevent the postponement of tasks in critical stages, especially when the amount of aggregated resources requested by virtual machines exceeds that of available resources on the over-committed physical machines. Experimental results show that the over-commit approach indeed improves the resource utilization. However, when the degree of applying the over-commit approach increases, the burden of this proposed approach also conceivably increases. Therefore, the proposed framework further applies the proactive task scheduling approach to execute the time-critical tasks earlier to shorten the processing time. A small-scale cloud system including 3 servers is built for experiments. Preliminary experimental results show the performance improvement of our proposed framework in IaaS clouds.

A New Proactive Scheduling Methodology for Front-End Crude Oil and Refinery Operations under Uncertainty of Shipping Delay

Crude oil planning and scheduling is crucial to petroleum refineries because of the potential for significant economic benefits. In reality, however, crude oil scheduling activities are highly vulnerable to disruptions caused by various uncertainties. In this paper, a new proactive scheduling methodology simultaneously covering crude unloading, transferring, and processing (CUTP) has been developed. The CUTP scheduling methodology is to maximize the total profit subject to various inventory, operation, transportation, and production constraints, while maintaining the refinery normal operating conditions under the uncertainty of crude shipping delays. To quantify shipping delay uncertainties, a combined feasibility index has been developed and embedded into the developed continuous-time mixed-integer nonlinear programming scheduling model, which is solved by the global solver of ANTIGONE. In addition, the relationship between the total profit and the minimum flexibility threshold is also given. The efficacy of this study has been demonstrated by industrial-scale case studies.

The impact of overtime as a time-based proactive scheduling and reactive allocation strategy on the robustness of a personnel shift roster

The workforce size and the overtime budget have an important impact on the total personnel costs of an organisation. Since the personnel costs represent a significant fraction of the operating costs, it is important to define an appropriate hiring and overtime policy. Overtime is defined as an extension of the daily working time or the total working time over the planning period. In this paper, we make the distinction between scheduled and unscheduled overtime when we define the overtime policy. Scheduled overtime is proactively assigned in the baseline personnel roster whereas unscheduled overtime is allocated as a reactive strategy to overcome operational disruptions. The hiring and overtime policy undoubtedly influence the robustness included in a personnel roster, i.e., the capability of an organisation to deal with roster disruptions at an acceptable cost. In this paper, we investigate the trade-off between the hiring budget and the overtime budget and the way overtime should be allocated in the personnel management process. The latter comprehends a trade-off between the proactive scheduling of overtime and the reservation of overtime to balance supply and demand in response to operational variability. Insights are obtained by exploring three different strategies to compose a personnel shift baseline roster. We verify the robustness of each of these strategies by applying a three-step methodology that thrives on optimisation and simulation and we formulate some managerial guidelines to define an appropriate hiring and overtime policy.

Implementation of a drainage information, analysis and management system

Abstract An integrated drainage information, analysis and management system (DIAMS) was developed and implemented for the New Jersey Department of Transportation (NJDOT). The purpose of the DIAMS is to provide a useful tool for managers to evaluate drainage infrastructure, to facilitate the determination of the present costs of preserving those infrastructures, and to make decisions regarding the optimal use of their infrastructure budgets. The impetus for DIAMS is the culvert information management system (CIMS), which is developed to manage the data for culvert pipes. DIAMS maintains and summarizes accumulated inspection data for all types of drainage infrastructure assets, including pipes, inlet/outlet structures, outfalls and manufactured treatment devices. DIAMS capabilities include identifying drainage infrastructure, maintaining inspection history, mapping locations, predicting service life based on the current condition states, and assessing present asset value. It also includes unit cost values of 72 standard items to estimate the current cost for new assets with the ability to adjust for future inflation. In addition, DIAMS contains several different repair, rehabilitation and replacement options to remedy the drainage infrastructure. DIAMS can analyze asset information and determine decisions to inspect, rehabilitate, replace or do nothing at the project and network levels by comparing costs with risks and failures. Costs may be optimized to meet annual maintenance budget allocations by prioritizing drainage infrastructure needing inspection, cleaning and repair. DIAMS functional modules include vendor data uploading, asset identification, system administration and financial analysis. Among the significant performance feature of DIAMS is its proactive nature, which affords decision makers the means of conducting a comprehensive financial analysis to determine the optimal proactive schedule for the proper maintenance actions and to prioritize them accordingly. Benefits of DIAMS include long-term savings that accrue by adopting optimized preventive maintenance strategies and facilitating compliance with Governmental Accounting Standards Board (GASB) and federal storm water regulations.