An algorithm for optimal scheduling and risk assessment of projects

Zafra-Cabeza, Ascensión; Ridao, Miguel A.; Camacho, Eduardo F.

doi:10.1016/j.conengprac.2004.03.016

Cited by 25 publications

(12 citation statements)

References 10 publications

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“…Another way to incorporate the uncertainty in the optimization problem is to minimize the risk resulting from the uncertain inflows. Previous work considers risk in optimization and control problems (Zafra-Cabeza, Ridao, & Camacho, 2004; Zafra-Cabeza, Ridao, Camacho, Kempf, & Rivera, 2007). In the case presented in this article, however, the risk itself is the direct result of an uncertain disturbance that varies in time.…”

Section: Introductionmentioning

confidence: 89%

Multiple Model Predictive Control on a drainage canal system

Overloop

Weijs

Dijkstra

2008

Control Engineering Practice

112

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Section: Introductionmentioning

confidence: 89%

Multiple Model Predictive Control on a drainage canal system

Overloop

Weijs

Dijkstra

2008

Control Engineering Practice

112

View full text Add to dashboard Cite

“…Gone et al [4] proposed a time-disturbance analysis method to optimize the utilization of activity floats, so that the delay of the project schedule can be minimized; Zafra-Cabeza et al [5] adopted a "p-timed Petri net" to represent the risk measure of an activity and developed a method to reduce the impact of risks; Yi and Langford [6] analyzed the safety disasters occurred in the past to develop a safety planning method for the project safety manager to focus on the concentration of safety risks in the project. Kilic et al [7] proposed a Genetic Algorithm to optimize the multiobjective optimization problem of project scheduling, considering four attributes: indirect costs, operation costs, risk reduction costs, and delay punishment costs; Schatteman et al [8] proposed a computer-aided risk management system to identify, analyze, and quantity the major risk factors, and to estimate their impacts to the project schedule so that risk reduction actions are planned; Zafra-Cabeza et al [9] conducted a Monte Carlo simulation of High-tech factory construction project using risk optimization techniques to optimize project cost and schedule.…”

Section: Integration Of Risk Management and Schedulingmentioning

confidence: 99%

A Risk-Based Critical Path Scheduling Method (II) : A Visual Approach System Using BIM

Chang¹,

Yu²,

Cheng³

2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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The traditional construction schedule planning methods are mostly based on the project activities list broken down by the experienced engineers who primarily analyze the work content of construction drawings, and the activity durations estimated by simulating the construction process according to the conditions of the construction site. However, such an approach does not only give hard time the new engineers without experience, but is also unable to capture the dynamic risk factors while estimating the construction duration. As a result, the planned schedule is usually inaccurate and ineffective. To tackle the abovementioned problem, this paper presents a BIM-based Visual Risk critical path Scheduling System (BIM-VRcpSS) to assist the construction scheduler in developing a more accurate and effective schedule. The proposed BIMVRcpSS provides functions of risk-based Critical Path Scheduling Method (R-CPSM) based on seven different risk levels. It also equips the construction schedulers with a 3D BIM visual simulation tool to better estimate the activity durations compared with the traditional 2D approaches. Moreover, with the aid of a cloud data based system, the construction schedulers are able to fine-tune duration estimates by considering the impacts of different risk factors without spatial and time limitations. As such, the proposed BIM-VRcpSS can increase the duration estimation accuracy of construction activities, and improve the traditional scheduling approaches.

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“…Risk management is a relatively new perspective that was first applied to natural disasters [12], but in the last decade has been extended to the fields of project management and financial policies, in which it is generating a growing interest [13], [14]. Risk management can be summarized as the identification of risks, their ranking or prioritization, the resolution of those deemed significant and their monitoring through their applicable life.…”

Section: Introductionmentioning

confidence: 99%

A Risk-Based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health

Zafra-Cabeza

Rivera

Ridao

et al. 2011

IEEE Trans. Contr. Syst. Technol.

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This paper examines how control engineering and risk management techniques can be applied in the field of behavioral health through their use in the design and implementation of adaptive behavioral interventions. Adaptive interventions are gaining increasing acceptance as a means to improve prevention and treatment of chronic, relapsing disorders, such as abuse of alcohol, tobacco, and other drugs, mental illness, and obesity. A risk-based Model Predictive Control (MPC) algorithm is developed for a hypothetical intervention inspired by Fast Track, a real-life program whose long-term goal is the prevention of conduct disorders in at-risk children. The MPC-based algorithm decides on the appropriate frequency of counselor home visits, mentoring sessions, and the availability of after-school recreation activities by relying on a model that includes identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. MPC is particularly suited for the problem because of its constraint-handling capabilities, and its ability to scale to interventions involving multiple tailoring variables. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this paper can increase intervention effectiveness and adherence while reducing waste, resulting in advantages over conventional fixed treatment. A series of simulations are conducted under varying conditions to demonstrate the effectiveness of the algorithm.

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An algorithm for optimal scheduling and risk assessment of projects

Cited by 25 publications

References 10 publications

Multiple Model Predictive Control on a drainage canal system

Multiple Model Predictive Control on a drainage canal system

A Risk-Based Critical Path Scheduling Method (II) : A Visual Approach System Using BIM

A Risk-Based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health

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