Timing control points via simulation for production systems under random disturbances

Golenko‐Ginzburg, Dimitri; Laslo, Zohar

doi:10.1016/s0378-4754(00)00199-3

Cited by 5 publications

(8 citation statements)

References 8 publications

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“…The design of a project monitoring system has been mentioned in the literature as a vital part of project management efforts to achieve projects’ objectives [ 3 , 5 , 6 , 7 ]. According to these studies, the need for monitoring the project plan arises due to the nature of projects executed in dynamic environments.…”

Section: Literature Reviewmentioning

confidence: 99%

“…From the results presented in Table 3 we can now calculate the project duration’s empirical distribution. For example, in the shortest realizations (with

), the project duration is 6.18 time units, thus within the range of [ 6 , 7 ]. Accordingly, the probability of the value lying in this range is estimated empirically as

.…”

Section: Implementation Examplementioning

confidence: 99%

“…Accordingly, the probability of the value lying in this range is estimated empirically as

. Similarly, one can estimate the probability of the project’s duration being within the range [ 7 , 8 ] as

; within the range [ 8 , 9 ] as

; and within the range [ 10 , 11 ] as

Using the estimated probabilities, the base (initial) entropy,

, which measures the uncertainty prior to the project initiation, is calculated as follows:

…”

Section: Implementation Examplementioning

confidence: 99%

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Project Management Monitoring Based on Expected Duration Entropy

Kashi

Rozenes

Ben‐Gal

2020

Entropy

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Projects are rarely executed exactly as planned. Often, the actual duration of a project’s activities differ from the planned duration, resulting in costs stemming from the inaccurate estimation of the activity’s completion date. While monitoring a project at various inspection points is pricy, it can lead to a better estimation of the project completion time, hence saving costs. Nonetheless, identifying the optimal inspection points is a difficult task, as it requires evaluating a large number of the project’s path options, even for small-scale projects. This paper proposes an analytical method for identifying the optimal project inspection points by using information theory measures. We search for monitoring (inspection) points that can maximize the information about the project’s estimated duration or completion time. The proposed methodology is based on a simulation-optimization scheme using a Monte Carlo engine that simulates potential activities’ durations. An exhaustive search is performed of all possible monitoring points to find those with the highest expected information gain on the project duration. The proposed algorithm’s complexity is little affected by the number of activities, and the algorithm can address large projects with hundreds or thousands of activities. Numerical experimentation and an analysis of various parameters are presented.

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Section: Literature Reviewmentioning

confidence: 99%

“…From the results presented in Table 3 we can now calculate the project duration’s empirical distribution. For example, in the shortest realizations (with

), the project duration is 6.18 time units, thus within the range of [ 6 , 7 ]. Accordingly, the probability of the value lying in this range is estimated empirically as

.…”

Section: Implementation Examplementioning

confidence: 99%

“…Accordingly, the probability of the value lying in this range is estimated empirically as

. Similarly, one can estimate the probability of the project’s duration being within the range [ 7 , 8 ] as

; within the range [ 8 , 9 ] as

; and within the range [ 10 , 11 ] as

Using the estimated probabilities, the base (initial) entropy,

, which measures the uncertainty prior to the project initiation, is calculated as follows:

…”

Section: Implementation Examplementioning

confidence: 99%

See 1 more Smart Citation

Project Management Monitoring Based on Expected Duration Entropy

Kashi

Rozenes

Ben‐Gal

2020

Entropy

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show abstract

“…In the literature, the problem of proper inspection frequency is mostly avoided by considering a pre-established total number of inspection points. However, there is scant research addressing this issue, mostly in the context of possible reduction of the total number of the inspection points (Friedman et al, 1989;Golenko-Ginzburg & Gonik, 1997;Golenko-Ginzburg & Laslo, 2001). For example, Golenko-Ginzburg & Laslo (2001) proposed an optimization procedure that can be realized with different execution modes.…”

Section: Project Inspection Pointsmentioning

confidence: 99%

“…However, there is scant research addressing this issue, mostly in the context of possible reduction of the total number of the inspection points (Friedman et al, 1989;Golenko-Ginzburg & Gonik, 1997;Golenko-Ginzburg & Laslo, 2001). For example, Golenko-Ginzburg & Laslo (2001) proposed an optimization procedure that can be realized with different execution modes. At each inspection point the model faces a stochastic optimization problem where the objective is to minimize the number of inspection points, subject to a chanceconstrained completion deadline.…”

Section: Project Inspection Pointsmentioning

confidence: 99%

Enhancing Project on Time Within Budget Performance by Implementing Proper Control Routines

Laslo

Gurevich

2014

ManageFon

Self Cite

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Project management focuses on the technical specifications of a project and how those specifications can be met within the cost, profit, time, safety, and quality constraints that the contract with the client has imposed on the enterprise (Clark & Colling, 2005). Large projects are often characterized by a combination of uncertainty and ambiguity related to goals and tasks, as well as the complexities that emanate from their size and the numerous dependencies between different activities and the environment. Among the factors likely to change the existing plan are the revisions of work content estimates, changes in technical specifications, technical difficulties, delivery failures, weather conditions, and labor unrest. Research has proved that routines, even if cost-effective, do not work well in situations of high uncertainty. For example, the studies of Van de Ven et al., (1976) and Keller (1994) show that most routines work properly in low uncertainty situations, and Kraut & Streeter (1995) have clarified that under high uncertainty the control routine used in routine production environments may not suffice. Turner (1993) and Pinto (2007) clarify that risk management has a positive effect on project success in terms of delivery of contractual commitments on time and within budget. Control and coordination are closely intertwined concepts in classic organization theory (Parker, 1984). Recent findings confirm that the implementation of a control routine and coordination system influences task completion competency and thus, project management performance (Liu et al., 2010). A control routine involves: 1) monitoring that serves to establish the need to control the tendency to deviate from the planned trajectory while there is still time to take corrective actions (Lock, 1987), 2) analyzing the situations at each inspection point using present views, and 3) deciding which corrective actions should be implemented if reestablishment of project targets is required. Sacks et al., (2005) claimed that the control of time, cost, and quality is performed almost exclusively manually, with the result that it is expensive, approximate, and commonly delivered with a time lag that does not allow an effectively closed control loop. The chances of successfully achieving the time and cost objectives during the course of implementation of a project are slim indeed, unless an adequate level of control followed by coordination, if required, is exercised throughout its lifecycle. Packendorf (1995), Kerzner (1998), Thiry (2004) and many others claimed that effective project management requires extensive inspection and internal coordination. Management 2014/72 Prevalent control routines are generally based on traditional deterministic models that in situations of short time float and scanty budgets are often unable to deliver complex projects "on time within budget." Because it does not take uncertainties into consideration, the deterministic approach grossly misleads management into thinking that the likelihood of delivery on time wi...

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Using the generalized maximum covering location model to control a project’s progress

Sabeghi

Tareghian

2018

Comput Manag Sci

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Timing control points via simulation for production systems under random disturbances

Cited by 5 publications

References 8 publications

Project Management Monitoring Based on Expected Duration Entropy

Project Management Monitoring Based on Expected Duration Entropy

Enhancing Project on Time Within Budget Performance by Implementing Proper Control Routines

Using the generalized maximum covering location model to control a project’s progress

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