R&amp;D project scheduling when activities may fail

Reyck, Bert De; Leus, Roel

doi:10.1080/07408170701413944

Cited by 68 publications

(47 citation statements)

References 37 publications

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“…Thus, this article extends the work of Buss and Rosenblatt [6], Benati [5], Sobel et al [34] and Creemers et al [10], who focus on duration risk only, and of Schmidt and Grossmann [33], Jain and Grossmann [21] and De Reyck and Leus [12], who look into technical risk only (although Schmidt and Grossmann [33] also explore the possibility of introducing multiple discrete duration scenarios).…”

Section: Introductionmentioning

confidence: 95%

“…De Reyck and Leus [12] study project scheduling with known activity durations but with uncertain activity outcomes. In that article, if an activity A ends no later than the start of another activity B then knowledge of the outcome (success or failure) of A can sometimes be used to avoid incurring the cost for B, since a failure in A would allow abandoning the project, but payment for B cannot be avoided when B has already started before the outcome of A is discovered.…”

Section: Policy Classmentioning

confidence: 99%

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Project planning with alternative technologies in uncertain environments

Creemers

Reyck

Leus

2015

European Journal of Operational Research

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Abstract:We investigate project scheduling with stochastic activity durations to maximize the expected net present value. Individual activities also carry a risk of failure, which can cause the overall project to fail. In the project planning literature, such technological uncertainty is typically ignored and project plans are developed only for scenarios in which the project succeeds. To mitigate the risk that an activity's failure jeopardizes the entire project, more than one alternative may exist for reaching the project's objectives. We propose a model that incorporates both the risk of activity failure and the possible pursuit of alternative technologies. We find optimal solutions to the scheduling problem by means of stochastic dynamic programming. Our algorithms prescribe which alternatives need to be explored, and how they should be scheduled. We also examine the impact of the variability of the activity durations on the project's value.

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Section: Introductionmentioning

confidence: 95%

Section: Policy Classmentioning

confidence: 99%

Project planning with alternative technologies in uncertain environments

Creemers

Reyck

Leus

2015

European Journal of Operational Research

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“…In this paper, we only consider 'sequential' schedules, in which jobs are processed one-at-a-time, and we therefore do not explicitly consider job durations. As pointed out in De Reyck and Leus (2008), when cash flows (costs and payoff) are not discounted, or more generally when the cash flows are time-independent, then it is a dominant decision to not schedule jobs in parallel. A second motivation for restricting the analysis to sequential schedules only is the possible use of a scarce (bottleneck) resource that can perform only one job at a time, e.g., expensive equipment or highly skilled labor.…”

Section: Definitionsmentioning

confidence: 99%

“…Precedence constraints between modules can be regulatory or technical in nature. Regulatory constraints often occur to protect testers or consumers; when testing a new drug, for instance, the toxicity has to be tested (e.g., via animal testing) before clinical tests on humans are allowed (De Reyck and Leus 2008). An example of a technical constraint is the impossibility of evaluating a new car design in the wind tunnel before a prototype is manufactured for this test.…”

Section: Introductionmentioning

confidence: 99%

A fast greedy heuristic for scheduling modular projects

et al. 2014

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This article describes a heuristic for scheduling so-called 'modular' projects. Exact solutions to this NP-hard problem can be obtained with existing branch-andbound and dynamic-programming algorithms, but only for small to medium-size instances. The proposed heuristic, by contrast, can be used even for large instances, or when instances are particularly difficult because of their characteristics, such as a low network density. The proposed heuristic draws from existing results in the literature on sequential testing, which will be briefly reviewed. The performance of the heuristic is assessed over a dataset of 360 instances.

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“…activities which may fail during the project execution (De Reyck, Leus 2008). (Creemers et al 2013) present a method considering technological and duration uncertainty.…”

Section: Decision Project Graphs With Uncertain Parametersmentioning

confidence: 99%

Innovative Projects Scheduling with Scenario-Based Decision Project Graphs

Gaspars-Wieloch

2017

Proceedings of 5th International Scientific Conference Contemporary Issues in Business, Management and Education ‘2017

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Abstract. Decision project graphs -DPG are treated as a combination of deterministic and stochastic networks. They are designed for projects where at some stages at least one activity from a set of alternative activities is supposed to be performed. A given alternative task may differ from other activities belonging to this set in respect of times, costs and even sets of successors. Decision project graphs are used in project planning, scheduling and management. Due to the fact that especially in the case of innovative projects many factors are not completely known before the project execution, the DPG issue has been already investigated both under certainty and uncertainty. In this contribution we present a novel scenario-based DPG rule which takes into consideration possible scenarios, dependent activity durations, the decision-maker's attitude towards risk and the distribution of parameter values connected with particular activities. The procedure is especially designed for totally new (innovative) projects where it is complicated to estimate probabilities of particular scenarios since no historical data are available. The decision rule is assisted with an optimization model which can be easily solved with the use of diverse optimization computer tools. The model may support both reactive and proactive project management.

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R&D project scheduling when activities may fail

Cited by 68 publications

References 37 publications

Project planning with alternative technologies in uncertain environments

Project planning with alternative technologies in uncertain environments

A fast greedy heuristic for scheduling modular projects

Innovative Projects Scheduling with Scenario-Based Decision Project Graphs

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