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52
A Computational Study on Bounding the Makespan Distribution in Stochastic Project Networks
 ANNALS OF OPERATIONS RESEARCH
, 1998
"... Given a stochastic project network with independently distributed activity durations, several approaches to bound the distribution function of the project completion time have been proposed. We have implemented the most promising of these algorithms and compare their behavior on a basis of nearly 20 ..."
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Cited by 30 (1 self)
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Given a stochastic project network with independently distributed activity durations, several approaches to bound the distribution function of the project completion time have been proposed. We have implemented the most promising of these algorithms and compare their behavior on a basis of nearly 2000 instances with up to 1200 activities of different testbeds. We propose a suitable numerical representation of the given distributions which is the basis for excellent computational results.
2002, Stability and resource allocation in project planning
"... The majority of resourceconstrained project scheduling efforts assumes perfect information about the scheduling problem to be solved and a static deterministic environment within which the precomputed baseline schedule is executed. In reality, project activities are subject to considerable uncerta ..."
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Cited by 26 (19 self)
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The majority of resourceconstrained project scheduling efforts assumes perfect information about the scheduling problem to be solved and a static deterministic environment within which the precomputed baseline schedule is executed. In reality, project activities are subject to considerable uncertainty, which generally leads to numerous schedule disruptions. In this paper, we present a resource allocation model that protects a given baseline schedule against activity duration variability. A branchandbound algorithm is developed that solves the proposed resource allocation problem. We report on computational results obtained on a set of benchmark problems. (project management; project planning; scheduling; resource allocation; constraint propagation) 1
Proactive algorithms for job shop scheduling with probabilistic durations
 Journal of Artificial Intelligence Research
"... Most classical scheduling formulations assume a fixed and known duration for each activity. In this paper, we weaken this assumption, requiring instead that each duration can be represented by an independent random variable with a known mean and variance. The best solutions are ones which have a hig ..."
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Cited by 24 (2 self)
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Most classical scheduling formulations assume a fixed and known duration for each activity. In this paper, we weaken this assumption, requiring instead that each duration can be represented by an independent random variable with a known mean and variance. The best solutions are ones which have a high probability of achieving a good makespan. We first create a theoretical framework, formally showing how Monte Carlo simulation can be combined with deterministic scheduling algorithms to solve this problem. We propose an associated deterministic scheduling problem whose solution is proved, under certain conditions, to be a lower bound for the probabilistic problem. We then propose and investigate a number of techniques for solving such problems based on combinations of Monte Carlo simulation, solutions to the associated deterministic problem, and either constraint programming or tabu search. Our empirical results demonstrate that a combination of the use of the associated deterministic problem and Monte Carlo simulation results in algorithms that scale best both in terms of problem size and uncertainty. Further experiments point to the correlation between the quality of the deterministic solution and the quality of the probabilistic solution as a major factor responsible for this success. 1.
The use of buffers in project management: The tradeoff between stability and makespan
 International Journal of Production Economics
, 2005
"... During execution projects may be subject to considerable uncertainty, which may lead to numerous schedule disruptions. Recent research efforts have focused on the generation of robust project baseline schedules that are protected against possible disruptions that may occur during schedule execution. ..."
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Cited by 22 (11 self)
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During execution projects may be subject to considerable uncertainty, which may lead to numerous schedule disruptions. Recent research efforts have focused on the generation of robust project baseline schedules that are protected against possible disruptions that may occur during schedule execution. The fundamental research issue we address in this paper is the potential tradeoff between the quality robustness (measured in terms of project duration) and solution robustness (stability, measured in terms of the deviation between the planned and realized start times of the projected schedule). We provide an extensive analysis of the results of a simulation experiment set up to investigate whether it is beneficial to concentrate safety time in project and feeding buffers, or whether it is preferable to insert time buffers that are scattered throughout the baseline project schedule in order to maximize schedule stability.
A Novel Branch and Bound Algorithm for Scheduling Flowshop Plants with Uncertain Processing Times
 Computers and Chemical Engineering
, 2001
"... We address the problem of scheduling a flowshop plant with uncertain process ing times described by discrete probability distributions. The objective is to find a sequence of batches that minimizes the expected makespan. To circumvent the prob lem of combinatorially explosive state spaces, we p ..."
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Cited by 15 (3 self)
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We address the problem of scheduling a flowshop plant with uncertain process ing times described by discrete probability distributions. The objective is to find a sequence of batches that minimizes the expected makespan. To circumvent the prob lem of combinatorially explosive state spaces, we propose a novel and rigorous branch and bound algorithm that provides the optimal solution and is based on the result that a lower bound to the expected makespan can be obtained by evaluating over an aggregated probability model. Numerical results for a number of example problems show that the solution times for the proposed method are several orders of magnitude smaller than those for a multiperiod model. In addition, an important extension of this method is proposed for the case of continuous probability distributions of certain forms, using discretization schemes that give excellent approximations.
Linear Preselective Policies for Stochastic Project Scheduling
, 1998
"... We introduce a new class of policies for stochastic project scheduling with resource constraints, the linear preselective policies. They combine the benefits of two known classes of scheduling policies for stochastic and deterministic scheduling, viz. priority policies and preselective policies. Pri ..."
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Cited by 13 (1 self)
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We introduce a new class of policies for stochastic project scheduling with resource constraints, the linear preselective policies. They combine the benefits of two known classes of scheduling policies for stochastic and deterministic scheduling, viz. priority policies and preselective policies. Priority policies solve resource conflicts by means of a priority list (list scheduling). They have several computational benefits but suffer from the wellknown Graham anomalies. On the other hand, preselective policies possess favorable properties such as continuity and monotonicity and thus avoid these anomalies, but computing optimal such policies requires excessive computation time. The new class of linear preselective policies is a subclass of the class of preselective policies. Like priority policies, linear preselective policies use priority lists for their definition. They thus inherit all favorable properties of preselective policies but are far better computationally tractable. We st...
R&DProject Scheduling when Activities May Fail
"... An R&D project typically consists of several stages. Due to technological risks, the project may have to be terminated before completion, each stage having a specific likelihood of success. In the projectplanning andscheduling literature, this technological uncertainty has typically been ignor ..."
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Cited by 12 (6 self)
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An R&D project typically consists of several stages. Due to technological risks, the project may have to be terminated before completion, each stage having a specific likelihood of success. In the projectplanning andscheduling literature, this technological uncertainty has typically been ignored and project plans are developed only for scenarios in which the project succeeds. In this paper we examine how to schedule projects in order to maximize their expected net present value when the project activities have a probability of failure and when an activity’s failure leads to overall project termination. We formulate the problem, show that it is NPhard, develop a branchandbound algorithm that allows to obtain optimal solutions and provide extensive computational results. In the process, we establish a complexity result for an open problem in singlemachine scheduling, namely for the discounted weightedcompletiontime objective with general precedence constraints.
Resourceconstrained project scheduling for timely project completion
, 2007
"... with stochastic activity durations ..."
Scheduling optimization under uncertainty: An alternative approach
 Computers and Chemical Engineering
, 2003
"... The prevalent approach to the treatment of processing time uncertainties in production scheduling problems is through the use of probabilistic models. Apart from requiring detailed information about probability distribution functions, this approach also has the drawback that the computational expens ..."
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Cited by 11 (0 self)
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The prevalent approach to the treatment of processing time uncertainties in production scheduling problems is through the use of probabilistic models. Apart from requiring detailed information about probability distribution functions, this approach also has the drawback that the computational expense of solving these models is very high. In this work, we present a nonprobabilistic treatment of scheduling optimization under uncertainty, based on concepts from fuzzy set theory and interval arithmetic, to describe the imprecision and uncertainty in the task durations. We first provide a brief review on the fuzzy set approach, comparing it with the probabilistic approach. We then present MILP models derived from applying this approach to two different problems flowshop scheduling and new product development process scheduling. Results indicate that these MILP models are computationally tractable for reasonably sized problems. We also describe tabu search implementations in order to handle larger problems. 1