Markov and Markov-regenerative PERT networks
Operations Research
On generating all maximal independent sets
Information Processing Letters
Introduction to algorithms
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Data Structures, Algorithms, & Software Principles in C
Data Structures, Algorithms, & Software Principles in C
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Scheduling under uncertainty: Optimizing against a randomizing adversary
APPROX '00 Proceedings of the Third International Workshop on Approximation Algorithms for Combinatorial Optimization
Diagnosing Double Regular Systems
Diagnosing Double Regular Systems
RanGen: A Random Network Generator for Activity-on-the-Node Networks
Journal of Scheduling
Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods
Journal of Scheduling
Parallel scheduling of complex dags under uncertainty
Proceedings of the seventeenth annual ACM symposium on Parallelism in algorithms and architectures
Scheduling Markovian PERT networks to maximize the net present value
Operations Research Letters
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In this paper, we model a research-and-development project as consisting of several modules, with each module containing one or more activities. We examine how to schedule the activities of such a project in order to maximize the expected profit when the activities have a probability of failure and when an activity's failure can cause its module and thereby the overall project to fail. A module succeeds when at least one of its constituent activities is successfully executed. All activities are scheduled on a scarce resource that is modeled as a single machine. We describe various policy classes, establish the relations among them, develop exact algorithms to optimize over two different classes (one dynamic program and one branch-and-bound algorithm), and examine the computational performance of the algorithms on two randomly generated instance sets.