Complexity of scheduling parallel task systems
SIAM Journal on Discrete Mathematics
Approximation Algorithms for the Discrete Time-Cost Tradeoff Problem
Mathematics of Operations Research
Scheduling under uncertainty: bounding the makespan distribution
Computational Discrete Mathematics
An approximation algorithm for scheduling malleable tasks under general precedence constraints
ACM Transactions on Algorithms (TALG)
Machine scheduling with resource dependent processing times
Mathematical Programming: Series A and B
A survey of scheduling with controllable processing times
Discrete Applied Mathematics
The discrete time/cost trade-off problem: extensions and heuristic procedures
Journal of Scheduling
Hardness of approximation of the discrete time-cost tradeoff problem
Operations Research Letters
A branch-and-price algorithm for multi-mode resource leveling
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
Tight approximation algorithms for scheduling with fixed jobs and nonavailability
ACM Transactions on Algorithms (TALG)
Approximation algorithms for scheduling and packing problems
WAOA'11 Proceedings of the 9th international conference on Approximation and Online Algorithms
Faster approximation algorithms for scheduling with fixed jobs
CATS '11 Proceedings of the Seventeenth Computing: The Australasian Theory Symposium - Volume 119
Faster approximation algorithms for scheduling with fixed jobs
CATS 2011 Proceedings of the Seventeenth Computing on The Australasian Theory Symposium - Volume 119
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Large-scale maintenance in industrial plants requires the entire shutdown of production units for disassembly, comprehensive inspection, and renewal. We derive models and algorithms for this so-called turnaround scheduling that include different features such as time-cost trade-off, precedence constraints, external resource units, resource leveling, different working shifts, and risk analysis. We propose a framework for decision support that consists of two phases. The first phase supports the manager in finding a good makespan for the turnaround. It computes an approximate project time-cost trade-off curve together with a stochastic evaluation. Our risk measures are the expected tardiness at time t and the probability of completing the turnaround within time t. In the second phase, we solve the actual scheduling optimization problem for the makespan chosen in the first phase heuristically and compute a detailed schedule that respects all side constraints. Again, we complement this by computing upper bounds for the same two risk measures. Our experimental results show that our methods solve large real-world instances from chemical manufacturing plants quickly and yield an excellent resource utilization. A comparison with solutions of a mixed-integer program on smaller instances proves the high quality of the schedules that our algorithms produce within a few minutes.