Neighbourhood Based Robustness Applied to Tardiness and Total Flowtime Job Shops
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A planning and scheduling methodology for the virtual enterprise
Managing virtual web organizations in the 21st century
Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods
Journal of Scheduling
Evolutionary Scheduling: A Review
Genetic Programming and Evolvable Machines
Sensitivity bounds for machine scheduling with uncertain communication delays
Journal of Scheduling
Proceedings of the 38th conference on Winter simulation
A robust approach for the single machine scheduling problem
Journal of Scheduling
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Journal of Scheduling
A theoretic and practical framework for scheduling in a stochastic environment
Journal of Scheduling
Flexible solutions in disjunctive scheduling: General formulation and study of the flow-shop case
Computers and Operations Research
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A hybrid approach to large-scale job shop scheduling
Applied Intelligence
An immune genetic algorithm based on bottleneck jobs for the job shop scheduling problem
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Worst-case evaluation of flexible solutions in disjunctive scheduling problems
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part III
Computers and Operations Research
Schedule execution for two-machine flow-shop with interval processing times
Mathematical and Computer Modelling: An International Journal
Computers and Operations Research
Hi-index | 0.00 |
In this paper we study the weighted tardiness job-shop scheduling problem, taking into consideration the presence of random shop disturbances. A basic thesis of the paper is that global scheduling performance is determined primarily by a subset of the scheduling decisions to be made. By making these decisions in an a priori static fashion, which maintains a global perspective, overall performance efficiency can be achieved. Further, by allowing the remaining decisions to be made dynamically, flexibility can be retained in the schedule to compensate for unforeseen system disturbances. We develop a decomposition method that partitions job operations into an ordered sequence of subsets. This decomposition identifies and resolves a "crucial subset" of scheduling decisions through the use of a branch-and-bound algorithm. We conduct computational experiments that demonstrate the performance of the approach under deterministic cases, and the robustness of the approach under a wide range of processing time perturbations. We show that the performance of the method is superior, particularly for low to medium levels of disturbances.