Sequencing JIT mixed-model assembly lines
Management Science
Level schedules for mixed-model, Just-in-Time processes
Management Science
The scheduling of maintenance service
Discrete Applied Mathematics
Distance-Constrained Scheduling and Its Applications to Real-Time Systems
IEEE Transactions on Computers
Time Slot Allocation for Real-Time Messages with Negotiable Distance Constrains
RTAS '98 Proceedings of the Fourth IEEE Real-Time Technology and Applications Symposium
Stride Scheduling: Deterministic Proportional- Share Resource Management
Stride Scheduling: Deterministic Proportional- Share Resource Management
Journal of Scheduling
Scheduling Commercial Videotapes in Broadcast Television
Operations Research
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Lottery scheduling: flexible proportional-share resource management
OSDI '94 Proceedings of the 1st USENIX conference on Operating Systems Design and Implementation
Introducing dynamic diversity into a discrete particle swarm optimization
Computers and Operations Research
Solving the Response Time Variability Problem by means of Multi-start and GRASP metaheuristics
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Solving the response time variability problem by means of a psychoclonal approach
Journal of Heuristics
Using aggregation to reduce response time variability in cyclic fair sequences
Journal of Scheduling
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
On a periodic maintenance problem
Operations Research Letters
| Hi-index | 0.00 |
The response time variability problem (RTVP) is an NP-hard scheduling problem that has been studied intensively recently and has a wide range of real-world applications in mixed-model assembly lines, multithreaded computer systems, network environments and others. The RTVP arises whenever products, clients or jobs need to be sequenced in order to minimise the variability in the time between two successive points at which they receive the necessary resources. To date, the best exact method for solving this problem is a mixed integer linear programming (MILP) model, which solves to optimality most of instances with up to 40 units to be scheduled in a reasonable amount of time. The goal of this paper is to increase the size of the instances that can be solved to optimality. We have designed an algorithm based on the branch and bound (B&B) technique to take advantage of the particular features of the problem. Our computational experiments show that the B&B algorithm is able to solve larger instances with up to 55 units to optimality in a reasonable time.