A branch & bound algorithm for the open-shop problem
GO-II Meeting Proceedings of the second international colloquium on Graphs and optimization
A tabu search algorithm for the open shop scheduling problem
Computers and Operations Research
Open Shop Scheduling to Minimize Finish Time
Journal of the ACM (JACM)
A bee colony optimization algorithm to job shop scheduling
Proceedings of the 38th conference on Winter simulation
Computers and Operations Research
A new particle swarm optimization for the open shop scheduling problem
Computers and Operations Research
An efficient flow-shop scheduling algorithm based on a hybrid particle swarm optimization model
Expert Systems with Applications: An International Journal
A hybrid genetic algorithm for no-wait job shop scheduling problems
Expert Systems with Applications: An International Journal
An alternate two phases particle swarm optimization algorithm for flow shop scheduling problem
Expert Systems with Applications: An International Journal
Proceedings of the 40th Conference on Winter Simulation
Artificial bee colony algorithm: a survey
International Journal of Advanced Intelligence Paradigms
Hi-index | 12.05 |
Open shop scheduling problems (OSSP) are one of the most time-consuming works in scheduling problems. Currently, many artificial intelligence algorithms can reduce the problem-solving time to an acceptable time range, and even can further downsize the range of solution space. Although the range of solution space is technically downsized, in most scheduling algorithms every partial solution still needs to be completely solved before this solution can be evaluated. For example, if there is a schedule with 100 operations, then all 100 operations must be scheduled before the scheduler can evaluate its fitness. Therefore, the time-cost of unnecessary partial solutions is no longer saved. In order to improve the weakness stated above, this paper proposes a new bee colony optimization algorithm, with an idle-time-based filtering scheme, according to the inference of ''the smaller the idle-time, the smaller the partial solution'', and the ''smaller the makespan (Cmax) will be''. It can automatically stop searching a partial solution with insufficient profitability, while the scheduler is creating a new scheduling solution, and therefore, save time-cost for the remaining partial solution. The architecture and details of the bee colony optimization heuristic rule is detailed in this paper.