Hj. Liu et Jj. Dong, DISPATCHING RULE SELECTION USING ARTIFICIAL NEURAL NETWORKS FOR DYNAMIC PLANNING SCHEDULING, Journal of intelligent manufacturing, 7(3), 1996, pp. 243-250
Citations number
18
Categorie Soggetti
Controlo Theory & Cybernetics","Engineering, Manufacturing","Computer Science Artificial Intelligence
To schedule a job shop, the first task is to select an appropriate sch
eduling algorithm or rule. Because of the complexity of scheduling pro
blems, no general algorithm sufficient for solving all scheduling prob
lems has yet been developed. Most job-shop scheduling systems offer al
ternative algorithms for different situations, and experienced human s
chedulers are needed to select the best dispatching rule in these syst
ems. This paper proposes a new algorithm for jobshop scheduling proble
ms. This algorithm consists of three stages. First, computer simulatio
n techniques are used to evaluate the efficiency of heuristic rules in
different scheduling situations. Second, the simulation results are u
sed to train a neural network in order to capture the knowledge which
can be used to select the most efficient heuristic rule for each sched
uling situation. Finally, the trained neural network is used as a disp
atching rule selector in the realtime scheduling process. Research res
ults have shown great potential in using a neural network to replace h
uman schedulers in selecting an appropriate approach for real-time sch
eduling. This research is part of an ongoing project of developing a r
eal-time planning and scheduling system.