AN EXPERIMENT IN SCHEDULING AND PLANNING OF NONSTRUCTURED JOBS - LESSONS LEARNED FROM ARTIFICIAL-INTELLIGENCE AND OPERATIONAL-RESEARCH TOOLBOX

Most scheduling problems traditionally address well defined and struct ured environments. Some examples include manufacturing (job shop, flow shop, etc.) and project scheduling respectively. Another type of sche duling problem that has received little or no attention is defined her e as a non-structured scheduling problem (NSSP). A typical NSSP addres sed here involves scheduling aircraft turnaround functions. The schedu ling method consists of artificial intelligence (AI) and operational r esearch (OR) techniques. The results obtained from the hybrid model in dicate that flexibility and knowledge replication can be achieved at v arious levels of abstraction by converting non-structured problems to their structured equivalents. The model is implemented with TOP (a Tas k Oriented Planner), a decision support system for multiagent task sch eduling and planning.