抄録
Tank lorry scheduling is fundamental in the management of oil delivery facilities. A set of lorries is scheduled in a cost effective way to meet oil demands from a number of gas stations. The complexity of the scheduling arises from the diversity of constraints on oil delivery environments. The paper studies a lorry scheduling system alternative to a human scheduler taking an example of actual oil delivery facilities. Recent development in artificial intelligence leads to the use of knowledge based techniques : A method so called filtered beam search is applied to reduce combinatorially explosive search spaces. A number of combinations are omitted from the solution using the fact that most gas stations are scattered along with main roads. Another method which is used for multiattribute decision analysis is applied to evaluating candidates for a feasible solution. Each is characterized by the following four attributes : vacant lorry space, assigned delivery time, assigned lorry type, and the number of lorry hatches. A prototype system is developed for the lorry scheduling in the actual oil delivery facility. The schedules produced by the system are compared with those created by the human scheduler to verify the usefulness of the system.
