GA applications to physical distribution scheduling problem

A physical distribution system has a number of optimization problems. Most of them belong to a combinatorial problem, to which conventional mathematical programming methods may hardly be applied. This paper reports on two applications of the genetic algorithm (GA) to physical distribution scheduling problems, which arise at real physical distribution centers. The developed GA schedulers took the place of conventional schedulers, which were coded by rule-based technologies. Advantages of the introduction of GA schedulers into the physical distribution system are as follows: (1) the GA becomes a general problem-solver engine. Once we develop this engine, we only have to develop interfaces for the applications; and (2) fitness functions necessary for the GA force the physical distribution schedulers to have approximate performance estimation. This was not taken into consideration when the rule-based scheduler was used. Two applications of the discussed schedulers were implemented with real distribution centers, and they brought much efficiency to their management.