An application of genetic algorithms for general dynamic lotsizing problems

This paper presents an application of genetic algorithms for dynamic lotsizing problems, including the implementation methodology and the testing results of the algorithms. Currently, most of the existing studies for dynamic lotsizing problems concentrate on heuristic lot-sizing techniques which only consider some simple production structures and/or simple external demands structures. In this paper, the general dynamic lot-sizing problems are considered, which are characterized by the fact that each stage may have several predecessor and/or successor stages, all the items can have independent requirements, and/or all the cost parameters can be time-varying. A genetic algorithm for the problems is introduced, which attempts to heuristically optimize under all the conditions simultaneously. As to my knowledge,this genetic algorithm is the first one capable of solving such general dynamic lotsizing problems. In order to apply genetic algorithm, a coding scheme for lotsize plan/schedule is given and a feasibility routine is presented. In computational experiments, this genetic algorithm performed extremely well. It is concluded that the genetic algorithm is efficient and effective for dynamic lotsizing problems.