Dynamic Scheduling Using the Mixture of a Genetic Algorithm and a Priority Rule

Abstract

This paper presents a scheduling method using the mixture of a genetic algorithm and a priority rule to make effective schedules in a dynamic job shop scheduling environment. Dynamic scheduling problems are assumed to be a series of static problems and scheduled on a rolling basis. Each static problem is solved by a genetic algorithm in which genes are used for the priorities for next job selection. In the genetic algorithm, an effective priority rule is embedded to solve the static problems efficiently in a limited calculation time. The priorities for next job selection are calculated by multiplying two values for each operation of waiting jobs : the value of gene assigned for the operation and the value of priority calculated using the priority rule. It is possible to search schedules efficiently according to the scale of problems by tuning the weight between the genes of the genetic algorithm and the priorities calculated using the priority rule. Numerical experiments show the effectiveness of the proposed method.