Unified Genetic Algorithm Approach for Solving Flexible Job-Shop Scheduling Problem

This paper proposes a novel genetic algorithm (GA) approach that utilizes multichromosome to solve the flexible job-shop scheduling problem (FJSP), which involves two kinds of decisions: machine selection and operation sequencing. Typically, former is represented by string categorical values, whereas latter forms sequence operations. Consequently, chromosome conventional GAs for solving FJSP consists part sequential part. Since these parts are different from each other, operators required using GAs. In contrast, this unified GA enables application an identical crossover strategy in both parts. order implement approach, evolved applying candidate order-based (COGA), can use traditional strategies such as one-point or two-point crossovers. Such also be used evolve Thus, we handle manner if points both. study, was extend existing COGA (u-COGA), FJSPs. Numerical experiments reveal u-COGA useful FJSPs with complex structures.