Crossover and Mutation Strategies applied in Job Shop Scheduling Problems

Abstract

Abstract This paper reviews modern genetic algorithm based approaches for solving job shop scheduling problems. This paper elaborates the types of crossover methods such as Partially Mapped crossover (PMX), Order One Crossover (OX), etc involved in Genetic Algorithm (GA) that are used for Job Shop Scheduling problems. Job Shop Scheduling represents one of the hardest combinatorial optimization problems where number of possible schedules drastically increases with the number of operations and machines. Normal crossover operators will often lead to inadmissible solution . Many specialized combining order or adjacency information from the two parents. The crossover method operators builds an offspring by choosing a subsequence of elements from one parent and preserving the order and position of as many elements as possible from the other parent. A subsequence of elements is selected by choosing two random cut points, which serve as boundaries for the swapping operations. There are few distinct mutation operators widely used for JSS problems such as swap, inversion, insertion (shift) and displacement mutation. Here some modern genetic algorithm-based approaches from the literature are also discussed as well.