Analysis of Various Alternate Crossover Strategies for Genetic Algorithm to Solve Job Shop Scheduling Problems

In the manufacturing system, optimization of scheduling plays a vital role for effective utilization of resources. Efficient utilization of resources to improve the outcome of manufacturing system is the complex job shop scheduling. In recent years much attention has been given to solve the combinatorial optimization problems like Job Shop Scheduling using meta-heuristic approaches. This paper proposes a new approach to solve a Job Shop Scheduling Problem (JSSP). This paper introduces Order Preserving Multi Point Crossover (OPMPX), Unordered Subsequence Exchange Crossover (USXX) and Ordered Partially Mapped Crossover (OPMX) of Genetic Algorithm (GA). The above algorithms are experimented with the JSSP to analyze the performance. The objective of the proposed method is to minimize the makespan and flowtime with a variety of constraints. Job Scheduling is the process of completing jobs over a time with allocation of shared resources. The result and performance of the proposed work is tested with wellknown bench mark problems and also compared with the other conventional algorithms.