Genetic Algorithm For Job-shop Scheduling Problem Research Articles

An Improved GA for Job-Shop Scheduling Problem

This paper introduces an improved genetic algorithm (GA) to solve the job-shop scheduling problem. A typical case is illustrated to report on how it uses the encoding method to address the problem and to work out the optimal schedules. This method has been tested with ten groups of samples generated randomly. The results show its outperformance to the traditional GA on solving the same problem in terms of best, worst and average ability.

The Effect of Crossover and Mutation Operators on Genetic Algorithm for Job Shop Scheduling Problem

Job Shop Scheduling Problem (JSSP) is a famous NP-hard problem in scheduling field. The concentration of JSSP is to find a feasible scheduling plan to figure out the earliest completion time under machine and processing sequence constraints. At present, genetic algorithm has been widely adopted in varies of operation research problems including JSSP, and good performance have been achieved. However, few work have stress the selection of varies operators when implemented for JSSP. Using benchmark problems, this paper compares the effect of crossover and mutation operators on genetic algorithm for JSSP.

Hybrid Genetic Algorithm (GA) for job shop scheduling problems and its sensitivity analysis

The Job Shop Scheduling Problem (JSSP) is a hard combinatorial optimisation problem. This paper presents a heuristic-based Genetic Algorithm (GA) or Hybrid Genetic Algorithm (HGA) with the aim of overcoming the GA deficiency of fine tuning of solution around the optimum, and to achieve optimal or near optimal solutions for benchmark JSSP. The paper also presents a detail GA parameter analysis (also called sensitivity analysis) for a wide range of benchmark problems from JSSP. The findings from the sensitivity analysis or best possible parameter combination are then used in the proposed HGA for optimal or near optimal solutions. The experimental results of the HGA for several benchmark problems are encouraging and show that HGA has achieved optimal solutions for more than 90% of the benchmark problems considered in this paper. The presented results will provide a reference for selection of GA parameters for heuristic-based GAs for JSSP.

Genetic Algorithm with Local Search for Job Shop Scheduling Problem

The job shop scheduling problem (JSSP) is a well-known difficult combinatorial optimization problem. This paper presents a hybrid genetic algorithm for the JSSP with the objective of minimizing makespan. A new generation alternation model of genetic algorithm for JSSP is designed. Every pair of randomly selected parents must pass either crossover or mutation, which are deployed in parallel. In the inner structure of crossover, mutation is partially embedded. Schedules are constructed using a procedure that generates full active schedules. In each generation, a local search heuristic based on the neighborhood structure proposed by Nowicki and Smutnicki is applied to improve the solutions. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.