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The IUP Journal of Operations Management :
Multi-Objective Optimization for Job Shop Scheduling Problem Using Genetic Algorithm
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Multi-objective optimization is presented in this paper for job shop scheduling problem. Coding is executed using MATLAB for tardiness and makespan. Genetic algorithm is implemented using MATLAB toolbox, and Pareto frontier is used to analyze the results. Benchmark is selected for simulation.

 
 

Job shop scheduling is the primal necessity of any manufacturing industry for productivity enhancement. Dispatching rules are used for job shop problem. Various optimization technics are developed to enhance the productivity for job shop problem. Genetic Algorithm (GA), Teaching Learning Based Optimization (TLBO), Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) are widely used optimization techniques to handle numerical data involved in job shop-related problems.

Advanced optimization methods are described in various research studies to deal with the numerical involved in job shop scheduling problem. These methods are Simulated Annealing (SA), GA, Tabu Search (TS), ACO, Particle Swarm Optimization (PSO) and ABC. These methods are also used for solving various industrial problems (Keesari and Rao, 2014).

 
 

Operations Management Journal,Genetic Algorithm (GA), Teaching Learning Based Optimization (TLBO),Colony Optimization (ACO) and Artificial Bee Colony (ABC), Simulated Annealing (SA), Particle Swarm Optimization (PSO) .