The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A discrete event simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization to achieve multiple conflicting objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. In MO-SHGA each individual in each population acts as an input scenario of simulation. Also, it is very difficult to assign weights to the objective function in the traditional multi-objective GA because of pareto fronts. Therefore, we have proposed a probabilistic based linearization and multi-objective to single objective conversion method at population evolution phase. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA. The proposed research is useful for the development of synchronized human based assembly lines for real time monitoring, optimization, and control.

1. Introduction
 2. Literature Review
 3. Development of Mathematical Model
  3.1 Problem Statement
  3.2 Model Assumptions
  3.3 Notations
 4. Multi-Objective Simulation IntegratedHybrid Genetic Algorithm(MO-SHGA)
  4.1 The Proposed Multi-Objective Approach forMO-GA and MO-SHGA
  4.2 Model Behavior
  4.3 Numerical Example
 5. Results and Discussion
  5.1 CASE I : Deterministic Multi-ObjectiveSimulation Integrated Hybrid GeneticAlgorithm (MO-SHGA)
  5.2 CASE-II : Stochastic Multi-ObjectiveSimulation Integrated Hybrid GeneticAlgorithm (MO-SHGA)
 6. Conclusions
 Acknowledgment
 References

• 무하마드 임란(한양대학교 산업경영공학과) | Muhammad Imran (Department of Industrial & Management Engineering, Hanyang University)
• 강창욱(한양대학교 산업경영공학과) | Changwook Kang (Department of Industrial & Management Engineering, Hanyang University) Corresponding author