Optimizing Manufacturing Material Handling With Simulation-Based Heuristic: A Case Study Of A Semiconductor Manufacturing Factory

Abstract

In this paper, the material handling manufacturing is studied including the operation allocation of machine and the material handling operation employee assignment and addressed via a case study from a semiconductor manufacturing company. The goal of this project is to create and incorporate methodical approaches to the material handling planning issue. Two methods are used to formulate the solution: simulation-based heuristic optimization and MILP. wherein MILP serves as a benchmark for the best optimal solution for the outcomes of the simulation optimization. Due to demand uncertainty and stochastic input, complex problems like material handling can be addressed by simulation based optimization models with algorithms. Additionally, the study uses a simulation model in conjunction with three distinct algorithms - Genetic Algorithm, and Simulated Annealing - to determine which solution is the most ideal. Python programming will be used to run the simulation optimization while CPLEX programming will run the MILP model. The findings demonstrate that the suggested approach is successful in solving the material handling issue and has the potential to significantly advance this sector.