Enhancing value of simulations and improving decision making using process automation and prescriptive analytics
CHALLENGE - The goal of this study is to optimize the assembly line to maximize Profitability, Total Throughput, and Same-day Shipment, while minimizing Work In-Progress, subject to a maximum allowed limit on Staff Utilization. With six stations and three different staffing levels, the computer assembly simulation model simulates the computer assembly from order logging to shipping. There are sixteen independent variables that can be varied to optimize this model. Mathematically you can have 138,240,000 scenarios that can be set for the above parameter set. Considering the computational expense, it would be quite time-consuming to run all of these scenarios to identify best solutions.
SOLUTION - Prescriptive analytics, relying on optimization algorithms and data analytics, can help us examine the decision space, and identify the optimal solutions effectively and efficiently. We exploited simulation by integrating a WITNESS Computer Assembly Simulation Model in modeFRONTIER, a comprehensive solution for process automation and multidisciplinary optimization in the engineering design process. Once this process automation is complete, the user can easily run a variety of iterative studies on the simulation model, such as Design of Experiments (DOE) and Optimization. The primary goal of carrying out a Design of Experiments (DOE) on a simulation model is to perform statistical data analysis to gain insights into the problem. Through modeFRONTIER we run a Uniform Latin Hypercube (ULH) DOE of 1000 scenarios for the computer assembly model. We can then carry out detailed statistical analyses using the data generated through these simulations to obtain further insights. Subsequently, we run a multi-objective optimization in modeFRONTIER with the aim to maximize profitability. We utilized a flavour of Genetic Algorithm from modeFRONTIER called MOGA-II with 30 scenarios as the initial population and 3000 as the total number of maximum allowed runs. The optimization converges after just 729 simulation runs.
BENEFITS - The Design of Experiments, followed by the statistical analysis, helps identify the decision space and the key variables affecting the performance measures. The optimization drives simulations in WITNESS and efficiently finds optimal solutions once the optimization statement is defined, whether it is formed as single objective or multi-objective optimization. All these techniques help simulation engineers understand and quantify the different decision levels at hand. modeFRONTIER and WITNESS together not only help engineers identify the optimal solutions, but also assist the decision-making process by presenting a set of optimal alternatives.