Productivity Improvement Through Multi-Objective Simulation Optimization—A Case Study

Abstract

To maximize profitability and meet customers' expectations, assembly plants strive to enhance operations and minimize costs. Using discrete-event system simulation and optimization, this paper investigates different scenarios to enhance operations efficiency and presents a decision support tool to aid operational decision making in an air conditioner assembly plant. Specifically, simulation is used to evaluate the system-wide impact of an optimal sequencing rule at a bottleneck station; in addition, a multi-objective simulation optimization problem is solved to enable manufacturers to evaluate their time/cost tradeoffs. Using actual data from a previous month, the simulation study illustrates possible savings throughout the evaluated alternatives. To sustain the study outcomes, we combine mathematical programming and simulation into an integrated decision support tool. Overall, this study illustrates the benefits of the solution approach to a real-life system.

The Use of Simulation and Artificial Intelligence as a Decision Support Tool for Sustainable Production Lines

“…Today, an increasing number of companies are using simulation tools to reproduce the behaviour of systems in detail [20][21][22]. Simulation has been shown to play a crucial role in industrial growth and development, particularly with respect to decision-making support and the creation of more sustainable and cost-effective production processes [23][24][25]. The evolution of technology has led to the emergence of what is now known as Digital Twin (DT), which involves the automation of processes, as well as the collection and analysis of data in real time to inform better decision making.…”