Simulation or Optimization: Unveiling the Best Approach, Where and When?

Simulation remains a pivotal model in supporting the decision-making process, having been utilized for many years. Recently, there has been a noticeable increase in the importance of optimization-based projects, capturing the interest of numerous companies. However, there exists a conceptual confusion between simulation and optimization approaches within the enterprise demands. This confusion can lead to misleading decision processes and undesirable technological investments. Consequently, our post aims to clarify the expectations and appropriate applications of simulation and optimization approaches.

Simulation is a modeling technique that replicates the behavior of an existing or proposed system, enabling the decision-making process by providing results from various scenarios. It serves as an extensively employed tool across multiple sectors, catering to "what if" inquiries. Optimization, on the other hand, refers to the actions taken to perfect or optimize a system, process, or design. In our post, we specifically discuss mathematical optimization or mathematical programming approaches. Optimization endeavors to enhance an existing system based on mathematical models, striving to achieve the optimal output while adhering to specific constraints imposed by given inputs.

Despite the perceived similarities, simulation primarily aims to support decision-making by testing various scenarios rather than focusing on finding the best outcome. Simulation models do not pursue optimal or most efficient outputs. In contrast, optimization seeks systemic improvements by obtaining the best results from the existing system.

Our research, conducted with project managers from 16 different companies spanning various sectors that employ simulation and optimization approaches, reveals a significant intermingling of studies categorized as optimization or simulation projects.

Specifically, 33% of implemented optimization projects necessitate the use of simulation techniques, while 30% of the projects initially labeled as simulation projects turn out to be optimization projects. Exhibit 1 provides a confusion matrix detailing these findings.

Exhibit 1. Confusion Matrix

Due to the intertwined nature of these approaches and the ensuing concept confusion, we found that 20% of project managers involved in simulation projects expressed dissatisfaction with the results. Likewise, 17% of project managers in optimization projects reported dissatisfaction with the outcomes. Exhibit 2 presents the research results.

Exhibit 2. Research Outcomes

In light of the concept confusion, below is a comparative summary outlining the appropriate circumstances for utilizing simulation and optimization approaches:

Exhibit 3. Simulation vs Optimization

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