Difference Between Simulation and Optimization
Objectives Paragraph
Uncertainty in the business environment is a major threat at each and every level of the supply chain. Every day new challenges and opportunities arise – rising cost of fue, implications of an organization’s carbon footprint, outsourcing regulations, tax incentives, and political fluctuation. Proactively monitoring the implications of such events at frequent intervals is crucial for an organization. By using a variety of Supply Chain modeling and mathematical tools, an organization is able to develop an understanding of the implications of such factors. However, the vast array of tools also creates a dilemma about the best modeling approach. In the field of supply chain modeling, one dilemma that a corporation faces today is whether optimization, simulation, or a hybrid model (combination of optimization and simulation) is a better option to pursue.
In this paper, we fundamentally distinguish the two modeling approaches – Supply Chain Optimization vs. Supply Chain Simulation, and the scenarios where the each option should be employed.
Overview
Optimization focuses on finding the optimal solution from millions of possible alternatives while meeting the given constraints of the supply chain. Optimization utilizes mixed integer programming (MIP) or linear programming (LP) to obtain the optimal solution. Optimization models are used for network optimization, allocation management (refinery and terminals), route optimization (retail logistics) and vendor-managed inventory (retail network management).
Simulation identifies the impact of different variables on an organization’s entire supply chain. It answers the fundamental question – what will happen to the cost and service levels associated with a Supply Chain if an ‘X’ factor is manipulated The tool however does not drive to an optimal solution. Simulation also enables a user to visualize real world behavior of an “optimal solution” derived from the optimization. Simulation models are
Uncertainty in the business environment is a major threat at each and every level of the supply chain. Every day new challenges and opportunities arise – rising cost of fue, implications of an organization’s carbon footprint, outsourcing regulations, tax incentives, and political fluctuation. Proactively monitoring the implications of such events at frequent intervals is crucial for an organization. By using a variety of Supply Chain modeling and mathematical tools, an organization is able to develop an understanding of the implications of such factors. However, the vast array of tools also creates a dilemma about the best modeling approach. In the field of supply chain modeling, one dilemma that a corporation faces today is whether optimization, simulation, or a hybrid model (combination of optimization and simulation) is a better option to pursue.
In this paper, we fundamentally distinguish the two modeling approaches – Supply Chain Optimization vs. Supply Chain Simulation, and the scenarios where the each option should be employed.
Overview
Optimization focuses on finding the optimal solution from millions of possible alternatives while meeting the given constraints of the supply chain. Optimization utilizes mixed integer programming (MIP) or linear programming (LP) to obtain the optimal solution. Optimization models are used for network optimization, allocation management (refinery and terminals), route optimization (retail logistics) and vendor-managed inventory (retail network management).
Simulation identifies the impact of different variables on an organization’s entire supply chain. It answers the fundamental question – what will happen to the cost and service levels associated with a Supply Chain if an ‘X’ factor is manipulated The tool however does not drive to an optimal solution. Simulation also enables a user to visualize real world behavior of an “optimal solution” derived from the optimization. Simulation models are