Simulation Budget Allocation Across Iterations in Iterative Local Search: A Stopping Rule-Based Approach

Simulation optimization has a wide range of applications, with simulation budget allocation serving as a critical method for improving efficiency. In many studies on simulation budget allocation for iterative search, the primary focus is on budget allocation within iterations, i.e., splitting the budget among neighborhood solutions. While allocation across iterations often relies on simple strategies such as equal distribution or gradual increase. This study explores the potential for improving search performance by determining the budget allocated to each iteration in a more effective manner. To achieve this, we propose a stopping rule based on the posteriori probability of correct move for the classical local search. Numerical experiments were conducted on a custom-designed grid-based search problem and a buffer allocation problem in an engine cylinder head assembly line. The results demonstrate that the proposed method enhances the performance of local search.