A Simheuristic Approach Cooperating Skill Optimization Algorithm with Ordinal Optimization

The constrained discrete optimization problems (CDOP) have a stochastic objective function and deterministic inequality constraints. The CDOP is NP-hard due to the large and exponentially growing solution space. The ordinal optimization framework is treated as a recognized framework for resolving NP-hard problems. A simheuristic approach cooperating skill optimization algorithm with ordinal optimization is developed to solve this CDOP in a short time. The simheuristic approach comprises two levels. The skill optimization algorithm is used to construct a differentiated subset with a cursory estimate at the first level. Then, the ranking and selection method is used to find a superior solution from differentiated subsets in the second level. Finally, the approach is applied to the multiple-item inventory system's stock level optimization problem. A real-life example with 5 potential customers and 3 products is used to test the performance. Simulation results reveal that the simheuristic approach can obtain stable and excellent results on the CDOP.