###### Abstract  
The stochastic inequality constrained optimization problems (SICOPs) consider the problems of optimizing an objective function involving stochastic inequality constraints. The SICOPs belong to a category of NP-hard problems in terms of computational complexity. The ordinal optimization (OO) method offers an efficient framework for solving NP-hard problems. Even though the OO method is helpful to solve NP-hard problems, the stochastic inequality constraints will drastically reduce the efficiency and competitiveness. In this paper, a heuristic method coupling elephant herding optimization (EHO) with ordinal optimization (OO), abbreviated as EHOO, is presented to solve the SICOPs with large solution space. The EHOO approach has three parts, which are metamodel construction, diversification and intensification. First, the regularized minimal-energy tensor-product splines is adopted as a metamodel to approximately evaluate fitness of a solution. Next, an improved elephant herding optimization is developed to find \(N\) significant solutions from the entire solution space. Finally, an accelerated optimal computing budget allocation is utilized to select a superb solution from the \(N\) significant solutions. The EHOO approach is tested on a one-period multi-skill call center for minimizing the staffing cost, which is formulated as a SICOP. Simulation results obtained by the EHOO are compared with three optimization methods. Experimental results demonstrate that the EHOO approach obtains a superb solution of higher quality as well as a higher computational efficiency than three optimization methods. s _articles  
intelligence [5]. However, the convergence of the stochastic gradient descent methods generally requires complicated assumptions on the cost function and the constraint set [3]. The metaheuristic approaches [4], such as the simulated annealing, tabu search and evolutionary algorithms (EA) [6], are adopted to search for the global optimum. Nevertheless, the metaheuristic approaches are highly dependent on the control parameters involved and require massively parallel implementations to yield results within an acceptable time. Bio-inspired artificial intelligence [5] discusses the collective behavior emerging within self-organizing societies of agents. Some instances of novel swarm intelligence (SI) approaches are: Brain storm optimization, whale optimization algorithm, sooty tern optimization, fireworks algorithm and elephant herding optimization (EHO) [7; 8]. Despite the success of applying these SI techniques [9], several limitations and barriers have been identified [10]. To reduce the computing time of SICOPs, a heuristic method coupling elephant herding optimization (EHO) with ordinal optimization (OO) [11], abbreviated as EHOO, is presented to find a superb solution in an acceptable time frame. The EHOO method consists essentially of three parts, which are metamodel construction, diversification and intensification. First, a metamodel based on the regularized minimal-energy tensor-product splines (RMTS) [12] is adopted to approximately evaluate fitness of a solution. Next, we proceed with an improved elephant herding optimization (IEHO) to determine \(N\) significant solutions from entire solution space. Finally, an accelerated optimal computing budget allocation (AOCBA) is utilized to choose a superb solution from the \(N\) significant solutions. These three parts significantly reduce the computational effort required to solve SICOPs. Subsequently, the problem of staffing optimization in a one-period multi-skill call center is formulated as a SICOP that contains a large solution space. Finally, the EHOO method is employed to solve the problem of staffing optimization in a one-period multi-skill call center. The goal of staffing optimization in a one-period multi-skill call center is to decide an optimal number of agents for minimizing the operating cost while satisfying the service level constraints. There are three contributions of this work.