Merging crow search into ordinal optimization for solving equality constrained simulation optimization problems

Abstract

Equality-constrained simulation optimization problems (ECSOP) involve the finding of optimal solutions by simulation within a well-defined search space under deterministic equality constraints. ECSOPs belong to the class of NP-hard problems. The large search space makes them difficult to solve in a short period using conventional optimization techniques. An approach that merges the crow search (CS) into ordinal optimization (OO), abbreviated as CSOO, is developed to find a near-optimal solution to the ECSOP within a reasonable time. The proposed approach has three phases, which are surrogate model, exploration and exploitation. First, a surrogate model, based on the multivariate adaptive regression splines, is used to evaluate the fitness of a solution. Next, an enhanced crow search algorithm is used to find N excellent solutions in the search space. Finally, an intensified optimal computing budget allocation is used to find a near-optimal solution among the N excellent solutions. The proposed CSOO approach is applied to a three-stage ten-node network-type production line, and the formulated problem is an ECSOP with a large search space. The developed formulation can be used for network-type production lines with any distribution of arrivals and production times. Simulation results that are obtained using the CSOO are compared with those obtained using four competing methods Test results reveal that the proposed approach yields a near-optimal solution of much higher quality than obtained using four competing methods, and with a much higher computing efficiency.