Integrating Bat Algorithm to Ordinal Optimization for Solving the Facility-Sizing Problem

Facility-sizing is more essential on small sites due to limitations on the space and the effects of imprecise judgement of facility size. The aim of facility-sizing problem is to efficiently install the facility to minimize the cost of installation and lower the probability of violating demand. In this work, an approach integrating bat algorithm (BA) to ordinal optimization (OO) is developed to rmd an optimal facility size for minimizing the cost of installation in manufacturing facilities. The solution method has two phases. The first phase utilizes the BA aided with a crude evaluation to construct a selected subset of candidate solutions. The second phase is to identify a near-optimal solution among the selected subset using the optimal computing budget allocation (OCBA) technique. The proposed approach has been tested on an example with m=10 facilities. Test results demonstrate that the proposed approach can yield a near-optimal solution within a reasonable computing time.