An evolutionary programming approach for solving the capacitated facility location problem with risk pooling

TLDR: A genetic algorithm that is computationally very efficient is developed to solve the capacitated facility location problem with risk pooling (CLMRP), a joint location-inventory problem involving a single supplier and multiple retailers that face stochastic demand.

Abstract: In this paper, we propose a genetic algorithm as an alternative technique for solving the capacitated facility location problem with risk pooling (CLMRP). The CLMRP is a joint location-inventory problem involving a single supplier and multiple retailers that face stochastic demand. Due to the stochasticity of demand associated with each retailer, risk pooling may be achieved by allowing some retailers to serve as distribution centres (DCs). This is a combinatorial optimisation problem that has been shown to be NP-hard. A genetic algorithm that is computationally very efficient is developed to solve the problem. A computational experiment is conducted to test the performance of the developed technique and computational results are reported. The algorithm can easily find optimal or near optimal solutions for benchmark test problems from the literature, where the Lagrangian relaxation approach was used.