Meta-heuristic algorithms for solving a fuzzy single-period problem

Single-period problem (SPP) is a classical stochastic inventory model that has become very popular recently. In this research, we developed a SPP with fuzzy environment. The demand of each product is considered as LR-fuzzy variables (ranking fuzzy numbers based on the left and right deviation degrees), and multiple constraints (including service level, batch order, budget, space and upper limit for each order). The aim of this paper is to maximize the total expected profit under incremental discount strategy. Five hybrid intelligent algorithms based on fuzzy simulation (FS) and meta-heuristic methods are presented; they are bees colony optimization (BCO), harmony search (HS), particle swarm optimization (PSO), genetic algorithm (GA) and simulated annealing (SA). Three numerical examples are presented to illustrate the performance of the algorithms. Our study shows that the BCO-FS hybrid method performs better than the HS-FS, GA-FS, PSO-FS, and SA-FS hybrid methods.