Optimizing of bullwhip effect and net stock amplification in three-echelon supply chains using evolutionary multi-objective metaheuristics

Abstract

In this article, we intend to model and optimize the bullwhip effect (BWE) and net stock amplification (NSA) in a three-stage supply chain consisting of a retailer, a wholesaler, and a manufacturer under both centralized and decentralized scenarios. In this regard, firstly, the causes of BWE and NSA are mathematically formulated using response surface methodology (RSM) as a multi-objective optimization model that aims to minimize the BWE and NSA on both chains. The simultaneous analysis of the BWE and NSA is considered as the main novelty of this paper. To tackle the addressed problem, we propose a novel multi-objective hybrid evolutionary approach called MOHES; MOHES is a hybrid of two known multi-objective algorithms i.e. multi-objective electro magnetism mechanism algorithm (MOEMA) and population-based multi-objective simulated annealing (PBMOSA). We applied a co-evolutionary strategy for this purpose with eligibility of both algorithms. Proposed MOHES is compared with three common and popular algorithms (i.e. NRGA, NSGAII, and MOPSO). Since the utilized algorithms are very sensitive to parameter values, RSM with the multi-objective decision making (MODM) approach is employed to tune the parameters. Finally, the hybrid algorithm and the singular approaches are compared together in terms of some performance measures. The results indicate that the hybrid approach achieves better solutions when compared with the others, and also the results show that in a decentralized chain, the order batching factor and the demand signal processing in wholesaler are the most important factors on BWE. Conversely, in a centralized chain, factors such as rationing, shortage gaming, and lead time are the most effective at reducing the BWE.