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

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