Developing a multi-method simulation model of a green closed-loop supply chain and determining pricing and advertising policy against a competitor

The environmental impact of supply chains has motivated many studies in this area. This research proposes a novel multi-method simulation approach which combines agent-based and discrete event modelling approaches to model a green closed-loop supply chain and optimise it through an optimisation via simulation technique. A closed-loop supply chain is developed under the demand uncertainty to minimise total cost and total greenhouse gases (GHG) emissions and maximise management preference of the supply chain in the presence of a competitor. Taguchi design of experiments method is used to generate scenarios, then total cost and total GHG emissions are recorded through simulating the scenarios. Management preference is determined based on decision makers' opinion. Scenarios are ranked against three attributes with the proposed panel-group TOPSIS method. Inventory replenishment parameters, pricing and advertising policies, and transportation type are determined via solving the model. An automotive industry case is provided to demonstrate the model's capabilities.