In this paper, the effect of load bundling on overall costs, service level, and CO$_2$ emissions is evaluated for a multi-stage, multi-item supply chain. A simulation-based optimisation approach is used to optimize the inventory management parameters reorder point and lot size. The optimisation approach consists of a simulation model and a metaheuristic search procedure, which is a subclass of the evolutionary algorithm. For the evaluation of the load bundling opportunity in different demand structures, a multicriterial objective function is optimized. The paper shows that the load bundling opportunity has significant cost and environmental benefits. The study points out that the load bundling opportunity leads to smaller and more customer-driven lot sizes which simultaneously reduce the carbon emissions. Finally, results show that for medium to high service level target values, ABC-clustered order rate scenarios lead to lower supply chain costs than demand scenarios with an identical order rate for all items.
- green supply chain management
- Inventory management
- NSGA-II metaheuristic
- simulation-based optimisation
- transportation optimisation