Complex and delocalized manufacturing industries require high levels of integration between production and transportation in order to effectively implement lean and agile operations. There are, however, limitations in research and applications simultaneously embodying further sustainability dimensions. This article presents a methodological framework based on optimization and simulation to integrate (i) aggregate optimized plans for production and multimodal transportation with (ii) detailed dynamic distribution plans affected by demand uncertainty. The objective function of the optimization model considers supply, production, transportation and CO2 emission costs as well as collaboration over the multimodal network. Bill-of-materials and capacity constraints are included. A feedback between simulation and optimization is used to plan requirements for materials and components. Computational experiments are based on realistic instances. Results demonstrate that the framework can be effectively used to analyze cost-CO2 emissions trade-offs, effects of demand uncertainty and collaborative distribution strategies on economic and environmental performance of the supply chain.
Optimization and simulation of collaborative networks for sustainable production and transportation
Stecca G
2016
Abstract
Complex and delocalized manufacturing industries require high levels of integration between production and transportation in order to effectively implement lean and agile operations. There are, however, limitations in research and applications simultaneously embodying further sustainability dimensions. This article presents a methodological framework based on optimization and simulation to integrate (i) aggregate optimized plans for production and multimodal transportation with (ii) detailed dynamic distribution plans affected by demand uncertainty. The objective function of the optimization model considers supply, production, transportation and CO2 emission costs as well as collaboration over the multimodal network. Bill-of-materials and capacity constraints are included. A feedback between simulation and optimization is used to plan requirements for materials and components. Computational experiments are based on realistic instances. Results demonstrate that the framework can be effectively used to analyze cost-CO2 emissions trade-offs, effects of demand uncertainty and collaborative distribution strategies on economic and environmental performance of the supply chain.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.