Robust optimization theory for co2 emission control in collaborative supply chains

Abstract

Global sourcing in complex assembly production systems entails the management of potentially high variability and multiple risks in costs, quality and lead times. Additionally, current strategies of many companies or environmental regulatory frameworks impose - or will impose - on industries worldwide to take control, among others, of CO2 emissions and related costs generated in supply, production and distribution. Strategic planning should therefore manage multifaceted risks in order to prevent high-costly re-planning. This work addresses the problem of simultaneously controlling CO2 emission, production and transportation costs in supplier-manufacturer echelons. The problem is addressed by using the robust optimization theory applied to network strategic planning. A non-collaborative scenario in which each manufacturer independently selects its suppliers is compared to a scenario in which all the supply-chain actors aim to minimize production, transportation and CO2 emission costs. Computational experiments on realistic instances show positive effects of collaboration on costs, especially in more constrained tests.