Minimizing Transport Emissions for Products Delivery: Accounting for Uncertainty on Industrial Supply Chains
Di Pretoro, Alessandro
Negny, Stephane
Montastruc, Ludovic

How to Cite

Di Pretoro A., Negny S., Montastruc L., 2022, Minimizing Transport Emissions for Products Delivery: Accounting for Uncertainty on Industrial Supply Chains, Chemical Engineering Transactions, 96, 511-516.


Transport represents almost a quarter of Europe's greenhouse gas emissions and it is the main cause of air pollution in cities. Therefore, the Commission's low-emission mobility strategy represents a key challenge towards a low-carbon, circular economy. Moreover, with the increasing price of transportation fuel, it is becoming a more and more relevant cost item in the industrial sector.
In the production sector, supply chains are usually optimized according to an established set of expected customers, this means that, when customers are not known, the outlined optimal solution could considerably underperform in terms of costs and transportation path. Accounting for uncertainties when designing a supply chain strategy could be then of critical importance to reduce the vehicle travel distances and, thus, the related emissions due to fuel consumption as well as to have more reliable expectations.
In this research work, the analysis of the optimal travel paths under uncertain customers’ location is addressed for a classical vehicle routing problem with delivery based on a methodology outlined in previous studies by the same authors. The approach exploits stochastic sampling generation to provide a probability distribution concerning the expected emissions and travel costs (travel equivalent distance in this case) for a given centralized factory location. In the selected case study, the uncertainty concerning both the customer locations and their expected demand are considered since these have been proved to be the most critical parameters. As indicator for the environmental impact the authors selected the Global Warming Potential.
The analysis shows that accounting for uncertainties during optimization of the supply chain design phase could provide a more reliable estimation of both travel distance and fuel consumption. Moreover, it allows to quantify the variance of these two in order to have an estimation of the maximum and minimum values. This approach is then worth to be extended to industrial cases and applications in order to be validated and become the standard methodology for a more low-carbon mobility strategy in the industrial sector.