Abstract
This article proposes a new model to address the design problem of a sustainable regional logistics network with uncertainty in future logistics demand. In the proposed model, the future logistics demand is assumed to be a random variable with a given probability distribution. A set of chance constraints with regard to logistics service capacity and environmental impacts is incorporated to consider the sustainability of logistics network design. The proposed model is formulated as a two-stage robust optimization problem. The first-stage problem before the realization of future logistics demand aims to minimize a risk-averse objective by determining the optimal location and size of logistics parks with CO2 emission taxes consideration. The second stage after the uncertain logistics demand has been determined is a scenario-based stochastic logistics service route choices equilibrium problem. A heuristic solution algorithm, which is a combination of penalty function method, genetic algorithm, and Gauss–Seidel decomposition approach, is developed to solve the proposed model. An illustrative example is given to show the application of the proposed model and solution algorithm. The findings show that total social welfare of the logistics system depends very much on the level of uncertainty in future logistics demand, capital budget for logistics parks, and confidence levels of the chance constraints.
Highlights
There is a wide consensus that freight transportation is a major contributor to climate change and global warming is due to various pollution emissions
Freight transportation is largely driven by fossil fuel combustion, mostly diesel fuel, resulting in emissions of greenhouse gases (GHG), such as carbon dioxide (CO2), nitrogen oxide (NOx), sulfur oxide (SOx), particulate matters, and air toxics.[1]
It indicates that the existing related studies mainly focused on minimizing transportation cost or time, and little attention has been paid to logistics demand uncertainty and environment-related costs
Summary
There is a wide consensus that freight transportation is a major contributor to climate change and global warming is due to various pollution emissions. A mixed stochastic programming model is proposed and the solution based on simulated annealing (SA) and an imperialist competitive algorithm (ICA) is developed to solve real-sized instances It indicates that the existing related studies mainly focused on minimizing transportation cost or time, and little attention has been paid to logistics demand uncertainty and environment-related costs. The proposed model explicitly considers the effects of logistics demand uncertainty in future and CO2 emissions of transport modes It is formulated as a two-stage optimization problem with a set of chance constraints for the consideration of logistics system sustainability. A two-stage optimization model is developed to investigate the problem of optimizing location and size of logistics park considering CO2 emissions and uncertainty of logistics demand in future. According to random utility theory, lsw can be measured by the following log-sum formula shown in equation (9)[10,45]
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