Abstract
In this paper, we propose robust optimisation models for the distribution network design problem (DNDP) to deal with uncertainty cases in a collaborative context. The studied network consists of collaborative suppliers who satisfy their customers’ needs by delivering their products through common platforms. Several parameters—namely, demands, unit transportation costs, the maximum number of vehicles in use, etc.—are subject to interval uncertainty. Mixed-integer linear programming formulations are presented for each of these cases, in which the economic and environmental dimensions of the sustainability are studied and applied to minimise the logistical costs and the CO2 emissions, respectively. These formulations are solved using CPLEX. In this study, we propose a case study of a distribution network in France to validate our models. The obtained results show the impacts of considering uncertainty by comparing the robust model to the deterministic one. We also address the impacts of the uncertainty level and uncertainty budget on logistical costs and CO2 emissions.
Highlights
In recent decades, companies have become more concerned about the economic and environmental impacts of their logistics operations
In this paper, we propose robust optimisation models for the distribution network design problem (DNDP) to deal with uncertainty cases in a collaborative context
We address the impacts of the uncertainty level and uncertainty budget on logistical costs and CO2 emissions
Summary
Companies have become more concerned about the economic and environmental impacts of their logistics operations. In other cases, the only available information is the specification of intervals containing the uncertain values of these parameters To solve this problem, the application of robust optimisation techniques, which can perform well even in the worst case scenarios, is the best alternative [7]. We examine a case in which demands, unit transportation costs, and the maximum number of vehicles in use are uncertain, and the only available information is an interval of uncertainty. We use a dual transformation to reformulate them as compact mixed-integer linear programming (MILP) with a polynomial number of variables and constraints We solve these MILP formulations using a commercial solver to highlight the impacts of the uncertainty and its budget on several parameters.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
