Allocation of products to a heterogeneous fleet of trucks in a cross-docking center based on carbon emissions and costs in food and beverage industry: Novel uncertain solution approaches

The threat of climate change continues to grow, which calls for strategies to reduce emissions. Carbon emissions from transportation are among the highest in the world, so it is essential to improve its efficiency. Cross-docking is a smart way to improve the efficiency of transportation operations through the optimal use of truck capacity. This paper develops a novel bi-objective mixed integer linear programming (MILP) model to determine which products should be shipped together, select the most appropriate truck among the available ones, and schedule them. It reveals a new class of cross-dock truck scheduling problems, in which products are not interchangeable and are sent to different destinations. The first objective is to minimize overall system costs, while the second is to minimize total carbon emissions. To deal with uncertainties in factors, such as costs, time, and emission rate, these parameters are considered interval numbers. Furthermore, innovative uncertain approaches are introduced under interval uncertainty based on optimistic and pessimistic Pareto solutions for solving MILP problems via epsilon-constraint and weighting methods. The proposed model and solution procedures are used for planning an operational day at a regional distribution center (RDC) of a real food and beverage company, and results are compared. The results show that the proposed epsilon-constraint method outperforms the other implemented methods in terms of quantity and variety of optimistic and pessimistic Pareto solutions. Using the newly developed procedure, the amount of carbon produced by trucks could decrease by 18% under optimistic assumptions and 44% under pessimistic assumptions. As a result of the proposed solution approaches, managers can observe how their optimism level and the importance of objective functions influence their decisions.


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  • Accession Number: 01881226
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 25 2023 2:50PM