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 t...

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Vydané v:Journal of environmental management Ročník 332; s. 117071
Hlavní autori: Rajabzadeh, Mohsen, Mousavi, Seyed Meysam
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Elsevier Ltd 15.04.2023
Predmet:
beverage industry
Beverages
Carbon
Carbon emissions
climate change
Cross-docking
environmental management
Epsilon-constraint method
Food and beverage industry
Interval programming
Motor Vehicles
Multi-objective mixed integer programming
Transportation
Uncertainty
Weighting method
Interval programming
Multi-objective mixed integer programming
Epsilon-constraint method
Weighting method
Cross-docking
Carbon emissions
Food and beverage industry
ISSN:0301-4797, 1095-8630, 1095-8630
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Shrnutí: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. •Introducing a novel bi-objective MILP model for trucks in a cross-docking center•Considering new objective functions, overall costs and total carbon emissions•Presenting uncertainty in time, cost, and carbon emissions in interval form•Proposing new uncertain solution approaches to tackle interval MOP problems•Implementing a real study in food and beverage company
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0301-4797
1095-8630
1095-8630
DOI:10.1016/j.jenvman.2022.117071