Drone shipping versus truck delivery in a cross-docking system with multiple fleets and products

•We propose a new bi-objective multi-product combined cross-docking truck-scheduling model.•The model allows for direct drone shipping from supplier to customers and multiple fleets.•An efficient multi-objective epsilon-constraint method is adapted to solve the model.•The efficient Pareto frontiers...

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Vydáno v:	Expert systems with applications Ročník 72; s. 93 - 107
Hlavní autoři:	Tavana, Madjid, Khalili-Damghani, Kaveh, Santos-Arteaga, Francisco J., Zandi, Mohammad-Hossein
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Elsevier Ltd 15.04.2017 Elsevier BV
Témata:	Algorithms Cross-docking Delivery scheduling Docking Drone shipping Drone vehicles Functions (mathematics) Logistics Mathematical models Mathematical programming Mixed integer Multiple fleets Multiple objective analysis Multiple products Robustness (mathematics) Scheduling Sensitivity analysis Shipping Truck-scheduling Truck-scheduling Multiple fleets Multiple products Cross-docking Drone shipping
ISSN:	0957-4174, 1873-6793
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Abstract	•We propose a new bi-objective multi-product combined cross-docking truck-scheduling model.•The model allows for direct drone shipping from supplier to customers and multiple fleets.•An efficient multi-objective epsilon-constraint method is adapted to solve the model.•The efficient Pareto frontiers are estimated by generating non-dominated solutions.•We analyze the effects that changes in direct shipping costs have on the Pareto frontiers. We propose a new bi-objective multi-product combined cross-docking truck-scheduling model with direct drone shipping and multiple fleets.The proposed model considers two conflicting objective functions (scheduling cost and time) within a multi-objective mixed integer mathematical programming problem. Several constraint sets are also considered for both allocation and scheduling phenomena. An efficient multi-objective epsilon-constraint method is adapted to solve the proposed model. Several numerical examples and metrics are provided to demonstrate the applicability of the proposed model and exhibit the efficacy of the solution procedures and algorithms. The efficient frontiers of the numerical examples are estimated by generating non-dominated solutions. The effects that modifications in the costs associated with the direct shipping of products have on the corresponding Pareto frontiers are analyzed. Finally, sensitivity analysis is used to assess the robustness of the results of the model in the presence of uncertainty. [Display omitted]
AbstractList	We propose a new bi-objective multi-product combined cross-docking truck-scheduling model with direct drone shipping and multiple fleets.The proposed model considers two conflicting objective functions (scheduling cost and time) within a multi-objective mixed integer mathematical programming problem. Several constraint sets are also considered for both allocation and scheduling phenomena. An efficient multi-objective epsilon-constraint method is adapted to solve the proposed model. Several numerical examples and metrics are provided to demonstrate the applicability of the proposed model and exhibit the efficacy of the solution procedures and algorithms. The efficient frontiers of the numerical examples are estimated by generating non-dominated solutions. The effects that modifications in the costs associated with the direct shipping of products have on the corresponding Pareto frontiers are analyzed. Finally, sensitivity analysis is used to assess the robustness of the results of the model in the presence of uncertainty. •We propose a new bi-objective multi-product combined cross-docking truck-scheduling model.•The model allows for direct drone shipping from supplier to customers and multiple fleets.•An efficient multi-objective epsilon-constraint method is adapted to solve the model.•The efficient Pareto frontiers are estimated by generating non-dominated solutions.•We analyze the effects that changes in direct shipping costs have on the Pareto frontiers. We propose a new bi-objective multi-product combined cross-docking truck-scheduling model with direct drone shipping and multiple fleets.The proposed model considers two conflicting objective functions (scheduling cost and time) within a multi-objective mixed integer mathematical programming problem. Several constraint sets are also considered for both allocation and scheduling phenomena. An efficient multi-objective epsilon-constraint method is adapted to solve the proposed model. Several numerical examples and metrics are provided to demonstrate the applicability of the proposed model and exhibit the efficacy of the solution procedures and algorithms. The efficient frontiers of the numerical examples are estimated by generating non-dominated solutions. The effects that modifications in the costs associated with the direct shipping of products have on the corresponding Pareto frontiers are analyzed. Finally, sensitivity analysis is used to assess the robustness of the results of the model in the presence of uncertainty. [Display omitted]
Author	Khalili-Damghani, Kaveh Zandi, Mohammad-Hossein Santos-Arteaga, Francisco J. Tavana, Madjid
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Keywords	Truck-scheduling Multiple fleets Multiple products Cross-docking Drone shipping
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truck-to-door assignment problems
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Snippet	•We propose a new bi-objective multi-product combined cross-docking truck-scheduling model.•The model allows for direct drone shipping from supplier to... We propose a new bi-objective multi-product combined cross-docking truck-scheduling model with direct drone shipping and multiple fleets.The proposed model...
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SubjectTerms	Algorithms Cross-docking Delivery scheduling Docking Drone shipping Drone vehicles Functions (mathematics) Logistics Mathematical models Mathematical programming Mixed integer Multiple fleets Multiple objective analysis Multiple products Robustness (mathematics) Scheduling Sensitivity analysis Shipping Truck-scheduling
Title	Drone shipping versus truck delivery in a cross-docking system with multiple fleets and products
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