Exploring fairness in food delivery routing and scheduling problems
Exploring fairness in food delivery routing and scheduling problems
Demand for delivery of take-away meals to customers has been growing worldwide, with deliveries often performed by non-specialised gig economy couriers working for online platform operators such as Deliveroo or Just Eat. This has led to the introduction of the ‘meal delivery problem’, characterised by a series of individual pickup and delivery tasks to be assigned to available couriers. While there is a vast set of algorithms proposed in the literature that aim to minimise total workload, very little attention has been given to equitably distributing work between couriers. We propose a new multi-objective problem that is aiming at distributing orders equitably between couriers as well as minimising total workload, where all information is known upfront. We propose an integer linear programming (ILP) model with a weighted objective function that is used to derive the Pareto front in small-scale problems by exploiting the ϵ−constraint approach. This formulation has been proven to solve in a reasonable time for problems with up to 60 orders, however, the optimal Pareto front can only be computed within a reasonable time for problems up to 30 orders. For problems with more orders, we propose a Variable Neighbourhood Search (VNS) algorithm, for which the fitness evaluation evolves in order to explore a wider set of the solution space. The VNS is compared against the ILP and also tested on more realistic size instances with up to 3123 orders, improving the performance over the business as usual and shows that equitable distribution of work can be achieved alongside reducing the total travelled distance.
Fairness, Food delivery, Integer linear programming, Variable neighbourhood search, Vehicle routing
Martinez-Sykora, Antonio
2f9989e1-7860-4163-996c-b1e6f21d5bed
Mcleod, Fraser
93da13ec-7f81-470f-8a01-9339e80abe98
Cherrett, Tom
e5929951-e97c-4720-96a8-3e586f2d5f95
Friday, Adrian
9ec6b472-8423-449b-9bf6-fea8cbe1df8c
15 April 2024
Martinez-Sykora, Antonio
2f9989e1-7860-4163-996c-b1e6f21d5bed
Mcleod, Fraser
93da13ec-7f81-470f-8a01-9339e80abe98
Cherrett, Tom
e5929951-e97c-4720-96a8-3e586f2d5f95
Friday, Adrian
9ec6b472-8423-449b-9bf6-fea8cbe1df8c
Martinez-Sykora, Antonio, Mcleod, Fraser, Cherrett, Tom and Friday, Adrian
(2024)
Exploring fairness in food delivery routing and scheduling problems.
Expert Systems with Applications, 240, [122488].
(doi:10.1016/j.eswa.2023.122488).
Abstract
Demand for delivery of take-away meals to customers has been growing worldwide, with deliveries often performed by non-specialised gig economy couriers working for online platform operators such as Deliveroo or Just Eat. This has led to the introduction of the ‘meal delivery problem’, characterised by a series of individual pickup and delivery tasks to be assigned to available couriers. While there is a vast set of algorithms proposed in the literature that aim to minimise total workload, very little attention has been given to equitably distributing work between couriers. We propose a new multi-objective problem that is aiming at distributing orders equitably between couriers as well as minimising total workload, where all information is known upfront. We propose an integer linear programming (ILP) model with a weighted objective function that is used to derive the Pareto front in small-scale problems by exploiting the ϵ−constraint approach. This formulation has been proven to solve in a reasonable time for problems with up to 60 orders, however, the optimal Pareto front can only be computed within a reasonable time for problems up to 30 orders. For problems with more orders, we propose a Variable Neighbourhood Search (VNS) algorithm, for which the fitness evaluation evolves in order to explore a wider set of the solution space. The VNS is compared against the ILP and also tested on more realistic size instances with up to 3123 orders, improving the performance over the business as usual and shows that equitable distribution of work can be achieved alongside reducing the total travelled distance.
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Accepted/In Press date: 5 November 2023
e-pub ahead of print date: 10 November 2023
Published date: 15 April 2024
Additional Information:
Funding Information:
This work was funded by the EPSRC under grant EP/S027726/1 .
Publisher Copyright:
© 2023 The Author(s)
Keywords:
Fairness, Food delivery, Integer linear programming, Variable neighbourhood search, Vehicle routing
Identifiers
Local EPrints ID: 484132
URI: http://eprints.soton.ac.uk/id/eprint/484132
ISSN: 0957-4174
PURE UUID: a653a721-7fcd-4c23-97ff-cee20eae6f08
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Date deposited: 10 Nov 2023 18:01
Last modified: 06 Jun 2024 01:49
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Author:
Adrian Friday
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