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Dynamic pricing for vehicle ferries: using packing and simulation to optimize revenues

Dynamic pricing for vehicle ferries: using packing and simulation to optimize revenues
Dynamic pricing for vehicle ferries: using packing and simulation to optimize revenues
We propose an improved heuristic approach to the vehicle ferry revenue management problem, where the aim is to maximize the revenue obtained from the sale of vehicle tickets by varying the prices charged to different vehicle types, each occupying a different amount of deck space. Customers arrive and purchase tickets according to their vehicle type and their willingness-to-pay, which varies over time. The optimization problem can be solved using dynamic programming but the possible states in the selling season are the set of all feasible vehicle mixes that fit onto the ferry. This makes the problem intractable as the number of vehicle types increases. We propose a state space reduction, which uses a vehicle ferry loading simulator to map each vehicle mix to a remaining space state. This reduces the state space of the dynamic program. Our approximation approach allows the value function to be approximated rapidly and accurately with a relatively coarse discretization of states. We present simulations of the selling season using this reduced state space to validate the method. The vehicle ferry loading simulator was developed in collaboration with a vehicle ferry company and addresses real-world constraints such as manoeuvrability, elevator access, strategic parking gaps, vehicle height constraints and ease of implementation of the packing solutions.
University of Southampton
Bayliss, Christopher
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Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Martinez Sykora, Antonio
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Bennell, Julia
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Bayliss, Christopher
5fb04968-5cbf-40d8-84b0-02e8c7e94a59
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Martinez Sykora, Antonio
2f9989e1-7860-4163-996c-b1e6f21d5bed
Bennell, Julia
38d924bc-c870-4641-9448-1ac8dd663a30

Bayliss, Christopher, Currie, Christine, Martinez Sykora, Antonio and Bennell, Julia (2017) Dynamic pricing for vehicle ferries: using packing and simulation to optimize revenues University of Southampton 29pp. (doi:10.5258/SOTON/P0002). (Submitted)

Record type: Monograph (Working Paper)

Abstract

We propose an improved heuristic approach to the vehicle ferry revenue management problem, where the aim is to maximize the revenue obtained from the sale of vehicle tickets by varying the prices charged to different vehicle types, each occupying a different amount of deck space. Customers arrive and purchase tickets according to their vehicle type and their willingness-to-pay, which varies over time. The optimization problem can be solved using dynamic programming but the possible states in the selling season are the set of all feasible vehicle mixes that fit onto the ferry. This makes the problem intractable as the number of vehicle types increases. We propose a state space reduction, which uses a vehicle ferry loading simulator to map each vehicle mix to a remaining space state. This reduces the state space of the dynamic program. Our approximation approach allows the value function to be approximated rapidly and accurately with a relatively coarse discretization of states. We present simulations of the selling season using this reduced state space to validate the method. The vehicle ferry loading simulator was developed in collaboration with a vehicle ferry company and addresses real-world constraints such as manoeuvrability, elevator access, strategic parking gaps, vehicle height constraints and ease of implementation of the packing solutions.

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Dynamic Pricing With Simulation and Packing 2017_10_13 - Author's Original
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Submitted date: 19 October 2017

Identifiers

Local EPrints ID: 418895
URI: https://eprints.soton.ac.uk/id/eprint/418895
PURE UUID: 0482dd13-14e3-49d4-8d12-f2fc07b2ff19
ORCID for Christine Currie: ORCID iD orcid.org/0000-0002-7016-3652
ORCID for Antonio Martinez Sykora: ORCID iD orcid.org/0000-0002-2435-3113

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Date deposited: 23 Mar 2018 17:31
Last modified: 08 Jun 2019 00:31

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