A Simheuristic approach to the vehicle ferry revenue management problem

We propose a Simheuristic approach to the vehicle ferry revenue management problem, where the aim is to maximize the revenue 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, enabling it to be solved rapidly. We present simulations of the selling season using this reduced state space to validate the method.