دانلود رایگان مقاله انگلیسی قیمت گذاری پویا برای معابر وسیله نقلیه: شبیه سازی برای بهینه سازی درآمد - الزویر 2019

abstract

We propose an 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 typically increases over time because customers purchasing tickets closer to departure tend to accept higher prices. 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 and ferry size 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 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.

Discussion

We have described a practical method for finding the optimal price to charge on vehicle ferries with stochastic demand and variable configurations. This compares well with the exact solution on small problem instances (97.48% of the optimal revenue and 0.7% of the computation time) and remains tractable for larger problems. Experimental results from a range of demand scenarios showed that our method led to higher average revenues than alternative pricing strategies in most cases. In particular, it attained average revenues that were 6% higher than those attained by the approximation of current practice. We believe this to be due to the method incorporating more flexibility in its pricing, allowing it to react to realized demand and improve the efficiency of the packing. This is underlined by the observation that improving the efficiency of the packing by re-optimizing during the selling season was shown to result in a 2.2% average increase in revenue. Being flexible with the ferry configuration and allowing it to vary during the selling period was shown to increase average revenue in some cases whilst being susceptible to pitfalls in certain situations. In particular the average demand scenario appeared to have the 0 and 2 mezzanine deck configurations as non-profitable attractors due to the effect that different pricing policies have on the final vehicle mix.