Optimal Capacity Rationing Policy for a Container Leasing System with Multiple Kinds of Customers and Substitutable Containers

Abstract

In this paper, we consider a container leasing firm that has elementary and premium containers, which are downward substitutable and for use by elementary contract customers (ECCs), premium contract customers (PCCs), as well as walk-in customers (WICs). ECCs can be satisfied by elementary containers or premium ones at discounted prices while PCCs only accept premium containers. WICs can be satisfied by any type of container at different prices. The objective is to maximise the expected total rental revenue by managing its limited capacity. We formulate this problem as a discrete-time Markov Decision Process and show the submodularity and concavity of the value function. Based on this, we show that the optimal policy can be characterised by a series of rationing thresholds, a series of substitution thresholds and a priority threshold, all of which depend on the system states. We further give conditions under which the optimal policy can be simplified. Numerical experiments are conducted to show the impact of the substitution of two items on the revenue, to compare the performance of the optimal policy with those of the commonly used policies and to investigate the influence of arrival rates on the optimal policy. Last, we extend the basic model to consider different rental durations, ECCs’ acceptance behaviour and endogenous prices for WICs. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: This work was supported by National Natural Science Foundation of China/Research Grants Council of Hong Kong Joint Research Scheme [Grant 71661167009, N_PolyU531/16], National Natural Science Foundation of China [Grants 72171016, 71831001], and British Academy\Leverhulme Small Research [Grant SRG19\190059]. This work was also supported by Zhejiang Shuren University Research [Grant KXJ0121605] and Beijing Logistics Informatics Research Base. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2022.4425 .