Logistics Capacity Balancing Platforms in the Sharing Economy: When Will Simple Rules Be Optimal?

Abstract

The logistics industry faces high risk as demands for logistics services are stochastic. Facing the market demand volatility, individual logistics service providers (called &#x201C;agents&#x201D;) who are individual decision makers have their own preferences with respect to their profit targets. It is commonly observed that some agents are more risk taking (and hence they set very ambitious profit targets) whereas some are more risk averse. This creates a situation in which some agents over-reserve logistics capacities but some under-reserve. In the sharing economy, the logistics capacities can be balanced out and shared via platforms. In this article, we analytically build newsvendor problem-based optimization models to explore the value of a capacity-balancing-platform, in the presence of multiple agents. We propose two capacity-balancing mechanisms (called Rules 1 and 2) for the platform: 1) Rule 1 is an <i>equal balancing rule</i> in which all the capacities of agents will be collected and evenly distributed to all agents and 2) Rule 2 is a <i>surplus balancing rule</i> in which all capacities of agents will be collected and classified, and balancing is done with respect to surplus in capacity reservation. We then compare Rule 1 and Rule 2 with the original system when the platform is absent (Rule 0). We analytically prove that Rule 2 always outperforms Rule 0 with respect to the total systems expected profit. For the homogeneous case, Rule 1 is the optimal allocation rule and outperforms (with respect to the total systems expected profit) the more complex rule, i.e., Rule 2, which interestingly shows that &#x201C;simplicity is better.&#x201D; To generate more insights and to check the robustness of findings, we extend the analyses to cover more cases.