Estimating customer delay and tardiness sensitivity from periodic queue length observations

TL;DR

This paper develops a method to estimate customer delay and tardiness sensitivity ratios from daily queue length data, assuming customers follow a Nash equilibrium strategy, without needing to estimate the strategy itself.

Contribution

It introduces a novel method of moments estimator for the delay-tardiness ratio that is consistent, asymptotically normal, and robust to sampling frequency, without requiring equilibrium strategy estimation.

Findings

Estimator is strongly consistent and asymptotically normal.

Performance remains robust with varying sampling frequencies.

Simulation results validate the estimator's effectiveness.

Abstract

A single server commences its service at time zero every day. A random number of customers decide when to arrive to the system so as to minimize the waiting time and tardiness costs. The costs are proportional to the waiting time and the tardiness with rates $\alpha$ and $\beta$, respectively. Each customer's optimal arrival time depends on the others' decisions, thus the resulting strategy is a Nash equilibrium. This work considers the estimation of the ratio $\displaystyle\theta\equiv \beta/(\alpha+\beta)$ from queue length data observed daily at discrete time points, given that customers use a Nash equilibrium arrival strategy. A method of moments estimator is constructed from the equilibrium conditions. Remarkably, the method does not require estimation of the Nash equilibrium arrival strategy itself, or even an accurate estimate of its support. The estimator is strongly consistent and the estimation error is asymptotically normal. Moreover, the asymptotic variance of the estimation error as a function of the queue length covariance matrix (at sampling times) is derived. The estimator performance is demonstrated through simulations, and is shown to be robust to the number of sampling instants each day.