Capacity Allocation in Queuing Systems with Preferred Service Completion Times

TL;DR

This paper models a queuing system with preferred service times, analyzing capacity allocation strategies that balance outsourcing costs and early service penalties, especially relevant for curbside pickup services.

Contribution

It introduces a novel queuing model with preferred completion times and develops threshold-based policies for capacity allocation, including heuristics for complex systems.

Findings

Optimal policies are threshold-based for small systems.

Heuristics perform robustly across various parameters.

Long-term costs depend on capacity, horizon, and cost factors.

Abstract

Retailers use a variety of mechanisms to enable sales and delivery. A relatively new offering by companies is curbside pickup where customers purchase goods online, schedule a pickup time, and come to a pickup facility to receive their orders. To model this new service structure, we consider a queuing system where each arriving job has a preferred service completion time. Unlike most queuing systems, we make a strategic decision for when to serve each job based on their requested times and the associated costs. We assume that all jobs must be served before or on their requested time period, and the jobs are outsourced when the capacity is insufficient. Costs are incurred for jobs that are outsourced or served early. For small systems, we show that optimal capacity allocation policies are of threshold type. For general systems, we devise heuristic policies based on similar threshold structures. Our numerical study investigates the performance of the heuristics developed and shows the robustness of them with respect to several service parameters. Our results provide insights on how the optimal long-run average costs change based on the capacity of the system, the length of the planning horizon, cost parameters and the order pattern.