Conference Agenda

Session
TB7 - SM7: Service staffing and capacity allocation
Time:
Tuesday, 28/June/2022:
TB 10:30-12:00

Session Chair: Gar Goei Loke
Location: Forum 11


Presentations

How to staff when customers arrive in batches

Andrew Daw1, Robert Hampshire2, Jamol Pender3

1University of Southern California, Marshall School of Business; 2University of Michigan & US Department of Transportation; 3Cornell University

From cloud computing to Covid quarantines, requests for service can arrive in batches. How should this impact the service's staffing? Here, we find that there is no economy of scale as batches grow large, a stark contrast with classical square root rules. By consequence, the queue length is not asymptotically normal; in fact, the fluid and diffusion-type limits coincide. When arrivals are both quick and in batches, an economy of scale can exist, but we show that it is weaker than expected.



Service staffing for shared resources

Buyun Li1, Vincent Slaugh2

1Indiana University, United States of America; 2Cornell University, United States of America

Motivated by hotel housekeeping, we study shift construction decisions for room attendants amid uncertainty about customer arrival and departure times. We provide analytical results using the framework of M-convexity. A numerical case study for one hotel suggests that reallocating a small number of workers to later shifts can effectively eliminate guest waiting after the posted check-in time. We also identify alternate optimal solutions that can be useful for recruiting and retaining workers.



Joint capacity allocation and job assignment under uncertainty

Peng Wang3, Yun Fong Lim2, Gar Goei Loke1

1Rotterdam School of Management, Netherlands, The; 2Singapore Management University, Singapore; 3National University of Singapore, Singapore

We consider the multi-period problem of jointly allocating resources to J supply nodes and assigning jobs of I different demand origins to the nodes. The goal is to maximize rewards for matching or minimize costs of waiting and assignment. We introduce a distributive decision rule, which represents the proportion of jobs served by each of the supply nodes. We test against benchmark models developed specifically for allocation or assignment decisions only and record 1-15% reductions in costs.