Optimal Scheduling of Waitstaff with Different Experience Levels at a Restaurant Chain

Akhundov, Najmaddin; Tahirov, Nail; Glock, Christoph H.

Abstract

Restaurants often face strong pressure to reduce costs. Managers regularly respond by hiring temporary or part-time workers and by trying to reduce the size of the workforce as much as possible, which makes it difficult to develop a personnel schedule that provides sufficient service to the customers. The problem gets even more complicated if (frequent) employee turnover and demand fluctuations occur and if employees have different experience levels. This paper presents mathematical models to support waitstaff scheduling at a restaurant chain based in Baku, Azerbaijan, taking into account the managerial requirements of the company. The problem we address is equivalent to a general tour scheduling problem that assigns waitstaff to work shifts throughout the week. We develop three integer programming models taking account of factors, such as employee types and experience levels, differences in the complexity of customer orders, and side tasks and responsibilities, to find the optimal number of employees together with the best tour for each of them. The models are solved to optimality, and the results are applied at a branch of the restaurant chain in Baku. Compared with the existing schedule, the optimized schedule enabled the restaurant to reduce overstaffing levels by approximately 40% and labor costs by 20% while keeping the same service standards.

Citation

Akhundov, N., Tahirov, N., & Glock, C. H. (2022). Optimal Scheduling of Waitstaff with Different Experience Levels at a Restaurant Chain. INFORMS Journal on Applied Analytics, 52(4), 324-343. https://doi.org/10.1287/inte.2022.1124

• Journal Article Type: Article
• Acceptance Date: Apr 1, 2022
• Online Publication Date: Jun 23, 2022
• Publication Date: 2022-07
• Deposit Date: Nov 27, 2024
• Journal: INFORMS Journal on Applied Analytics
• Print ISSN: 2644-0865
• Electronic ISSN: 2644-0873
• Publisher: Institute for Operations Research and Management Sciences
• Peer Reviewed: Peer Reviewed
• Volume: 52
• Issue: 4
• Pages: 324-343
• DOI: https://doi.org/10.1287/inte.2022.1124
• Public URL: https://durham-repository.worktribe.com/output/3108386