Loading…

Real-time recovering strategies on personnel scheduling in the retail industry

•Our real-time control models allow interaction between decision maker and employees.•Experiments were made with an iterative MIP and then compared with a greedy algorithm.•Experiments used instances based on real data from two stores of a Chilean retailer.•Multiskilled employees are a vital recover...

Full description

Saved in:
Bibliographic Details
Published in:Computers & industrial engineering 2017-11, Vol.113, p.589-601
Main Authors: Mac-Vicar, Michael, Ferrer, Juan Carlos, Muñoz, Juan Carlos, Henao, César Augusto
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Our real-time control models allow interaction between decision maker and employees.•Experiments were made with an iterative MIP and then compared with a greedy algorithm.•Experiments used instances based on real data from two stores of a Chilean retailer.•Multiskilled employees are a vital recovery resource to face unexpected variations.•Model’s schedule adjustments reduced lost profits due to unexpected variations by 18%. Retailers must frequently deal with alterations in planned customer service levels due to unexpected demand variations or unscheduled employee absences. Although personnel scheduling techniques have been extensively studied and successfully applied, previous treatments of scheduling adjustments in response to demand and employee contingencies have not systematically considered all of the relevant issues. After presenting a mathematical specification of the problem, this study develops various algorithms that search for the best adjustments among all available contingency recovery resources, including transfers of multiskilled employees between different store areas. The proposed formulations also permit interaction between the user/decision maker and the affected employees. The underlying objective is to maximize profits, favoring solutions with fewer schedule modifications in order to minimize worker dissatisfaction. Due to the complexity of the basic model, the problem is divided and simplified using two greedy heuristics. Both algorithms can be implemented with real-world size problems and reach good solutions within minutes. Multiskilled employees prove to be an important reserve capacity for recovery of service levels in the face of unexpected variations. Empirical results using real data from a Chilean chain retailer show that in the worst scenario, the proposed model's schedule adjustments reduced lost profits due to unexpected variations by 18%.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2017.09.045