3 research outputs found
A stochastic integer programming approach to reserve staff scheduling with preferences
De nos jours, atteindre un niveau élevé de satisfaction des employés à l’intérieur d’horaires efficients est une tâche importante et ardue à laquelle les compagnies font face. Dans ce travail, nous abordons une nouvelle variante du problème de création d’horaire de personnel face à une demande inconnue, en tenant compte de la satisfaction des employés via l’incertitude endogène qui découle de la combinaison des préférences des employés envers les horaires, et de ceux qu’ils reçoivent. Nous abordons ce problème dans le contexte de la création d’horaire d’employés remplaçants, un problème opérationnel de l’industrie du transport en commun qui n’a pas encore été étudié, bien qu’assez présent dans les compagnies nord-américaines. Pour faire face aux défis qu’amènent les deux sources d’incertitude, les absences des employés réguliers et des employés remplaçants, nous modélisons ce problème en un programme stochastique en nombres entiers à deux étapes avec recours mixte en nombres entiers. Les décisions de première étape consistent à trouver les journées de congé des employés remplaçants. Une fois que les absences inconnues des employés réguliers sont révélées, les décisions de deuxième étape consistent à planifier les tâches des employés remplaçants. Nous incorporons les préférences des employés remplaçants envers les journées de congé dans notre modèle pour observer à quel point la satisfaction de ces employés peut affecter leurs propres taux d’absence. Nous validons notre approche sur un an de données de la ville de Los Angeles. Notre travail est présentement en cours d’implémentation chez un fournisseur mondial de solutions logicielles pour les opérations de transport en commun.Nowadays, reaching a high level of employee satisfaction in efficient schedules is an important and
difficult task faced by companies. In this work, we tackle a new variant of the personnel scheduling
problem under unknown demand by considering employee satisfaction via endogenous uncertainty
depending on the combination of their preferred and received schedules. We address this problem
in the context of reserve staff scheduling, an operational problem from the transit industry that
has not yet been studied, although rather present in North American transit companies. To
handle the challenges brought by the two uncertainty sources, regular employee and reserve
employee absences, we formulate this problem as a two-stage stochastic integer program with
mixed-integer recourse. The first-stage decisions consist in finding the days off of the reserve
employees. After the unknown regular employee absences are revealed, the second-stage decisions
are to schedule the reserve staff duties. We incorporate reserve employees’ preferences for days
off into the model to examine how employee satisfaction may affect their own absence rates.
We validate our approach on one year of data from the city of Los Angeles. Our work is currently
being implemented in a world-leader software solutions provider for public transit operations
A stochastic integer programming approach to reserve staff scheduling with preferences
Nowadays, reaching a high level of employee satisfaction in efficient
schedules is an important and difficult task faced by companies. We tackle a
new variant of the personnel scheduling problem under unknown demand by
considering employee satisfaction via endogenous uncertainty depending on the
combination of their preferred and received schedules. We address this problem
in the context of reserve staff scheduling, an unstudied operational problem
from the transit industry. To handle the challenges brought by the two
uncertainty sources, regular employee and reserve employee absences, we
formulate this problem as a two-stage stochastic integer program with
mixed-integer recourse. The first-stage decisions consist in finding the days
off of the reserve employees. After the unknown regular employee absences are
revealed, the second-stage decisions are to schedule the reserve staff duties.
We incorporate reserve employees' days-off preferences into the model to
examine how employee satisfaction may affect their own absence rates.Comment: 25 pages, 4 figures, submitted to International Transactions in
Operational Researc
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
This paper reports on the first international competition on AI for the
traveling salesman problem (TSP) at the International Joint Conference on
Artificial Intelligence 2021 (IJCAI-21). The TSP is one of the classical
combinatorial optimization problems, with many variants inspired by real-world
applications. This first competition asked the participants to develop
algorithms to solve a time-dependent orienteering problem with stochastic
weights and time windows (TD-OPSWTW). It focused on two types of learning
approaches: surrogate-based optimization and deep reinforcement learning. In
this paper, we describe the problem, the setup of the competition, the winning
methods, and give an overview of the results. The winning methods described in
this work have advanced the state-of-the-art in using AI for stochastic routing
problems. Overall, by organizing this competition we have introduced routing
problems as an interesting problem setting for AI researchers. The simulator of
the problem has been made open-source and can be used by other researchers as a
benchmark for new AI methods.Comment: 21 page