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Ph.D. position available at IMT Atlantique

Topic: Employee scheduling and routing under uncertainty for home service operations optimization

Applications are invited for a Ph.D. position in Operations Research, based at IMT Atlantique campus Nantes. The 36-month position is a project funded by the French National Research Agency (ANR) under the ANR JCJC project HOPES, financed from 2022 to2026 (https://anr.fr/Project-ANR-21-CE22-0002).

ANR HOPES

The project HOPES (acronym for Home service Operations Planning with EmployeeS preferences and uncertainty) aims to address problems linked to home service (HS) planning and optimization. HS operations planning refers to problems involving employee mobilization to perform work-related activities at geographically dispersed customer locations [1], [2]. In these problems, employees perform more than one activity in a day. For instance, caregivers visit patients at their homes to provide treatment, drivers deliver packages to customers’ homes, and technicians carry out installation or maintenance activities at each customer’s place. Because customers might request multiple services spread over different days, and a demand for continuous operations needs to be satisfied, the most prevalent HS operation planning problems in real-life are multi-period. This important characteristic makes necessary the integration of different decision levels (e.g., tactical employee scheduling and operational employee routing) while optimizing HS operations. Hence, HS operation planning integrates scheduling and routing decisions, in addition to some important features such as individual preferences and uncertainty in demands and supply capacities.

HOPES will propose innovative decision support tools based on the formulation and design of new optimization approaches that will integrate techniques drawn from data science, deterministic optimization, and stochastic optimization. Instead of proposing a method that only works for a specific application, HOPES aims at proposing a general framework that can be extended to solve different variants of the problem. The decision support tools developed in the project are expected to increase the quality of service of the users, the well- being of employees, and to improve the planning and execution of home service operations.

THESIS TOPIC AND MISSIONS

Within HOPES project and the Modeling, Optimization and DEcision for Logistics, Industry and Services (modelis) research team, the recruited person will be responsible for developing optimization approaches to solve employee scheduling and routing problems including work regulations and various sources of uncertainty. To this end, the person will work on two main tasks:

- Multi-period employee scheduling and routing with uncertainty: the objective of this task is to model and to integrate different sources of uncertainty into the scheduling and routing components of the problem. Examples of such sources of uncertainty include the lack of knowledge of whether an order will even materialize at all (in last-mile delivery applications), the arrival of new patients to the system (in home healthcare applications), unpredictable traffic conditions affecting travel times, or random service times. Integrating uncertainty in HS operations planning is of great importance, as no perfect information is available in real-world operations. Ignoring the presence of these sources of uncertainty can frequently lead to situations that are either infeasible or sub-optimal. Some of the tasks include:

Perform a systematic literature review on stochastic vehicle routing and scheduling problems.
Identify the various sources of uncertainty and the correlation between them.
Modelling and integration of different sources of uncertainty.
Implementation of an efficient algorithm to solve the problem.

- Multi-period employee scheduling and routing with employee preferences and uncertainty: The objective of this task is to obtain a general model that simultaneously includes several features of the problem. Since the mathematical formulation will include the interaction between variables from different decision levels, the problem cannot be decomposed into smaller independent problems (e.g. single-period problems) becoming considerably more difficult to solve. Therefore, in this task the candidate will develop advanced solution methods such hybrid methods (matheuristics), or decomposition methods to efficiently solve the problem

The recruited person will also participate in the realization of case studies integrating the data of industrial partners and prospects. The goal is to test and to illustrate the benefits of the developed models and solution approaches on use cases related to home health care and last-mile delivery problems.

The work will be presented at national and international conferences in the operations research field and published in recognized scientific journals.

REQUIRED SKILLS

The recruited person must:

Hold an engineering degree or a master’s in applied mathematics, operations research, or computer science.
Be capable of working in a team in an academic context working closely with professionals in the healthcare and last-mile delivery sectors.
Be autonomous in a programming language (C++, Java, Python or Julia).
Have experience at implementing mathematical models and exact solution methods and/or
metaheuristics to solve optimization problems.
Have experience using tools for data analysis and machine learning.
Be able to communicate in French.
Be able to communicate their work to the scientific community in English.
Knowledge of home healthcare or transport sectors is a plus.

FURTHER INFORMATION

A 36-month contract ideally starting between September 2022 and January 2023, depending on the availability of the recruited person.

Position based at IMT Atlantique Nantes campus, at the Department of Automation, Production and Computer Sciences (DAPI).

HOW TO APPLY:

Applications should be sent by email until May 15, 2022. Please attach a cover letter, a detailed CV, the most recent grades, a list of publications, and a list of 2 to 3 referees.

Contacts:

María I. RESTREPO RUIZ
maria-isabel.restrepo-ruiz@imt-atlantique.fr

Olivier PÉTON
olivier.peton@imt-atlantique.fr

ABOUT IMT ATLANTIQUE
https://www.imt-atlantique.fr

IMT Atlantique is a major generalist engineering school and an international research center dependent on the Ministry in charge of Industry and Digital Technology. Resulting from the merger on January 1, 2017 of Télécom Bretagne and Mines Nantes, it is a school of the Institut Mines-Télécom.

The Automation, Production and Computer Sciences Department (DAPI) of IMT Atlantique is based on the Nantes campus. It has around a hundred people, including around forty permanent faculty members. The research themes of the department are in Control, Robotics, Industrial Engineering, Decision Support and Software Engineering.

LS2N / MODELIS TEAM

DAPI is a stakeholder in the Digital Sciences Laboratory of Nantes (LS2N, UMR CNRS 6004) of which the IMT Atlantique School is one of the supervisory bodies.

The candidate will join the Modeling, Optimization and DEcision for Logistics, Industry and Services (modelis) research team at LS2N. This new team comes from the Logistics and Production Systems (SLP) team of the laboratory. The modelis team develops operational research methods for optimization and decision support in production and logistics. It seeks to solve both open or poorly resolved theoretical problems and applied research problems related to the appearance of new issues, new practices, or data accessible to companies. The key researchers involved in supervising the thesis will be Maria Isabel Restrepo (Associate professor, leader of HOPES project) and Olivier Péton (Professor).

REFERENCES

[1] “Home health care routing andscheduling: A review-ScienceDirect.” https://www.sciencedirect.com/science/article/pii/S0305054816301848 (accessed Feb. 15, 2022).
[2] J.A.Castillo-Salazar,D.Landa-Silva,andR.Qu,“Workforceschedulingandrouting problems: literature survey and computational study,” Ann. Oper. Res., vol. 239, no. 1, pp. 39–67, Apr. 2016, doi: 10.1007/s10479-014-1687-2.