Multiobjective heuristic methods for the field technician scheduling problem

• Grant number: 22/06747-0
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: July 01, 2022
• End date: April 30, 2023
• Field of knowledge: Engineering - Production Engineering - Operational Research
• Principal Investigator: Débora Pretti Ronconi
• Grantee: José Angel Riveaux Merino
• Host Institution: Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 16/01860-1 - Cutting, packing, lot-sizing, scheduling, routing and location problems and their integration in industrial and logistics settings, AP.TEM

Abstract

This project aims to propose a broad study of the service technician routing and scheduling problem. In this problem, technicians have to execute a set of jobs or service tasks on a given period. Tasks are in different locations within a city, with different priorities and time windows. Technicians have different skills and working hours. The project is divided into two parts. In the first part, each task is executed by only one technician and - to prioritize the quality customer service - an approach not found in literature is proposed: to tackle the problem in multiobjective context, simultaneously maximizing the sum of priorities values associated with the tasks performed and to serve priority customers as soon as possible. This is a NP-hard optimization problem and a Genetic Algorithm (GA), which has presented success in similar problems, will be applied. In the second part, a variation of this problem found in the literature will be studied, where teams of technicians with different skills must perform the tasks. The objective function intended to minimize the costs of displacement and outsourcing. In the two parts, the results of the proposed GA will be compared with the methods and results of the literature. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

MELO, RAFAEL A.; RIBEIRO, CELSO C.; RIVEAUX, JOSE A.. A biased random-key genetic algorithm for the minimum quasi-clique partitioning problem. ANNALS OF OPERATIONS RESEARCH, v. N/A, p. 33-pg., 2023-09-28. (22/06747-0)