A Multi-objective Biased Random-Key Genetic Algorithm for Service Technician Routing and Scheduling Problem

Author(s)

Damm, Ricardo de Brito ; Ronconi, Debora P. ; Mes, M ; LallaRuiz, E ; Voss, S

• Document type: Journal article
• Source: COMPUTATIONAL LOGISTICS (ICCL 2021); v. 13004, p. 16-pg., 2021-01-01.

Abstract

Every day many service companies need to plan the tasks that will be carried out by its field staff. Maintenance service technicians have to perform a set of jobs at different locations in a city or state. This problem can be defined as the Service Technician Routing and Scheduling Problem in which tasks have different priorities and time windows, and technicians have different skills and working hours. Scheduling must account for technicians' lunch breaks, which must be respected. Each task is performed by only one technician. To ensure quality customer service and consumer rights are upheld, a novel approach is proposed: to address the problem in a multi-objective context aiming to execute the priority tasks and, simultaneously, to serve the customers at the beginning of their time windows. A Multi-objective Biased Random-Key Genetic Algorithm (BRKGA) was customized to tackle this NP-hard optimization problem and then compared with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The analyzed methods showed similar performance for small instances, but for medium- and large-sized instances the proposed method presented superior performance and more robust results. (AU)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 16/01860-1 - Cutting, packing, lot-sizing, scheduling, routing and location problems and their integration in industrial and logistics settings
• Grantee: Reinaldo Morabito Neto
• Support Opportunities: Research Projects - Thematic Grants