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

Autor(es)

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

• Tipo de documento: Artigo Científico
• Fonte: COMPUTATIONAL LOGISTICS (ICCL 2021); v. 13004, p. 16-pg., 2021-01-01.

Resumo

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)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 16/01860-1 - Problemas de corte, empacotamento, dimensionamento de lotes, programação da produção, roteamento, localização e suas integrações em contextos industriais e logísticos
• Beneficiário: Reinaldo Morabito Neto
• Modalidade de apoio: Auxílio à Pesquisa - Temático