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Hybrid Particle Swarm and Whale Optimization Algorithm for Multi-Visit and Multi-Period Dynamic Workforce Scheduling and Routing Problems

Author

Listed:
  • Voravee Punyakum

    (Research Unit on System Modelling for Industry, Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Kanchana Sethanan

    (Research Unit on System Modelling for Industry, Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Krisanarach Nitisiri

    (Research Unit on System Modelling for Industry, Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Rapeepan Pitakaso

    (Metaheuristics for Logistic Optimization Laboratory, Department of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand)

Abstract

This paper focuses on the dynamic workforce scheduling and routing problem for the maintenance work of harvesters in a sugarcane harvesting operation. Technician teams categorized as mechanical, hydraulic, and electrical teams are assumed to have different skills at different levels to perform services. The jobs are skill-constrained and have time windows. During a working day, a repair request from a sugarcane harvester may arrive, and as time passes, the harvester’s position may shift to other sugarcane fields. We formulated this problem as a multi-visit and multi-period dynamic workforce scheduling and routing problem (MMDWSRP) and our study is the first to address the workforce scheduling and routing problem (WSRP). A mixed-integer programming formulation and a hybrid particle swarm and whale optimization algorithm (HPSWOA) were firstly developed to solve the problem, with the objective of minimizing the total cost, including technician labor cost, penalty for late service, overtime, travel, and subcontracting costs. The HPSWOA was developed for route planning and maintenance work for each mechanical harvester to be provided by technician teams. The proposed algorithm (HPSWOA) was validated against Lingo computational software using numerical experiments in respect of static problems. It was also tested against the current practice, the traditional whale optimization algorithm (WOA), and traditional particle swarm optimization (PSO) in respect of dynamic problems. The computational results show that the HPSWOA yielded a solution with significantly better quality. The HPSWO was also tested against the traditional genetic algorithm (GA), bat algorithm (BA), WOA, and PSO to solve the well-known CEC 2017 benchmark functions. The computational results show that the HPSWOA achieved more superior performance in most cases compared to the GA, BA, WOA, and PSO algorithms.

Suggested Citation

  • Voravee Punyakum & Kanchana Sethanan & Krisanarach Nitisiri & Rapeepan Pitakaso, 2022. "Hybrid Particle Swarm and Whale Optimization Algorithm for Multi-Visit and Multi-Period Dynamic Workforce Scheduling and Routing Problems," Mathematics, MDPI, vol. 10(19), pages 1-20, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3663-:d:934791
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    References listed on IDEAS

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