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Thematische Bibliographien / Workforce Scheduling and Routing Problem

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Auswahl der wissenschaftlichen Literatur zum Thema „Workforce Scheduling and Routing Problem“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

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Zeitschriftenartikel zum Thema "Workforce Scheduling and Routing Problem"

1

Pereira, Dilson Lucas, Júlio César Alves, and Mayron César de Oliveira Moreira. "A multiperiod workforce scheduling and routing problem with dependent tasks." Computers & Operations Research 118 (June 2020): 104930. http://dx.doi.org/10.1016/j.cor.2020.104930.

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2

Punyakum, Voravee, Kanchana Sethanan, Krisanarach Nitisiri, and Rapeepan Pitakaso. "Hybrid Particle Swarm and Whale Optimization Algorithm for Multi-Visit and Multi-Period Dynamic Workforce Scheduling and Routing Problems." Mathematics 10, no. 19 (2022): 3663. http://dx.doi.org/10.3390/math10193663.

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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.

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3

Xie, Fulin, Chris N. Potts, and Tolga Bektaş. "Iterated local search for workforce scheduling and routing problems." Journal of Heuristics 23, no. 6 (2017): 471–500. http://dx.doi.org/10.1007/s10732-017-9347-8.

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4

Algethami, Haneen, Anna Martínez-Gavara, and Dario Landa-Silva. "Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem." Journal of Heuristics 25, no. 4-5 (2018): 753–92. http://dx.doi.org/10.1007/s10732-018-9385-x.

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5

Castillo-Salazar, J. Arturo, Dario Landa-Silva, and Rong Qu. "Workforce scheduling and routing problems: literature survey and computational study." Annals of Operations Research 239, no. 1 (2014): 39–67. http://dx.doi.org/10.1007/s10479-014-1687-2.

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6

Urquhart, Neil, Emma Hart, and William Hutcheson. "Using MAP-Elites to support policy making around Workforce Scheduling and Routing." at - Automatisierungstechnik 68, no. 2 (2020): 110–17. http://dx.doi.org/10.1515/auto-2019-0107.

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AbstractAlgorithms such as MAP-Elites provide a means of allowing users to explore a solution space by returning an archive of high-performing solutions. Such an archive, can allow the user an overview of the solution space which may be useful when formulating policy around the problem itself. The number of solutions that can potentially be returned by MAP-Elites is controlled by a parameter d that discretises the user-defined features into ‘bins’. For a fixed evaluation budget, increasing the number of bins increases user-choice, but at the same time, may lead to a reduction in overall quality of solutions. We undertake a study of the application of Map-Elites to a Workforce Scheduling and Routing problem, using a set of realistic instances based in London.

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7

Fukunaga, Takashi, and Junko Hosoda. "Workforce scheduling and routing problem for maintenance work with various work condition on sites." Proceedings of Manufacturing Systems Division Conference 2022 (2022): 211. http://dx.doi.org/10.1299/jsmemsd.2022.211.

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8

Eliiyi, Uğur, Onur Uğurlu, and Sel Tatari. "A Sequential Workforce Scheduling and Routing Problem for the Retail Industry: A Case Study." Journal of Transportation and Logistics 7, no. 2 (2022): 479–98. http://dx.doi.org/10.26650/jtl.2022.1096085.

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9

Dahite, Lamiaa, Abdeslam Kadrani, Rachid Benmansour, Rym Nesrine Guibadj, and Cyril Fonlupt. "Multi-Objective Model and Variable Neighborhood Search Algorithms for the Joint Maintenance Scheduling and Workforce Routing Problem." Mathematics 10, no. 11 (2022): 1807. http://dx.doi.org/10.3390/math10111807.

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This paper addresses a problem faced by maintenance service providers: performing maintenance activities at the right time on geographically distributed machines subjected to random failures. This problem requires determining for each technician the sequence of maintenance operations to perform to minimize the total expected costs while ensuring a high level of machine availability. To date, research in this area has dealt with routing and maintenance schedules separately. This study aims to determine the optimal maintenance and routing plan simultaneously. A new bi-objective mathematical model that integrates both routing and maintenance considerations is proposed for time-based preventive maintenance. The first objective is to minimize the travel cost related to technicians’ routing. The second objective can either minimize the total preventive and corrective maintenance cost or the failure cost. New general variable neighborhood search (GVNS) and variable neighborhood descent (VND) algorithms based on the Pareto dominance concept are proposed and performed over newly generated instances. The efficiency of our approach is demonstrated through several experiments. Compared to the commercial solver and existing multi-objective VND and GVNS, these new algorithms obtain highly competitive results on both mono-objective and bi-objective variants.

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10

Çakırgil, Seray, Eda Yücel, and Gültekin Kuyzu. "An integrated solution approach for multi-objective, multi-skill workforce scheduling and routing problems." Computers & Operations Research 118 (June 2020): 104908. http://dx.doi.org/10.1016/j.cor.2020.104908.

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