Replication Data for: Simulation of Heuristics for Automated Guided Vehicle Task Sequencing with Resource Sharing and Dynamic Queues
Instances for the task sequencing problem analysed in the paper "Simulation of Heuristics for Automated Guided Vehicle Task Sequencing with Resource Sharing and Dynamic Queues". The instances were generated based on an industrial scenario at a manufacturing company in Spain but modified to...
| Autores: | , , , |
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| Tipo de recurso: | conjunto de datos |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Consorci de Serveis Universitaris de Catalunya (CSUC) |
| Repositorio: | CORA.Repositori de Dades de Recerca |
| OAI Identifier: | oai:dnet:cora.rdr____::628c54f777809e998c8d1831c1257dcb |
| Acceso en línea: | https://doi.org/10.34810/DATA1766 |
| Access Level: | acceso abierto |
| Palabra clave: | Computer and Information Science Automated guided vehicle systems Heuristic algorithms Discrete-event simulation |
| Sumario: | Instances for the task sequencing problem analysed in the paper "Simulation of Heuristics for Automated Guided Vehicle Task Sequencing with Resource Sharing and Dynamic Queues". The instances were generated based on an industrial scenario at a manufacturing company in Spain but modified to protect the Company interests. The company operates a production facility equipped with several AGVs responsible for collecting and transporting products between designated plant locations. An instance is defined by the following factors: the number of workstations (m), the number of AGVs available (r), the number of tasks to be performed (t), the travel and processing times for each task, and the allocation of tasks to workstations (m) and AGVs (r). Two distinct instance types were considered for each workstation–AGV–task combination regarding processing time. The first type features processing times, derived from a uniform random distribution, tightly centered around an average value, resulting in instances with low variability in processing times (LVPT). In contrast, the second type showcases a broader range between minimum and maximum processing times, leading to instances with high variability in processing times (HVPT). Similarly, for assigning tasks to workstation–AGV combinations, two instance types were created. The first type ensures a balanced assignment, resulting in low variability in the task assignment (LVTA) to each workstation–AGV combination. Conversely, the second type introduces an unbalanced assignment, causing some workstation–AGV combinations to handle more work than others, thereby leading to high variability in task assignments (HVTAs). Combining these characteristics results in the following four distinct instance types for assessing the performance of the algorithms: (01) low variability in processing times and low variability in task assignment (LVPT/LVTA); (02) high variability in processing times and low variability in task assignment (HVPT/LVTA); (03) low variability in processing times and high variability in task assignment (LVPT/HVTA); and (04) high variability in processing times and task assignment (HVPT/HVTA). For example, the instance file named "m10r5t300_02.txt" contains 10 machines (m), 5 AGVs (r), and 300 tasks (t), and the variability type is (02) high variability in processing times and low variability in task assignment (HVPT/LVTA). |
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