Operations planning test bed under rolling horizons, multiproduct,multiechelon, multiprocess for capacitated production planning modelling with strokes

[EN] One of the problems when conducting research in mathematical programming models for operations planning is having an adequate database of experiments that can be used to verify advances and developments with enough factors to understand different consequences. This paper presents a test bed generator and instances database for a rolling horizons analysis for multiechelon planning, multiproduct with alternatives processes, multistroke, multicapacity with different stochastic demand patterns to be used with a stroke-like bill of materials considering production costs, setup, storage and delays for operations management. From the analysis of the operations planning obtained from this test bed, it is concluded that a product structure with an alternative process obtains the lowest total cost and the highest service level. In addition, decreasing seasonal demand could present a lower total cost than constant demand, but would generate a worse service level. This test bed will allow researchers further investigation so as to verify improvements in forecast methods, rolling horizons parameters, employed software, etc.Rius-Sorolla, G.; Maheut, J.; Estelles Miguel, S.; García Sabater, JP. (2021). Operations planning test bed under rolling horizons, multiproduct,multiechelon, multiprocess for capacitated production planning modelling with strokes. Central European Journal of Operations Research. 29:1289-1315. https://doi.org/10.1007/s10100-020-00687-5S1289131529Araujo SA, Arenales MN, Clark A (2007) Joint rolling-horizon scheduling of materials processing and lot-sizing with sequence-dependent setups. J Heuristics 13(4):337–358. https://doi.org/10.1007/s10732-007-9011-9ASIC (2018) Clúster de cálculo: Rigel. http://www.upv.es/entidades/ASIC/catalogo/857893normalc.html. Accessed date 22 July 2018Baker KR (1977) An experimental study of the effectiveness of rolling schedules in production planning. 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