Energy-Efficient Flexible Flow Shop Scheduling With Due Date and Total Flow Time

One of the most significant optimization issues facing a manufacturing company is the flexible flow shop scheduling problem (FFSS). However, FFSS with uncertainty and energy-related elements has received little investigation. Additionally, in order to reduce overall waiting times and earliness/tardiness issues, the topic of flexible flow shop scheduling with shared due dates is researched. Using transmission line loadings and bus voltage magnitude variations, an unique severity function is formulated in this research. Optimize total energy consumption, total agreement index, and make span all at once. Many different meta-heuristics have been presented in the past to find near-optimal answers in an acceptable amount of computation time. To explore the potential for energy saving in shop floor management, a multi-level optimization technique for flexible flow shop scheduling and integrates power models for individual machines with cutting parameters optimisation into energy-efficient scheduling issues is proposed. However, it can be difficult and time-consuming to fine-tune algorithm-specific parameters for solving FFSP

Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems

[EN] Industrial production scheduling problems are challenges that researchers have been trying to solve for decades. Many practical scheduling problems such as the hybrid flowshop are ATP-hard. As a result, researchers resort to metaheuristics to obtain effective and efficient solutions. The traditional design process of metaheuristics is mainly manual, often metaphor-based, biased by previous experience and prone to producing overly tailored methods that only work well on the tested problems and objectives. In this paper, we use an Automatic Algorithm Design (AAD) methodology to eliminate these limitations. AAD is capable of composing algorithms from components with minimal human intervention. We test the proposed MD for three different optimization objectives in the hybrid flowshop. Comprehensive computational and statistical testing demonstrates that automatically designed algorithms outperform specifically tailored state-of-the-art methods for the tested objectives in most cases.Pedro Alfaro-Fernandez and Ruben Ruiz are partially supported by the Spanish Ministry of Science, Innovation, and Universities, under the project "OPTEP-Port Terminal Operations Optimization" (No. RTI2018-094940-B-I00) financed with FEDER funds and under grants BES-2013-064858 and EEBB-I-15-10089. This work was supported by the COMEX project (P7/36) within the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office. Thomas Stiitzle acknowledges support from the Belgian F.R.S.-FNRS, of which he is a Research Director.Alfaro-Fernandez, P.; Ruiz García, R.; Pagnozzi, F.; Stützle, T. (2020). Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems. European Journal of Operational Research. 282(3):835-845. https://doi.org/10.1016/j.ejor.2019.10.004S8358452823Bożejko, W., Gnatowski, A., Niżyński, T., Affenzeller, M., & Beham, A. (2018). Local Optima Networks in Solving Algorithm Selection Problem for TSP. Advances in Intelligent Systems and Computing, 83-93. doi:10.1007/978-3-319-91446-6_9Bożejko, W., Pempera, J., & Smutnicki, C. (2013). Parallel tabu search algorithm for the hybrid flow shop problem. Computers & Industrial Engineering, 65(3), 466-474. doi:10.1016/j.cie.2013.04.007Burke, E. K., Hyde, M. R., & Kendall, G. (2012). Grammatical Evolution of Local Search Heuristics. IEEE Transactions on Evolutionary Computation, 16(3), 406-417. doi:10.1109/tevc.2011.2160401Cahon, S., Melab, N., & Talbi, E.-G. (2004). ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics. Journal of Heuristics, 10(3), 357-380. doi:10.1023/b:heur.0000026900.92269.ecCarlier, J., & Neron, E. (2000). An Exact Method for Solving the Multi-Processor Flow-Shop. RAIRO - Operations Research, 34(1), 1-25. doi:10.1051/ro:2000103Chung, T.-P., & Liao, C.-J. (2013). An immunoglobulin-based artificial immune system for solving the hybrid flow shop problem. Applied Soft Computing, 13(8), 3729-3736. doi:10.1016/j.asoc.2013.03.006Cui, Z., & Gu, X. (2015). An improved discrete artificial bee colony algorithm to minimize the makespan on hybrid flow shop problems. Neurocomputing, 148, 248-259. doi:10.1016/j.neucom.2013.07.056Ding, J.-Y., Song, S., Gupta, J. N. D., Zhang, R., Chiong, R., & Wu, C. (2015). An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem. Applied Soft Computing, 30, 604-613. doi:10.1016/j.asoc.2015.02.006Dubois-Lacoste, J., López-Ibáñez, M., & Stützle, T. (2011). A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems. Computers & Operations Research, 38(8), 1219-1236. doi:10.1016/j.cor.2010.10.008Dubois-Lacoste, J., Pagnozzi, F., & Stützle, T. (2017). An iterated greedy algorithm with optimization of partial solutions for the makespan permutation flowshop problem. Computers & Operations Research, 81, 160-166. doi:10.1016/j.cor.2016.12.021Gupta, J. N. D. (1988). Two-Stage, Hybrid Flowshop Scheduling Problem. Journal of the Operational Research Society, 39(4), 359-364. doi:10.1057/jors.1988.63Gupta, J. N. D., & Stafford, E. F. (2006). Flowshop scheduling research after five decades. European Journal of Operational Research, 169(3), 699-711. doi:10.1016/j.ejor.2005.02.001Hidri, L., & Haouari, M. (2011). Bounding strategies for the hybrid flow shop scheduling problem. Applied Mathematics and Computation, 217(21), 8248-8263. doi:10.1016/j.amc.2011.02.108Hutter, F., Hoos, H. H., Leyton-Brown, K., & Stuetzle, T. (2009). ParamILS: An Automatic Algorithm Configuration Framework. Journal of Artificial Intelligence Research, 36, 267-306. doi:10.1613/jair.2861Johnson, S. M. (1954). Optimal two- and three-stage production schedules with setup times included. Naval Research Logistics Quarterly, 1(1), 61-68. doi:10.1002/nav.3800010110Khalouli, S., Ghedjati, F., & Hamzaoui, A. (2010). A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop. Engineering Applications of Artificial Intelligence, 23(5), 765-771. doi:10.1016/j.engappai.2010.01.008KhudaBukhsh, A. R., Xu, L., Hoos, H. H., & Leyton-Brown, K. (2016). SATenstein: Automatically building local search SAT solvers from components. Artificial Intelligence, 232, 20-42. doi:10.1016/j.artint.2015.11.002Li, J., Pan, Q., & Wang, F. (2014). A hybrid variable neighborhood search for solving the hybrid flow shop scheduling problem. Applied Soft Computing, 24, 63-77. doi:10.1016/j.asoc.2014.07.005Liao, C.-J., Tjandradjaja, E., & Chung, T.-P. (2012). An approach using particle swarm optimization and bottleneck heuristic to solve hybrid flow shop scheduling problem. Applied Soft Computing, 12(6), 1755-1764. doi:10.1016/j.asoc.2012.01.011Lopez-Ibanez, M., & Stutzle, T. (2012). The Automatic Design of Multiobjective Ant Colony Optimization Algorithms. IEEE Transactions on Evolutionary Computation, 16(6), 861-875. doi:10.1109/tevc.2011.2182651López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Birattari, M., & Stützle, T. (2016). The irace package: Iterated racing for automatic algorithm configuration. Operations Research Perspectives, 3, 43-58. doi:10.1016/j.orp.2016.09.002Marichelvam, M. K., Prabaharan, T., & Yang, X. S. (2014). A Discrete Firefly Algorithm for the Multi-Objective Hybrid Flowshop Scheduling Problems. IEEE Transactions on Evolutionary Computation, 18(2), 301-305. doi:10.1109/tevc.2013.2240304Marichelvam, M. K., Prabaharan, T., & Yang, X. S. (2014). Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan. Applied Soft Computing, 19, 93-101. doi:10.1016/j.asoc.2014.02.005Marichelvam, M. K., Prabaharan, T., Yang, X. S., & Geetha, M. (2013). Solving hybrid flow shop scheduling problems using bat algorithm. International Journal of Logistics Economics and Globalisation, 5(1), 15. doi:10.1504/ijleg.2013.054428Mascia, F., López-Ibáñez, M., Dubois-Lacoste, J., & Stützle, T. (2014). Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools. Computers & Operations Research, 51, 190-199. doi:10.1016/j.cor.2014.05.020Nawaz, M., Enscore, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11(1), 91-95. doi:10.1016/0305-0483(83)90088-9Pan, Q.-K., & Dong, Y. (2014). An improved migrating birds optimisation for a hybrid flowshop scheduling with total flowtime minimisation. Information Sciences, 277, 643-655. doi:10.1016/j.ins.2014.02.152Pan, Q.-K., Ruiz, R., & Alfaro-Fernández, P. (2017). Iterated search methods for earliness and tardiness minimization in hybrid flowshops with due windows. Computers & Operations Research, 80, 50-60. doi:10.1016/j.cor.2016.11.022Pan, Q.-K., Wang, L., Li, J.-Q., & Duan, J.-H. (2014). A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation. Omega, 45, 42-56. doi:10.1016/j.omega.2013.12.004Rajendran, C., & Ziegler, H. (1997). An efficient heuristic for scheduling in a flowshop to minimize total weighted flowtime of jobs. European Journal of Operational Research, 103(1), 129-138. doi:10.1016/s0377-2217(96)00273-1Ruiz, R., & Stützle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033-2049. doi:10.1016/j.ejor.2005.12.009Ruiz, R., & Vázquez-Rodríguez, J. A. (2010). The hybrid flow shop scheduling problem. European Journal of Operational Research, 205(1), 1-18. doi:10.1016/j.ejor.2009.09.024Sörensen, K. (2013). Metaheuristics-the metaphor exposed. International Transactions in Operational Research, 22(1), 3-18. doi:10.1111/itor.12001Vignier, A., Billaut, J.-C., & Proust, C. (1999). Les problèmes d’ordonnancement de type flow-shop hybride : état de l’art. RAIRO - Operations Research, 33(2), 117-183. doi:10.1051/ro:1999108Wang, S., Wang, L., Liu, M., & Xu, Y. (2013). An enhanced estimation of distribution algorithm for solving hybrid flow-shop scheduling problem with identical parallel machines. The International Journal of Advanced Manufacturing Technology, 68(9-12), 2043-2056. doi:10.1007/s00170-013-4819-yXu, Y., Wang, L., Wang, S., & Liu, M. (2013). An effective shuffled frog-leaping algorithm for solving the hybrid flow-shop scheduling problem with identical parallel machines. Engineering Optimization, 45(12), 1409-1430. doi:10.1080/0305215x.2012.73778

Mejora de tiempos de entrega en un flow shop híbrido flexible usando técnicas inteligentes. Aplicación en la industria de tejidos técnicos

Se busca aportar herramientas útiles para la programación de producción en la industria de tejidos técnicos. Se parte de las condiciones actuales de la programación de producción en este tipo de industria y de los antecedentes en la literatura científica sobre modelos aplicables a estos entornos. Se propone un modelo de solución por técnicas inteligentes a la problemática de la secuenciación y asignación de tareas en los entornos flow shop híbrido flexible considerando situaciones como: paralelismo entre máquinas no relacionadas, tiempos de montaje dependientes de la secuencia, entrada dinámica de trabajos, restricción de elegibilidad, maleabilidad y lotes de transferencia variables entre etapas. De allí se construye la propuesta de solución que involucra simultáneamente todas las condiciones de entorno real mencionadas y aplica un algoritmo genético modificado de acuerdo a las características del problema. Se concluye que el modelado considerando condiciones realistas es posible, que los algoritmos genéticos son una opción práctica para entornos reales y que las empresas pueden obtener mejoras en su capacidad de respuesta con este tipo de solucionesAbstract : It seeks to provide useful tools for production scheduling in the technical textiles industry. It begins in the current conditions of production scheduling in this type of industry and the background in scientific literature, applicable to these environments models. The mathematical model to solve the problem of sequencing and assigning jobs in Flexible hybrid flow shop environments is developed considering: unrelated parallel machines, sequence dependent setup time, dynamic entry of jobs, availability constrain, malleability and variable transfer batches between stages. The solution proposal is build including all actual environment features considered together and applying a modified genetic algorithm modeled according to the problem. It is concluded that the model of scheduling problems considering realistic conditions is possible, that genetic algorithms are a practical option for real environments, and that companies can achieve improvements in their responsiveness with this kind of solutionsDoctorad