Using real-time information to reschedule jobs in a flowshop with variable processing times

Versión revisada. Embargo 36 mesesIn a time where detailed, instantaneous and accurate information on shop-floor status is becoming available in many manufacturing companies due to Information Technologies initiatives such as Smart Factory or Industry 4.0, a question arises regarding when and how this data can be used to improve scheduling decisions. While it is acknowledged that a continuous rescheduling based on the updated information may be beneficial as it serves to adapt the schedule to unplanned events, this rather general intuition has not been supported by a thorough experimentation, particularly for multi-stage manufacturing systems where such continuous rescheduling may introduce a high degree of nervousness in the system and deteriorates its performance. In order to study this research problem, in this paper we investigate how real-time information on the completion times of the jobs in a flowshop with variable processing times can be used to reschedule the jobs. In an exhaustive computational experience, we show that rescheduling policies pay off as long as the variability of the processing times is not very high, and only if the initially generated schedule is of good quality. Furthermore, we propose several rescheduling policies to improve the performance of continuous rescheduling while greatly reducing the frequency of rescheduling. One of these policies, based on the concept of critical path of a flowshop, outperforms the rest of policies for a wide range of scenarios.Ministerio de Ciencia e Innovación DPI2016-80750-

A permutation flowshop model with time-lags and waiting time preferences of the patients

The permutation flowshop is a widely applied scheduling model. In many real-world applications of this model, a minimum and maximum time-lag must be considered between consecutive operations. We can apply this model to healthcare systems in which the minimum time-lag could be the transfer times, while the maximum time-lag could refer to the number of hours patients must wait. We have modeled a MILP and a constraint programming model and solved them using CPLEX to find exact solutions. Solution times for both methods are presented. We proposed two metaheuristic algorithms based on genetic algorithm and solved and compared them with each other. A sensitivity of analysis of how a change in minimum and maximum time-lags can impact waiting time and Cmax of the patients is performed. Results suggest that constraint programming is a more efficient method to find exact solutions and changes in the values of minimum and maximum time-lags can impact waiting times of the patients and Cmax significantly

Spatial-temporal data modelling and processing for personalised decision support

The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio

Spatial-temporal data modelling and processing for personalised decision support

The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio

The distributed assembly permutation flowshop scheduling problem

Nowadays, improving the management of complex supply chains is a key to become competitive in the twenty-first century global market. Supply chains are composed of multi-plant facilities that must be coordinated and synchronised to cut waste and lead times. This paper proposes a Distributed Assembly Permutation Flowshop Scheduling Problem (DAPFSP) with two stages to model and study complex supply chains. This problem is a generalisation of the Distributed Permutation Flowshop Scheduling Problem (DPFSP). The first stage of the DAPFSP is composed of f identical production factories. Each one is a flowshop that produces jobs to be assembled into final products in a second assembly stage. The objective is to minimise the makespan. We present first a Mixed Integer Linear Programming model (MILP). Three constructive algorithms are proposed. Finally, a Variable Neighbourhood Descent (VND) algorithm has been designed and tested by a comprehensive ANOVA statistical analysis. The results show that the VND algorithm offers good performance to solve this scheduling problem.Ruben Ruiz is partially supported by the Spanish Ministry of Science and Innovation, under the project 'RESULT - Realistic Extended Scheduling Using Light Techniques' with reference DPI2012-36243-C02-01. Carlos Andres-Romano is partially supported by the Spanish Ministry of Science and Innovation, under the project 'INSAMBLE' - Scheduling at assembly/disassembly synchronised supply chains with reference DPI2011-27633.Hatami, S.; Ruiz García, R.; Andrés Romano, C. (2013). The distributed assembly permutation flowshop scheduling problem. International Journal of Production Research. 51(17):5292-5308. https://doi.org/10.1080/00207543.2013.807955S529253085117Basso, D., Chiarandini, M., & Salmaso, L. (2007). Synchronized permutation tests in replicated designs. Journal of Statistical Planning and Inference, 137(8), 2564-2578. doi:10.1016/j.jspi.2006.04.016Biggs, D., De Ville, B., & Suen, E. (1991). A method of choosing multiway partitions for classification and decision trees. Journal of Applied Statistics, 18(1), 49-62. doi:10.1080/02664769100000005Chan, F. T. S., Chung, S. H., Chan, L. Y., Finke, G., & Tiwari, M. K. (2006). Solving distributed FMS scheduling problems subject to maintenance: Genetic algorithms approach. Robotics and Computer-Integrated Manufacturing, 22(5-6), 493-504. doi:10.1016/j.rcim.2005.11.005Chan, F. T. S., Chung, S. H., & Chan, P. L. Y. (2006). Application of genetic algorithms with dominant genes in a distributed scheduling problem in flexible manufacturing systems. International Journal of Production Research, 44(3), 523-543. doi:10.1080/00207540500319229Liao, C.-J., & Liao, L.-M. (2008). Improved MILP models for two-machine flowshop with batch processing machines. Mathematical and Computer Modelling, 48(7-8), 1254-1264. doi:10.1016/j.mcm.2008.01.001Framinan, J. M., & Leisten, R. (2003). An efficient constructive heuristic for flowtime minimisation in permutation flow shops. Omega, 31(4), 311-317. doi:10.1016/s0305-0483(03)00047-1Gao, J., & Chen, R. (2011). A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem. International Journal of Computational Intelligence Systems, 4(4), 497-508. doi:10.1080/18756891.2011.9727808Hansen, P., & Mladenović, N. (2001). Variable neighborhood search: Principles and applications. European Journal of Operational Research, 130(3), 449-467. doi:10.1016/s0377-2217(00)00100-4Hariri, A. M. A., & Potts, C. N. (1997). A branch and bound algorithm for the two-stage assembly scheduling problem. European Journal of Operational Research, 103(3), 547-556. doi:10.1016/s0377-2217(96)00312-8Jia, H. Z., Fuh, J. Y. H., Nee, A. Y. C., & Zhang, Y. F. (2002). Web-based Multi-functional Scheduling System for a Distributed Manufacturing Environment. Concurrent Engineering, 10(1), 27-39. doi:10.1177/1063293x02010001054Jia, H. Z., Nee, A. Y. C., Fuh, J. Y. H., & Zhang, Y. F. (2003). Journal of Intelligent Manufacturing, 14(3/4), 351-362. doi:10.1023/a:1024653810491Jia, H. Z., Fuh, J. Y. H., Nee, A. Y. C., & Zhang, Y. F. (2007). Integration of genetic algorithm and Gantt chart for job shop scheduling in distributed manufacturing systems. Computers & Industrial Engineering, 53(2), 313-320. doi:10.1016/j.cie.2007.06.024Kass, G. V. (1980). An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics, 29(2), 119. doi:10.2307/2986296Lee, C.-Y., Cheng, T. C. E., & Lin, B. M. T. (1993). Minimizing the Makespan in the 3-Machine Assembly-Type Flowshop Scheduling Problem. Management Science, 39(5), 616-625. doi:10.1287/mnsc.39.5.616Morgan, J. N., & Sonquist, J. A. (1963). Problems in the Analysis of Survey Data, and a Proposal. Journal of the American Statistical Association, 58(302), 415-434. doi:10.1080/01621459.1963.10500855Pan, Q.-K., & Ruiz, R. (2012). Local search methods for the flowshop scheduling problem with flowtime minimization. European Journal of Operational Research, 222(1), 31-43. doi:10.1016/j.ejor.2012.04.034Potts, C. N., Sevast’janov, S. V., Strusevich, V. A., Van Wassenhove, L. N., & Zwaneveld, C. M. (1995). The Two-Stage Assembly Scheduling Problem: Complexity and Approximation. Operations Research, 43(2), 346-355. doi:10.1287/opre.43.2.346Ruiz, 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., Şerifoğlu, F. S., & Urlings, T. (2008). Modeling realistic hybrid flexible flowshop scheduling problems. Computers & Operations Research, 35(4), 1151-1175. doi:10.1016/j.cor.2006.07.014Ruiz, R., & Andrés-Romano, C. (2011). Scheduling unrelated parallel machines with resource-assignable sequence-dependent setup times. The International Journal of Advanced Manufacturing Technology, 57(5-8), 777-794. doi:10.1007/s00170-011-3318-2Stafford, E. F., Tseng, F. T., & Gupta, J. N. D. (2005). Comparative evaluation of MILP flowshop models. Journal of the Operational Research Society, 56(1), 88-101. doi:10.1057/palgrave.jors.2601805Tozkapan, A., Kırca, Ö., & Chung, C.-S. (2003). A branch and bound algorithm to minimize the total weighted flowtime for the two-stage assembly scheduling problem. Computers & Operations Research, 30(2), 309-320. doi:10.1016/s0305-0548(01)00098-3Tseng, F. T., & Stafford, E. F. (2008). New MILP models for the permutation flowshop problem. Journal of the Operational Research Society, 59(10), 1373-1386. doi:10.1057/palgrave.jors.260245

An estimation of distribution algorithm for lot-streaming flow shop problems with setup times

Lot-streaming flow shops have important applications in different industries including textile, plastic, chemical, semiconductor and many others. This paper considers an n-job m-machine lot-streaming flow shop scheduling problem with sequence-dependent setup times under both the idling and noidling production cases. The objective is to minimize the maximum completion time or makespan. To solve this important practical problem, a novel estimation of distribution algorithm (EDA) is proposed with a job permutation based representation. In the proposed EDA, an efficient initialization scheme based on the NEH heuristic is presented to construct an initial population with a certain level of quality and diversity. An estimation of a probabilistic model is constructed to direct the algorithm search towards good solutions by taking into account both job permutation and similar blocks of jobs. A simple but effective local search is added to enhance the intensification capability. A diversity controlling mechanism is applied to maintain the diversity of the population. In addition, a speed-up method is presented to reduce the computational effort needed for the local search technique and the NEH-based heuristics. A comparative evaluation is carried out with the best performing algorithms from the literature. The results show that the proposed EDA is very effective in comparison after comprehensive computational and statistical analyses.This research is partially supported by the National Science Foundation of China (60874075, 70871065), and Science Foundation of Shandong Province in China under Grant BS2010DX005, and Postdoctoral Science Foundation of China under Grant 20100480897. Ruben Ruiz is partially funded by the Spanish Ministry of Science and Innovation, under the project "SMPA-Advanced Parallel Multiobjective Sequencing: Practical and Theoretical Advances" with reference DPI2008-03511/DPI and by the IMPIVA-Institute for the Small and Medium Valencian Enterprise, for the project OSC with references IMIDIC/2008/137, IMIDIC/2009/198 and IMIDIC/2010/175.Pan, Q.; Ruiz García, R. (2012). An estimation of distribution algorithm for lot-streaming flow shop problems with setup times. Omega. 40(2):166-180. https://doi.org/10.1016/j.omega.2011.05.002S16618040

Scheduling flow lines with buffers by ant colony digraph

This work starts from modeling the scheduling of n jobs on m machines/stages as flowshop with buffers in manufacturing. A mixed-integer linear programing model is presented, showing that buffers of size n - 2 allow permuting sequences of jobs between stages. This model is addressed in the literature as non-permutation flowshop scheduling (NPFS) and is described in this article by a disjunctive graph (digraph) with the purpose of designing specialized heuristic and metaheuristics algorithms for the NPFS problem. Ant colony optimization (ACO) with the biologically inspired mechanisms of learned desirability and pheromone rule is shown to produce natively eligible schedules, as opposed to most metaheuristics approaches, which improve permutation solutions found by other heuristics. The proposed ACO has been critically compared and assessed by computation experiments over existing native approaches. Most makespan upper bounds of the established benchmark problems from Taillard (1993) and Demirkol, Mehta, and Uzsoy (1998) with up to 500 jobs on 20 machines have been improved by the proposed ACO