58,041 research outputs found

    Scheduling unrelated parallel machines with resource-assignable sequence-dependent setup times

    Full text link
    [EN] A novel scheduling problem that results from the addition of resource-assignable setups is presented in this paper. We consider an unrelated parallel machine problem with machine and job sequence-dependent setup times. The new characteristic is that the amount of setup time does not only depend on the machine and job sequence but also on the amount of resources assigned, which can vary between a minimum and a maximum. The aim is to give solution to real problems arising in several industries where frequent setup operations in production lines have to be carried out. These operations are indeed setups whose length can be reduced or extended according to the amount of resources assigned to them. The objective function considered is a linear combination of total completion time and the total amount of resources assigned. We present a mixed integer program (MIP) model and some fast dispatching heuristics. We carry out careful and comprehensive statistical analyses to study what characteristics of the problem affect the MIP model performance. We also study the effectiveness of the different heuristics proposed. © 2011 Springer-Verlag London Limited.The authors are indebted to the referees and editor for a close examination of the paper, which has increased its quality and presentation. This work 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. The authors should also thank 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.Ruiz García, R.; Andrés Romano, C. (2011). Scheduling unrelated parallel machines with resource-assignable sequence-dependent setup times. International Journal of Advanced Manufacturing Technology. 57(5):777-794. https://doi.org/10.1007/S00170-011-3318-2S777794575Allahverdi A, Gupta JND, Aldowaisan T (1999) A review of scheduling research involving setup considerations. OMEGA Int J Manag Sci 27(2):219–239Allahverdi A, Ng CT, Cheng TCE, Kovalyov MY (2008) A survey of scheduling problems with setup times or costs. Eur J Oper Res 187(3):985–1032Balakrishnan N, Kanet JJ, Sridharan SV (1999) Early/tardy scheduling with sequence dependent setups on uniform parallel machines. Comput Oper Res 26(2):127–141Biggs D, De Ville B, and Suen E (1991) A method of choosing multiway partitions for classification and decision trees. J Appl Stat 18(1):49–62Chen J-F (2006) Unrelated parallel machine scheduling with secondary resource constraints. Int J Adv Manuf Technol 26(3):285–292Cheng TCE, Sin CCS (1990) A state-of-the-art review of parallel machine scheduling research. Eur J Oper Res 47(3):271–292Graham RL, Lawler EL, Lenstra JK, Rinnooy Kan AHG (1979) Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann Discrete Math 5:287–326Grigoriev E, Sviridenko M, Uetz M (2007) Unrelated parallel machine scheduling with resource dependent processing times. Math Program Ser A and B 110(1):209–228Guinet A (1991) Textile production systems: a succession of non-identical parallel processor shops. J Oper Res Soc 42(8):655–671Guinet A, Dussauchoy A (1993) Scheduling sequence dependent jobs on identical parallel machines to minimize completion time criteria. Int J Prod Res 31(7):1579–1594Horn WA (1973) Minimizing average flow time with parallel machines. Oper Res 21(3):846–847Kass GV (1980) An exploratory technique for investigating large quantities of categorical data. Appl Stat 29(2):119–127Kim DW, Kim KH, Jang W, Chen FF (2002) Unrelated parallel machine scheduling with setup times using simulated annealing. Robot Comput-Integr Manuf 18(3–4):223–231Lam K, Xing W (1997) New trends in parallel machine scheduling. Int J Oper Prod Manage 17(3):326–338Lee YH, Pinedo M (1997) Scheduling jobs on parallel machines with sequence dependent setup times. Eur J Oper Res 100(3):464–474Marsh JD, Montgomery DC (1973) Optimal procedures for scheduling jobs with sequence-dependent changeover times on parallel processors. AIIE Technical Papers, pp 279–286Mokotoff E (2001) Parallel machine scheduling problems: a survey. Asia-Pac J Oper Res 18(2):193–242Morgan JA, Sonquist JN (1963) Problems in the analysis of survey data and a proposal. J Am Stat Assoc 58:415–434Ng CT, Edwin Cheng TC, Janiak A, Kovalyov MY (2005) Group scheduling with controllable setup and processing times: minimizing total weighted completion time. Ann Oper Res 133:163–174Nowicki E, Zdrzalka S (1990) A survey of results for sequencing problems with controllable processing times. Discrete Appl Math 26(2–3):271–287Pinedo M (2002) Scheduling: theory, algorithms, and systems, 2nd edn. Prentice Hall, Upper SaddleRabadi G, Moraga RJ, Al-Salem A (2006) Heuristics for the unrelated parallel machine scheduling problem with setup times. J Intell Manuf 17(1):85–97Radhakrishnan S, Ventura JA (2000) Simulated annealing for parallel machine scheduling with earliness-tardiness penalties and sequence-dependent set-up times. Int J Prod Res 38(10):2233–2252Ruiz R, Sivrikaya Şerifoğlu F, Urlings T (2008) Modeling realistic hybrid flexible flowshop scheduling problems. Comput Oper Res 35(4):1151–1175Sivrikaya-Serifoglu F, Ulusoy G (1999) Parallel machine scheduling with earliness and tardiness penalties. Comput Oper Res 26(8):773–787Webster ST (1997) The complexity of scheduling job families about a common due date. Oper Res Lett 20(2):65–74Weng MX, Lu J, Ren H (2001) Unrelated parallel machines scheduling with setup consideration and a total weighted completion time objective. Int J Prod Econ 70(3):215–226Yang W-H, Liao C-J (1999) Survey of scheduling research involving setup times. Int J Syst Sci 30(2):143–155Zhang F, Tang GC, Chen ZL (2001) A 3/2-approximation algorithm for parallel machine scheduling with controllable processing times. Oper Res Lett 29(1):41–47Zhu Z, Heady R (2000) Minimizing the sum of earliness/tardiness in multi-machine scheduling: a mixed integer programming approach. Comput Ind Eng 38(2):297–30

    Static scheduling in clouds

    Get PDF
    Cloud computing aims to give users virtually unlimited pay-per-use computing resources without the burden of managing the underlying infrastructure. We present a new job execution environment Flextic that exploits scal- able static scheduling techniques to provide the user with a flexible pricing model, such as a tradeoff between dif- ferent degrees of execution speed and execution price, and at the same time, reduce scheduling overhead for the cloud provider. We have evaluated a prototype of Flextic on Amazon EC2 and compared it against Hadoop. For various data parallel jobs from machine learning, im- age processing, and gene sequencing that we considered, Flextic has low scheduling overhead and reduces job du- ration by up to 15% compared to Hadoop, a dynamic cloud scheduler

    Spatial-temporal data modelling and processing for personalised decision support

    Get PDF
    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

    Get PDF
    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

    Survey of dynamic scheduling in manufacturing systems

    Get PDF

    A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures

    Full text link
    Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, co-placement and scheduling of data with compute resources, and storing and transferring large volumes of data. We analyze the ecosystems of the two prominent paradigms for data-intensive applications, hereafter referred to as the high-performance computing and the Apache-Hadoop paradigm. We propose a basis, common terminology and functional factors upon which to analyze the two approaches of both paradigms. We discuss the concept of "Big Data Ogres" and their facets as means of understanding and characterizing the most common application workloads found across the two paradigms. We then discuss the salient features of the two paradigms, and compare and contrast the two approaches. Specifically, we examine common implementation/approaches of these paradigms, shed light upon the reasons for their current "architecture" and discuss some typical workloads that utilize them. In spite of the significant software distinctions, we believe there is architectural similarity. We discuss the potential integration of different implementations, across the different levels and components. Our comparison progresses from a fully qualitative examination of the two paradigms, to a semi-quantitative methodology. We use a simple and broadly used Ogre (K-means clustering), characterize its performance on a range of representative platforms, covering several implementations from both paradigms. Our experiments provide an insight into the relative strengths of the two paradigms. We propose that the set of Ogres will serve as a benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure

    The Integration of Process Planning and Shop Floor Scheduling in Small Batch Part Manufacturing

    Get PDF
    In this paper we explore possibilities to cut manufacturing leadtimes and to improve delivery performance in a small batch part manufacturing shop by integrating process planning and shop floor scheduling. Using a set of initial process plans (one for each order in the shop), we exploit a resource decomposition procedure to determine schedules to determine schedules which minimize the maximum lateness, given these process plans. If the resulting schedule is still unsatisfactory, a critical path analysis is performed to select jobs as candidates for alternative process plans. In this way, an excellent due date performance can be achieved, with a minimum of process planning and scheduling effort
    • …
    corecore