Online Optimisation Models in Short-term Production Planning

Henri Tokola

    Research output: ThesisDoctoral ThesisCollection of Articles

    Abstract

    In today's manufacturing world, real-time information is available or soon will be. Manufacturing companies can use it as a part of their information systems (including e.g. material requirements planning (MRP), enterprise resource planning (ERP), and manufacturing execution systems (MES)) for controlling the production system, i.e. for the adjustment of their inventory level, defining the capacity, and even scheduling the starting times of the jobs. A method that can be used to improve the decisions dynamically is online optimisation. Online optimisation repeatedly optimises and adjusts the decisions when there are changes or disturbances in the system. Online optimisation differs from traditional optimisation. First, the data is uncertain but, luckily, the uncertainty might decrease as time goes by. Second, quick decisions are needed but they should be made carefully as the decisions will also affect future decisions. This thesis studies online optimisation from the production planning perspective. In practice, many real factors, such as rescheduling intervals and material deliveries, affect online optimisation. The six publications of the thesis focus, first, on finding different short-term planning problems in manufacturing companies, second, on worker reactive coordination in parallel stations, third, on periodical rescheduling, fourth, on the effect of disturbances on assemble-to-order systems, fifth, on production line rescheduling, and, sixth, on the erection of the hull in shipbuilding in the event of material delays. The methods that are used in the publications of the thesis include survey, Markov models, simulation, mixed-integer-linear programming, and stochastic modelling. The modelling of online solutions to practical short-term planning problems is complex because of the large number of variables, most of which have to be considered. As the variables cannot be aggregated in the short term, a single variation in a variable, even a small one, can have significant consequences for the system. The thesis shows that online optimisation gives an advantage in certain short-term situations, such as in the cases of rush jobs or delays of components.
    Translated title of the contributionOnline-optimointimallit lyhyen aikavälin tuotannonohjauksessa
    Original languageEnglish
    QualificationDoctor's degree
    Awarding Institution
    • Aalto University
    Supervisors/Advisors
    • Niemi, Esko, Supervising Professor
    • Niemi, Esko, Thesis Advisor
    Publisher
    Print ISBNs978-952-60-6485-7
    Electronic ISBNs978-952-60-6486-4
    Publication statusPublished - 2015
    MoE publication typeG5 Doctoral dissertation (article)

    Keywords

    • optimisation
    • production planning

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