Case-Based Reasoning system for mathematical modelling options and resolution methods for production scheduling problems: Case representation, acquisition and retrieval

•CBR case base filling and the retrieval of relevant past cases to solve new problem.•Methods of bibliographic analysis to dynamically examine the scheduling topics.•CBR similarity issues.•Adaptability criteria to extract relevant past cases during the retrieval step.•Industrial case study on beer production.

Thanks to a wide and dynamic research community on short term production scheduling, a large number of modelling options and solving methods have been developed in the recent years both in chemical production and manufacturing domains. This trend is expected to grow in the future as the number of publications is constantly increasing because of industrial interest in the current economic context. The frame of this work is the development of a decision-support system to work out an assignment strategy between scheduling problems, mathematical modelling options and appropriate solving methods. The system must answer the question about which model and which solution method should be applied to solve a new scheduling problem in the most convenient way. The decision-support system is to be built on the foundations of Case Based Reasoning (CBR). CBR is based on a data base which encompasses previously successful experiences. The three major contributions of this paper are: (i) the proposition of an extended and a more exhaustive classification and notation scheme in order to obtain an efficient scheduling case representation (based on previous ones), (ii) a method for bibliographic analysis used to perform a deep study to fill the case base on the one hand, and to examine the topics the more or the less examined in the scheduling domain and their evolution over time on the other hand, and (iii) the proposition of criteria to extract relevant past experiences during the retrieval step of the CBR. The capabilities of our decision support system are illustrated through a case study with typical constraints related to process engineering production in beer industry.