Abstract
In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.
Highlights
Intelligent manufacturing shop floors usually use the cutting-edge technologies like Internet of Things (IoTs), cloud manufacturing, agent-based techniques, and big data to convert typical production resources such as workers, machines, materials and orders into smart manufacturing objects [1]
Random jobs arrival and machine breakdown may cause a huge influence on the original production plan or the whole production line. The managers with their rich production experience and management often modify the original plan, even shut down the whole production line to adapt to the uncertain shop floor environment. These production experience or management can be usually regard as the scheduling knowledge, such as shortest processing time (SPT) rules, which schedules the operation with shortest processing time first
The proposed dispatching rules mining and decision-making method is implemented in C++ language on a PC with Intel Core 2 Duo CPU 2.20 GHz processor and 2.00 GB RAM memory
Summary
Intelligent manufacturing shop floors usually use the cutting-edge technologies like IoT, cloud manufacturing, agent-based techniques, and big data to convert typical production resources such as workers, machines, materials and orders into smart manufacturing objects [1]. The production parameters like processing time, the distribution of job arrival, hint some kind of regularity in an intelligent On this account, once this regularity of the production data is discovered, the scheduling knowledge can be easy to apply into the real-time decision making, which serves as the primary motivation of this paper. In order to keep the stability of production process, according to the state of the art, there are three categories of optimization methods [2,3,4,5,6,7,8,9,10]: conventional deterministic methods, meta-heuristic algorithms and dispatching rules. IGEP is proposed to discover appropriate dispatching rules which can match the current intelligent shop floor scenarios. Real-time decision-making method calls newly appropriate dispatching rules for assigning jobs to machines timely and stores the production data into database.
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