Browsing by Author "Li, Jie"
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- An open-source simulation model for solving scheduling problemsPublication . Teymourifar, Aydin; Li, Jie; Li, Dan; Zheng, TaichengIn this study, an open-source simulation model is presented for solving scheduling problems. The model is capable of solving different benchmarks. The methods involved in the simulation are mainly based on generating dispatching rules or using them to solve problems, but there are other heuristics as well. Dispatching rules in an evolutionary process are generated using Gene Expression Programming. For this aim, a coding method, which has not been described in the literature before, is explained. Along with the explanation of the properties of the source code, information about deterministic, dynamic models, buffer states, machine breakdown states, and the methods used to deal with them is presented. Concepts are explained with visual examples. In addition, a subject that has not been investigated in the literature before is analyzed by using the simulation model. This topic is to examine the results of solving machine assignment and operation sequencing sub-problems in flexible job shop scheduling problems with different rules. Moreover, objective functions that the source code can handle are discussed. Unlike many studies in the literature, the codes are presented to the readers as open source. Also, it is open to development and can be easily modified by users to solve other types of problems. Finally, in the study, experimental results are presented on the basis of some benchmarks available in the literature, and the limits of the study and source code are explained.
- A hybrid framework integrating machine-learning and mathematical programming approaches for sustainable scheduling of flexible job-shop problemsPublication . Li, Dan; Zheng, Taicheng; Li, Jie; Teymourifar, AydinFlexible job shop scheduling has received considerable attention due to its extensive applications in manufacturing. High-quality scheduling solutions are desired but hard to be guaranteed due to the NP-hardness of computational complexity. In this work, a novel energy-efficient hybrid algorithm is proposed to effectively address the scheduling of flexible job shop problems within reasonable time frames. The hybrid framework hybridizes gene expression programming, variable neighborhood search, and simplified mixed integer linear programming approaches to minimize the total energy consumption. It is utilized to address 20 benchmark examples with moderate-or high-complexities. Computational results show that the hybrid algorithm can reach optimality for all considered moderate-size examples within two seconds. The proposed algorithm demonstrates significant competitive advantages relative to the existing mathematical programming approaches and a group-based decomposition method. Specifically, it shortens the computational time over one order of magnitude in some cases and leads to lower total energy consumption with a maximum decrease by 14.5%.