Mathematical models for job-shop scheduling problems with routing and process plan flexibility

By Cemal Özgüven, Lale Özbakır, Yasemin Yavuz. 2010

339 citations recorded by the source corpus.

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An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem (2016, neutral): “The mathematical model of FJSP can refer to Ozguven et al. (2010) and Roshanaei et al. (2013).”
A research survey: review of flexible job shop scheduling techniques (2016, background): “…parallel homogeneous machines, limited intermediate buffers and re-entrant machines Costs derived from failing to meet the ‘just in time’ due dates, in-process inventory costs, and costs of orders not fully completed at the end of the scheduling horizon 39 Özgüven et al. (2010) Research MILP.”
Flexible Job-Shop Scheduling via Graph Neural Network and Deep Reinforcement Learning (2023, methodology): “exact methods are mixed-integer linear programming [19] and constraint programming [20].”