AI RESEARCH
Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals
arXiv CS.AI
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ArXi:2605.22773v1 Announce Type: new The Flexible Job Shop Scheduling Problem (FJSP) is the optimal allocation of a set of jobs to machines. Two primary challenges persist in FJSP: the unpredictable arrival of future jobs and the combinatorial complexity of the problem, rendering it intractable for conventional mixed-integer linear programming solvers. This paper proposes an event-based \gls{DRL} approach to solve FJSP with random job arrivals.