Name
Abstract | ||
|---|---|---|
Real-time job shop scheduling is a sequential decision making problem. The main task is to decide which job in a queue should be processed next. The problem can be modeled as a Markov decision process. Jobs in the queue form an action set. Selecting one job to process is regarded as taking an action from the set. A dummy action, which means no job will be selected and the machine will keep idle, is also contained in the set. This removes the no-delay restriction from the problem. The reward function comprises the critical ratio of the selected job and the global job holding cost. Two algorithms, simulation-based value iteration and simulation-based Q-learning, are introduced to solve the scheduling problem from the perspective of a Markov decision process. The simulation explores the state space and accomplishes state transitions. The value function is parameterized and estimated by using a feedforward neural network.
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Year | DOI | Venue |
|---|---|---|
2017 | 10.5555/3242181.3242521 | WSC '17: Winter Simulation Conference
Las Vegas
Nevada
December, 2017 |
Field | DocType | ISSN |
Parameterized complexity,Feedforward neural network,Mathematical optimization,Job shop scheduling,Computer science,Simulation,Holding cost,Queue,Markov decision process,Bellman equation,State space | Conference | 0891-7736 |
ISBN | Citations | PageRank |
978-1-5386-3427-1 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Zhang | 1 | 2 | 3.12 |
| Shufang Xie | 2 | 0 | 0.68 |
| Oliver Rose | 3 | 17 | 10.43 |