Abstract
This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.
Highlights
The job-shop scheduling problem (JSSP) is one of the most difficult problems in scheduling
All neurons in constraint satisfaction adaptive neural network (CSANN) are structured into two problemspecific constraint blocks: the sequence constraint block (SC-block) that deals with all sequence constraints of a given JSSP and the resource constraint block (RCblock) that deals with all resource constraints of a given JSSP
In the first set of experiments, we focus on the study of the computational complexity of CSANN-II over the original CSANN based on the six test JSSPs
Summary
The job-shop scheduling problem (JSSP) is one of the most difficult problems in scheduling. Examples include the Heuristically-guided GA (HGA) (Hart and Ross 1998), the Order Based Giffler and Thompson (OBGT) algorithm (Vazquez and Whitley 2000b), and the GA with Time Horizon Exchange (THX) operators (Lin et al 1997) Another line of research on intelligent methods for JSSPs is to investigate artificial neural network based scheduling systems for JSSPs (Luh et al 2000; Akyol and Bayhan 2007).
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