Scaling neural network for job-shop scheduling

The authors present a novel analog computational network for solving NP-complete constraint-satisfaction problems, i.e. job-shop scheduling. In contrast to most neural approaches to combinatorial optimization based on quadratic energy cost functions, the authors propose to use linear cost functions. As a result, the network complexity (number of neurons and the number of resistive interconnections) grows only linearly with problem size, and large-scale implementations become possible. It is shown how to map a job-shop scheduling problem onto a simple neural net, where the number of neural processors equals the number of subjobs (operations) and the number of interconnections grows linearly with the total number of operations. Simulations show that the proposed approach produces better solutions than the traveling-salesman-problem-type Hopfield approach and the integer linear programming approach of Y.P. Foo and Y. Takefuji (1988) in terms of the quality of the solution and the network complexity