A Game Theoretic Approach to Job Shop Scheduling

This paper proposes a non-cooperative game approach based on neural network (GMBNN) to solve the job shop scheduling problem. Machines in manufacturing task are defined as players and strategies consist of all the feasible programs which are selected by dispatching rules for minimizing the mean flowtime. Strategies for the game model are generated from a backpropagation neural network, which selects combination of the rules for the machines. Case study shows that the GMBNN can be an effective approach to solve the job shop scheduling problem.