Taner, M. R.Hodgson, T. J.King, R. E.Thoney, K. A.2016-02-082016-02-082003207543http://hdl.handle.net/11693/24400This paper addresses job shop scheduling with sequence dependent family set-ups. Based on a simple, single-machine dynamic scheduling problem, state dependent scheduling rules for the single machine problem are developed and tested using Markov Decision Processes. Then, a generalized scheduling policy for the job shop problem is established based on a characterization of the optimal policy. The policy is combined with a ‘forecasting’ mechanism to utilize global shop floor information for local dispatching decisions. Computational results show that performance is significantly better than that of existing alternative policies.EnglishComputational methodsDecision theoryForecastingJob analysisMarkov processesMathematical modelsProblem solvingDynamic scheduling problemJob shop schedulingMarkov decision processesSchedulingArticle10.1080/00207540310001491671366-588X