김은갑 교수

Abstract We treat the scheduling of a single server in a finite-buffer capacity, multi-class, make-to-order production system subject to inventory holding costs, set-up times, and customer rejection costs. We employ theoretical and numerical analysis of a Markov decision process model to investigate the structure of optimal policies and the performance of heuristic policies. We establish the monotonicity of optimal performance with respect to the system parameters. Based on our insights, we prov

Abstract This paper considers a manufacturing system in which products are produced in both make‐to‐stock (MTS) and make‐to‐order (MTO) modes. Production of MTS and MTO products is done in batches, incurs a setup cost, and is non‐preemptive. The inventory of MTS products fulfills the demand of multiple classes, and each class demand can be satisfied or rejected. Customer orders for MTO production can be accepted or rejected, and their size is the same as the production batch. The primary goal of

We formulate the problem of scheduling a single server in a multi-class queueing system as a Markov decision process under the discounted cost and the average cost criteria. We develop a new implementation of the modified policy iteration (MPI) dynamic programming algorithm to efficiently solve problems with large state spaces and small action spaces. This implementation has an enhanced policy evaluation (PE) step and an adaptive termination test. To numerically evaluate various solution approac

This paper considers a two-station tandem production system consisting of make-to-stock and make-to-order facilities. The make-to-stock facility produces componentswhich are served for external demands as well as internal make-to-order operations while the make-to-order facility processes customer orders with the option toaccept or reject. We address the problem of coordinating the decision of when to accept customer order and when to satisfy component demand that maximizes the totalexpected dis