Adaptive control of stochastic manufacturing systems with hidden Markovian demands and small noise

©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.The adaptive production planning of failure-prone manufacturing systems is considered in this paper, In real manufacturing systems, the product demand is usually not known a priori. One of the major tasks in production scheduling is to estimate and predict the demand. In this paper, the authors consider the demand to be either the sum of an unknown rate and a small white noise or the sum of a hidden Markov chain and a small white noise. An algorithm is given to define a family of estimates for the unknown demand processes. Based on this family of estimates, adaptive controls are constructed, which are shown to be nearly optimal

Production control and supplier selection under demand disruptions

This paper investigates the effects of demand disruptions on production control and supplier selection in a three-echelon supply chain system. The customer demand is modeled as a jump-diffusion process in a continuous-time setting. A two-number production-inventory policy is implemented in the production control model for the manufacturer. The objective is to minimize the long-term average total cost consisting of backlog cost, holding cost, switching cost, and ordering cost. The simulated annealing method is applied to search the optimal critical switching values. Furthermore, an improved analytical hierarchy process (AHP) is proposed to select the best supplier, based on quantitative factors such as the optimal long-term total cost obtained through the simulated annealing method under demand disruptions and qualitative factors such as quality and service. Numerical studies are conducted to demonstrate the effects of demand disruptions in the face of various risk scenarios. Managerial insights from simulation results are provided as well. Our approaches can be implemented as the “stress test” for companies in front of various supply chain disruption scenarios.Peer Reviewe