Perturbation Analysis for the Design of Flexible Manufacturing System Flow Controllers

Dynamic allocation of stochastic capacity among competing activities in a just in time manufacturing environment is addressed by optimal flow control. Optimal policies are characterized by generally intractable Bellman equations. A near-optimal controller design technique is proposed. It provides an approximate numerical solution to the Bellman equation, a tight lower bound for the optimality gap of tractable, near-optimal controller designs, and a building block for improved, near-optimal controller designs that rely on the decomposition of a multiple part-type problem to smaller (two or three part-type) problems. Computational experience is reported for two and three part-type problems.

[1]  R. Suri,et al.  Time-optimal control of parts-routing in a manufacturing system with failure-prone machines , 1980, 1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[2]  Stephen C. Graves,et al.  A Review of Production Scheduling , 1981, Oper. Res..

[3]  John N. Tsitsiklis,et al.  Convexity and characterization of optimal policies in a dynamic routing problem , 1984 .

[4]  Xi-Ren Cao,et al.  Perturbation analysis and optimization of queueing networks , 1983 .

[5]  Stanley B. Gershwin,et al.  An algorithm for the computer control of a flexible manufacturing system , 1983 .

[6]  Stanley B. Gershwin,et al.  Short term production scheduling of an automated manufacturing facility , 1984, The 23rd IEEE Conference on Decision and Control.

[7]  R. Akella,et al.  Optimal control of production rate in a failure prone manufacturing system , 1985, 1985 24th IEEE Conference on Decision and Control.

[8]  Yu-Chi Ho,et al.  Performance evaluation and perturbation analysis of discrete event dynamic systems , 1987 .

[9]  A. Sharifnia,et al.  Production control of a manufacturing system with multiple machine states , 1988 .

[10]  Panganamala Ramana Kumar,et al.  Optimality of Zero-Inventory Policies for Unreliable Manufacturing Systems , 1988, Oper. Res..

[11]  El-Kébir Boukas,et al.  Manufacturing flow control and preventing maintenance: a stochastic control approach , 1988 .

[12]  Stanley B. Gershwin,et al.  Hierarchical flow control: a framework for scheduling and planning discrete events in manufacturing systems , 1989, Proc. IEEE.

[13]  J. R. Perkins,et al.  Stable, distributed, real-time scheduling of flexible manufacturing/assembly/diassembly systems , 1989 .

[14]  P. H. Algoet,et al.  Flow balance equations for the steady-state distribution of a flexible manufacturing system , 1989 .

[15]  P. R. Kumar,et al.  Dynamic instabilities and stabilization methods in distributed real-time scheduling of manufacturing systems , 1990 .

[16]  J. Jiang,et al.  A State Aggregation Approach to Manufacturing Systems Having Machine States with Weak and Strong Interactions , 1991, Oper. Res..

[17]  Stanley B. Gershwin,et al.  Dynamic setup scheduling and flow control in manufacturing systems , 1991, Discret. Event Dyn. Syst..

[18]  M. Zazanis,et al.  Consistency of perturbation analysis for a queue with finite buffer space and loss policy , 1991 .

[19]  M. Caramanis,et al.  Flow Control of Stochastic Manufacturing Systems: A Simulation-based Design Approach using Perturbation Analysis for Second Derivative Estimation , 1991, 1991 American Control Conference.

[20]  Jianqiang Hu Convexity of sample path performance and strong consistency of infinitesimal perturbation analysis estimates , 1992 .