Re-entrant lines

Traditionally, manufacturing systems have mainly been treated as either job shops or flow shops. In job shops, parts may arrive with random routes, with each route having a low volume. In flow shops, the routes are fixed and acyclic, as in assembly lines. With the advent of semiconductor manufacturing plants, and more recently, thin film lines, this dichotomy needs to be expanded to consider another class of systems, which we call “re-entrant lines”. The distinguishing feature of these manufacturing systems is that parts visit some machines more than once at different stages of processing.Scheduling problems arise because several parts at different stages of processing may be in contention with each other for service at the same machine. There may be uncertainties in the form of random service or set-up times, as well as random machine failures and repairs. The goal of scheduling is to improve performance measures such as mean sojourn time in the system, which is also known as the mean “cycle-time”, or the variance of the cycle-time.In this paper we provide a tutorial account of some recent results in this field. We describe several scheduling policies of interest, and provide some results concerning their stability and performance. Several open problems are suggested.

[1]  B. A. Sevast'yanov Influence of Storage Bin Capacity on the Average Standstill Time of a Production Line , 1962 .

[2]  G. Klimov Time-Sharing Service Systems. I , 1975 .

[3]  Steven A. Lippman,et al.  Applying a New Device in the Optimization of Exponential Queuing Systems , 1975, Oper. Res..

[4]  K. Mani Chandy,et al.  Open, Closed, and Mixed Networks of Queues with Different Classes of Customers , 1975, JACM.

[5]  S. Elmaghraby The Economic Lot Scheduling Problem (ELSP): Review and Extensions , 1978 .

[6]  Frank Kelly,et al.  Reversibility and Stochastic Networks , 1979 .

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

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

[9]  Burton Simon Priority Queues with Feedback , 1984, JACM.

[10]  Kenneth R. Baker,et al.  Sequencing Rules and Due-Date Assignments in a Job Shop , 1984 .

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

[12]  Stanley B Gershwin Stochastic scheduling and set-ups in flexible manufacturing systems , 1986 .

[13]  J. Walrand,et al.  Scheduling jobs with stochastically ordered processing times on parallel machines to minimize expected flowtime , 1986, Journal of Applied Probability.

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

[15]  D. Mitra Stochastic theory of a fluid model of producers and consumers coupled by a buffer , 1988, Advances in Applied Probability.

[16]  P. R. Kumar,et al.  Stable distributed real-time scheduling of flexible manufacturing/assembly/disassembly systems , 1988, Proceedings of the 27th IEEE Conference on Decision and Control.

[17]  C. Roger Glassey,et al.  Bottleneck Starvation Indicators for Shop Floor Control , 1988 .

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

[19]  C. R. Glassey,et al.  Bottleneck starvation indicators for shop floor control (semiconductor manufacturing process) , 1988 .

[20]  T. Lai,et al.  Open bandit processes and optimal scheduling of queueing networks , 1988, Advances in Applied Probability.

[21]  P. R. Kumar,et al.  Dynamic instabilities and stabilization methods in distributed real-time scheduling of manufacturing systems , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[22]  Peter J. Ramadge,et al.  On the real-time control of flexible manufacturing systems , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[23]  Semyon M. Meerkov,et al.  Analysis and synthesis of asynchronous, asymptotically reliable, asymptotically controllable serial production lines , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

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

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

[26]  J.-T. Lim,et al.  Homogeneous, asymptotically reliable serial production lines: theory and a case study , 1990 .

[27]  Suresh P. Sethi,et al.  An Asymptotic Analysis of Hierarchical Control of Manufacturing Systems Under Uncertainty , 1991, Math. Oper. Res..

[28]  P. R. Kumar,et al.  Distributed scheduling based on due dates and buffer priorities , 1991 .

[29]  Rene L. Cruz,et al.  A calculus for network delay, Part I: Network elements in isolation , 1991, IEEE Trans. Inf. Theory.

[30]  Hong Chen,et al.  A Fluid Model for Systems with Random Disruptions , 1992, Oper. Res..

[31]  Lawrence M. Wein,et al.  Scheduling Networks of Queues: Heavy Traffic Analysis of a Multistation Network with Controllable Inputs , 2011, Oper. Res..

[32]  P. R. Kumar,et al.  Control Policies for Scheduling of Semiconductor Manufacturing Plants , 1992, 1992 American Control Conference.