Optimization of kanban-based manufacturing systems

Authors:
Christos G. Panayiotou;Christos G. Cassandras
Affiliations:
Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA 01003, USA;Department of Manufacturing Engineering, Boston University, Boston, MA 02215, USA
Venue:
Automatica (Journal of IFAC)
Year:
1999

Citing 2
Cited 4

Resource allocation problems: algorithmic approaches

Resource allocation problems: algorithmic approaches
Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems

Journal of Optimization Theory and Applications

Generalized surrogate problem methodology for online stochastic discrete optimization

Journal of Optimization Theory and Applications
Application of analytic hierarchy process in just-in-time manufacturing systems: a review

International Journal of Data Analysis Techniques and Strategies
Managing dynamic flows in production chains through self-organization

Engineering Self-Organising Systems
Firing rate optimization of cyclic timed event graphs by token allocations

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.15

Visualization

Abstract

We develop and analyze an algorithm to maximize the throughput of a serial kanban-based manufacturing system with arbitrary arrival and service process distributions by adjusting the number of kanban allocated to each production stage while maintaining the total work-in-process inventory at any desired level. The optimality properties of the algorithm are proved under a necessary and sufficient ''smoothness condition''. The algorithm is driven by throughput sensitivities which, in general, can only be estimated along an observed sample path of the system. It is shown that the algorithm converges to the optimal allocation in probability and, under additional mild conditions, almost surely as well. Finally, it is shown that Finite Perturbation Analysis (FPA) techniques can be used to obtain the sensitivity estimates in order to reduce the amount of simulation required in either on-line or off-line simulation-based optimization.