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In some kinds of manufacturing, the time a part may spend in a buffer between successive operations (such as cleaning and baking in semiconductor fabrication) is limited. Parts that wait longer than this time must be reworked or discarded due to the risk of quality degradation. This constraint is important in several industries, especially the semiconductor industry. To study this issue, we present an analytical formulation for the part waiting time distribution in a Buzacott two-machine one-buffer transfer line. The numerical solution is tested with Little's law. Numerical experiments are provided to illustrate the accuracy of the solution as it is compared with simulation.

This paper deals with our previously introduced Hedging Zone Policy (HZP), which is a simple-to-implement, well-behaved heuristic for scheduling machine setup changes in real time. In the HZP, a pair of parameters for each product type defines a set in surplus space called the hedging zone, and a different changeover rule is implemented depending on the location of the production surplus state with respect to this zone. When the surplus state lies outside of the hedging zone, a fixed rank ordering or prioritization is used for deciding which item to produce next. When the surplus state is inside the hedging zone, the policy may either produce the current part type at its demand rate until more demand builds up, or change over to the item that is furthest behind (in an appropriate sense). After providing some background and overview of the HZP, we present new results concerning its stability properties. In particular, we show that if a deterministic counterpart of the stochastic system satisfies an easy-to-evaluate condition, the stochastic system with exponentially distributed failure and repair times is rate stable.

A method to study the performance of two-stage systems with unreliable parallel machines is presented. Once failed, machines require the intervention of a repair operator. In contrast to previous works which consider one repairman dedicated to each machine, a limited number of repairmen is assumed to be available for each stage. The resulting two machine system is modeled as a general multiple up and multiple down two-stage fluid system, for which an exact analytical solution is available. Results show that while considering parallel stages integrated in a system the allocation of repairmen is all but trivial and a new behaviour of the production rate as a function of the number of machines at the stage is described. Furthermore, for non-identical machines, the method makes it possible to suitably set priorities among failed machines. Finally, the results suggest the definition of new cost-oriented design problems for the optimal allocation of the operators in parallel machine lines.

A new control policy for scheduling sequence-dependent setups on a flexible machine is proposed. The policy admits a prioritization of part types and it is of the hedging type, i.e., production of each part type occurs at full capacity until its corresponding surplus reaches a predetermined target level. In order to deal with possibly lengthy and dissimilar setup times, two surplus bounds are defined for each part type: an upper bound that acts as the hedging point or production target, and a lower bound that provides hysteresis to the system by signaling when a part type should be considered for a new production run. During periods of time when all part types are within their upper and lower surplus bounds, the machine keeps producing its current setup at the demand rate. The intuitive nature of this policy and its simplicity make it amenable to implementation in manufacturing environments, while its performance and responsiveness to random disturbances may be effectively tuned by adjusting its parameters. Understanding the relationship between the surplus bounds and the dynamic behavior of the system is essential for this task, and presents interesting challenges. Some insight is provided through simulation experiments, while preliminary results concerning sufficient stability conditions are presented.

In this paper, we present an effective algorithm for maximizing profits through buffer size optimization for production lines. We consider both buffer space cost and average inventory cost with distinct cost coefficients for different buffers, and we include a nonlinear production rate constraint. To solve the problem, a corresponding unconstrained problem is introduced and a nonlinear programming approach is adopted. Numerical results are provided to show the efficiency and accuracy of our algorithm for both short and long lines.

Two main concerns of all manufacturers are quantity (How much is produced and when is it produced?) and quality (How well is it produced?). There is much existing manufacturing literature on quantity, and there are strategies that emphasize quantity. There is also a large literature on quality, and strategies have been developed to enhance quality. However, these issues should not be considered separately: quantity-oriented strategies affect quality, and quality strategies have an impact on quantity. But there is an important gap: there is little literature that deals with both together, and little that considers quality in a systems context. In this talk, we analyze how production system design, quality, and productivity are inter-related in small production systems. We show how inventory capacity can influence system yield and productivity, sometimes in counter-intuitive ways. We also discuss a variety of other problems that we have recently begun to investigate.

The production control problem in unreliable manufacturing systems has generated agreat deal of interest recently. The goal is the real-time calculation of production rates to minimize the totalexpected surplus and backlog costs of a production system. The calculation of the optimal production rates for asystem with a single unreliable machine that produces two part types requires the solution of the Bellman equation, which, in this case, is a set of hyperbolic partial differential equations. The boundary conditions depend on theoptimal policy, which makes it difficult to obtain an analytical solution. Furthermore, the space over which the solution is sought is partitioned into a set of regions and their boundaries. In this paper, we describe a method for solving the Bellman equations analytically for a particular policy when it is optimal. First, we solve the equations on one of the boundaries. Then, we propagate the solution over the surplus space, treating the partitions and their boundaries in a specific sequence

A real-time, closed-loop setup Control algorithm for a manufacturing facility with due dates and significant setup time is proposed. The problem is formulated as a dynamic program and numerically analyzed for a simple example. Extensions for prototyping are briefly discussed.