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Based upon basic drivers for the flow time, a lumped parameter model for a manufacturing line has been derived by using Kingman's approximation. The lumped parameter model for the flow time consists of a workstation and delay time. The workstation is characterized by the effective process time of the bottleneck workstation and the weighted sum of the coefficients of variation of all workstations. The delay time is determined by the sum of all effective process times of the nonbottleneck workstations. The advantage of the application of the lumped parameter model is that the variability of the total line is determined by a measurement at the operating point of the actual manufacturing line. This variability depends upon the operating point. Examples show that this dependency is not too strong for lines with a distinct bottleneck workstation in the actual line. From the derivation of the lumped parameter model it can be concluded that an improvement of the effective process time of the bottleneck workstation of the line results in an increase of the total variability of the line. This variability can be reduced by either reducing the variability of all workstations or the effective process times of the nonbottleneck workstations. Index Terms--Lumped parameter model, manufacturing systems, performance, variability.

In this paper, a system-theoretic approach to bottlenecks in Markovian production lines is introduced and analyzed. The approach is based on the sensitivity of the system production rate to machines' reliability parameters. Using this approach, definitions of bottlenecks are introduced, methods for their identification are developed, and their implications for production automation and preventative maintenance are discussed. Index Terms - Markovian statistics of breakdowns, production bottlenecks, production systems, unreliable machines.

An assembly sequence planner is a system which, given a description of an assembly to be manufactured, identifies subassemblies and generates high-level plans for the construction of the each subassembly, including the final assembly. Previous research has shown that feasible plans can be efficiently produced for large assemblies, but the generation of good plans has met with less success. Not only are there difficulties in defining "good plans," but all published methods for finding good plans have computational complexities that make them unlikely to be practical for large assemblies. This paper shows that representing an assembly as a hierarchy of assembly structures can overcome many of these difficulties. A planner is described which uses the structure hierarchy both as a framework for structure-dependent definitions of a good plan, and as a tool for finding good plans more rapidly by using high-level expert advice, by reusing subplans for repeated substructures, and by not fully optimizing the plan. Analytical and experimental results are presented to demonstrate the effectiveness of the approach.

This paper deals with models for flow management problems in flexible assembly systems (FAS's). The system consists of a set of machines that must perform the assembly of a number of parts, possibly of different types. Each part type requires a set of operations; the precedence relations among the operations are specified by an assembly tree. Machines are provided with limited-capacity tool magazines and a finite buffer for holding parts. Each machine can be tooled to perform only a particular subset of the operations required by the whole process. One problem is that of finding a feasible assignment of operations to machines and a feasible schedule of the subassemblies in order to minimize the completion time of all of the parts. In this paper, the problem is analysed as a case of pipelined assembly, i.e., when the FAS is characterized by a serial transportation system (flow line) and there exists a dominating path in the assembly tree. Typically, this happens when there is a main pallet and all of the other components are assembled directly on it in a given sequence. We present polynomial-time dynamic programming algorithms for solving the problem for both single-type and multi-type production. The approach is more general than typical ALB algorithms.

From the concept of designing for automation, a system modeled from simple part-mating is proposed for helping automating the fine motion control phase of assembly operations. The proposed system consists of three subsystems. The first subsystem is a part-mating model categorized from different mating geometric constraints of simple parts. The second subsystem is a coding system, named Structured Assembly Coding System (SACS), that codes different part-mating tasks with different levels of geometrical constraints. From this coding system, specific part-mating types can be identified for different tasks of assembly operations. Conversely, constraint configurations for each part-mating task can also be generated from the proposed part-mating model. After constraint configurations are identified for an assembly operation, the third subsystem is to automate an assembly operation by deducing the compliance control strategies. The proposed system is demonstrated through a case study of pump assembly. The actual implementation of such an automated assembly will be our future work.

We present a novel method for "what's next" scheduling of semiconductor manufacturing lines based on the deterministic fluid network model of Chen and Yao [2]. By "what's next" scheduling we mean specifying the order in which jobs are processed at each tool group. The approach we take here is first to determine how to allocate tool capacity among competing job types by solving a series of linear and quadratic programming problems related to the fluid model and then to specify a "What's next" scheduling algorithm designed to track these capacity allocations. The primary advantage of our approach is that it gives rise to a dynamic schedule which is based on global rather than local state information and which is responsive to stochastic changes in the line including tool incapacitation events and operator unavailability. This is in contrast to many other scheduling rules, such as first come first served and closest to completion which are essentially static rules based on local information. In addition to describing the scheduling algorithm, we present some theoretical results about the fluid model that have important implications in the context of semiconductor manufacturing as well as some preliminary simulation results comparing the fluid model to other scheduling policies.

An important aspect in recycling and reuse for environmentally conscious manufacturing is disassembly. Although disassembly systems contain the same basic elements as assembly systems, the disassembly problem differs significantly from the assembly problem due to the fact that the incoming disassembly product is not controlled. In addition, complete dismantling of products is not necessarily required for disassembly, whereas assembly builds a product completely. This paper describes a model for automated disassembly that accounts for workcell interaction and used product constraints. The model provides an essential means to determine, in real-time, the next component for disassembly using the knowledge of the product design and sensor feedback minimizing the steps to remove goal components. Sets of components for removal were resolved by minimizing setup time for disassembling the component. Given the model, a controller for product disassembly is defined that can account for missing and known replacement components. The controller can recover from unknown replacement components and jammed components when alternate removal sequences exist to meet the cell goal. Two case study examples are presented and experimentally simulated. Simulation results based on real product, vision sensor measure, and process input are presented and discussed. It is expected that the concepts demonstrated through these case studies can provide useful insights into other mechanical assemblies. Index Terms--Assembly system, disassembly, product modeling, supervisory control, workcell programming.

The variability factor, (i.e., the variance) of the cumulative production, [Sigma](t), delivered by a line composed of failure prone machines is studied in the fluid modeling approach. In this context, the evolution of [Sigma](t) is described by a stochastic differential equation in which the noise source describes the random failures of the machines. We calculate the fluctuations of the production for three different situations, namely for a single non-Markovian machine, for unbuffered networks of Markovian machines and for production dipoles composed of two machines separated by one buffer. The dynamics of the production dipole is approached via the introduction of a random environment model. Thanks to this new model, we can explicitly take into account the buffer induced correlations phenomena which directly influence the variability of [Sigma](t). The probabilistic properties of the random time needed to complete a batch of fixed size are also explicitly derived. Index Terms - Alternating renewal processes, batch completion time, fluid modeling, production dipole, production flows.