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Flexibility is evaluated in terms of production and operations and across hierarchical service levels. The measure developed to evaluate flexibility is characterized by an ordinal scale and general application to account for various flexibility factors and also integrates loss of goodwill. For multi-criteria situations, such a measure must consider both short- and long-term costs that depend on the expressed level of flexibility.

A stochastic, single-stage, single-product functioning under a Materials Requirements Planning (MRP) control is studied. MRP was traditionally based on a deterministic production system. In the fixed MRP system, production and inventory is done at a fixed basis so that a single-stage production occurs. Replacement of products is done only after one is finished. However, in the stochastic environment, production and inventory is done at random, replacement is done only when deemed necessary.

The characterization of production lines and transfer lines by queueing network models is considered. Specifically, the queueing network models in the literature are classified according to their assumptions into exact results, approximate, continuous part flow models. Exact result models may be of the closed-form, state or holding time type, depending on the approach. Likewise, approximate models may be obtained via the phase-type and generalized expansion method.

An approximation method is developed for computing the the average throughput of a K-station production line with exponential service times, manufacturing blocking and no intermediate buffers between adjacent stations. The holding time model method is utilized. The applicability of the method is due to its capacity to handle the case of workstations with different mean processing times. By applying the method to the balanced lines case, a simple formula can be used to provide very good results.

This article examines the use of cladistic analysis in historiography, focusing on an evolutionary model of automobile manufacturing factories. Topics include the history of automotive production strategies and how automation enabled large-scale production.

The objective of this paper is to develop a methodology for customizing pull control systems, replacing traditional control systems such as Kanban, Conwip, and Base Stock. The basic principle is to start with a pull control system that connects each stage of a given production line with each preceding stage; next, the optimization of the corresponding simulation model shows which of these potential control loops are actually implemented. The result of this methodology may be one of the traditional systems, but it may also be one of the following three new types: (1) the total line may be decomposed into several segments, each with its own classic control system, (2) the total line or its segments may combine different traditional systems, or (3) the line may be controlled through a new type of control system. Indeed, these three systems are found in a simulation experiment with a set of twelve production lines. These twelve lines are selected through a statistical design with ten factors that characterize production lines. Keywords: production control systems; heuristic optimization; simulation

The relevant issues pertaining to the system design of electronic assembly lines in printed circuit board manufacturing environments were examined using a mathematical programming model. The proposed algorithm considers the operational trade-offs associated with the principal design components of such assembly lines, namely, mini-line, flexible flow line and hybrid line designs. A set of managerial guidelines for choosing the appropriate design for electronic assembly lines is presented.

Markovian models of discrete materials flow production systems were studied, particularly the transient properties of a production line. Specifically, mean and variance of the number of parts produced in a given time period conditioned on an arbitrary initial condition were determined by applying a Markov reward model. Closed-form expression for the variance of the output from a single unreliable station was derived to demonstrate the process. Then, variance of the output from a general two-station production line with a finite buffer was determined through a set of recursive equations that take advantage of the special structure of the probability matrix.

A method for computing throughput given any work-in-process (WIP) level is proposed in a study involving the relationship between mean throughput and fixed WIP levels in Constant Work in Process (CONWIP) controlled, make-to-stock, assembly shops. The approach employs a Markov chain model and can be applied to WIP limiting controls other than CONWIP. To illustrate the results, four examples of CONWIP systems are presented.