Springer Science and Business Media LLC

This article reports an investigation into part-input methods for an implemented flexible flow system (FFS). Two new dynamic methods—look-ahead simulation and a fuzzy heuristic rule base—are compared to three simple myopic static sequencing rules and one dynamic rule. It is shown using simulation that for the existing system, the best sequence generated from the minimum production set performs very well, but the dynamic methods outperform the sequence when the system is modified and the sequence is unaltered. It is concluded that for a stable FFS, the static determination of the best input sequence is appropriate, but that a rapidly changing FFS—due to machine breakdown, changes in production requirements, etc.—may benefit from a dynamic part-input method. The look-ahead simulation outperforms the fuzzy rule base, and appears to be a more promising dynamic method. Suggestions for future research and evaluation are given.

Loading problems in flexible manufacturing systems involve assigning operations for selected part types and their associated tools to machines or machine groups. One of the objectives might be to maximize the expected production rate (throughput) of the system. Because of the difficulty in dealing with this objective directly, a commonly used surrogate objective is the closeness of the actual workload allocation to the continuous workload allocation that maximizes throughput. We test several measures of closeness and discuss correlations between these measures and throughput. Using the best measure, we show how to modify an existing branch and bound algorithm which was developed for the case of equal target workloads for all machine groups to accommodate unequal target workloads. We also develop a new branch and bound algorithm which can be used for both types of problems. The efficiency of the algorithm in finding optimal solutions is achieved through the application of better branching rules and improved dominance results. Computational results on randomly generated test problems indicate that the new algorithm performs well.

This article provides a theoretical basis for measuring the flexibility of manufacturing systems. The concept of multiple levels of measures (necessary, capability, actual, inflexibility, and optimality) for each flexibility type is introduced. Capability and actual measures are then developed for machine, routing, process, product, and volume flexibilities. For each of these flexibility types, a state defining variable is identified. A measure of flexibility is then derived by computing either, (i) the change effort expended in moving between states, (ii) the drop in system performance in moving between states, (iii) a general or physical scale of difference between two successive states, or (iv) a measure combining all three. The use of the developed measures is illustrated via a two-facility example.

This paper considers the operation of component placement equipment for the assembly of printed circuit boards (PCBs) in a medium-volume, medium-variety manufacturing environment. It focuses on the setup management and operational planning issues associated with productive use of these expensive resources. The concept of replanning is introduced to adapt to changes in the production environment by explicitly considering the initial state of the system. The partial setup strategy is suggested as a means of efficient adaptation and as a strategy that subsumes other setup strategies encountered in practice and the literature. These concepts are applied to the optimization of a single-placement machine producing multiple products. The results of using partial setups are compared with other commonly used strategies. Experimental results suggest significant gains at the singlemachine level. Future research is being pursued to improve the solution procedures and extend these replanning concepts to the line level.

Despite their strategic potential, tool management issues in flexible manufacturing systems (FMSs) have received little attention in the literature. Nonavailability of tools in FMSs cuts at the very root of the strategic goals for which such systems are designed. Specifically, the capability of FMSs to economically produce customized products (flexibility of scope) in varying batch sizes (flexibility of volume) and delivering them on an accelerated schedule (market response time) is seriously hampered when required tools are not available at the time needed. On the other hand, excess inventory of tools in such systems represents a significant cost due to the expensive nature of FMS tool inventory. This article constructs a dynamic tool requirement planning (DTRP) model for an FMS tool planning operation that allows dynamic determination of the optimal tool replenishments at the beginning of each arbitrary, managerially convenient, discrete time period. The analysis presented in the article consists of two distinct phases: In the first phase, tool demand distributions are obtained using information from manufacturing production plans (such as master production schedule (MPS) and material requirement plans (MRP)) and general tool life distributions fitted on actual time-to-failure data. Significant computational reductions are obtained if the tool failure data follow a Weibull or Gamma distribution. In the second phase, results from classical dynamic inventory models are modified to obtain optimal tool replenishment policies that permit compliance with such FMS-specific constraints as limited tool storage capacity and part/tool service levels. An implementation plan is included.

In this paper, we deal with the problem of sequencing parts and robot moves in a robotic cell where the robot is used to feed machines in the cell. The robotic cell, which produces a set of parts of the same or different types, is a flow-line manufacturing system. Our objective is to maximize the long-run average throughput of the system subject to the constraint that the parts are to be produced in proportion of their demand. The cycle time formulas are developed and analyzed for this purpose for cells producing a single part type using two or three machines. A state space approach is used to address the problem. Both necessary and sufficient conditions are obtained for various cycles to be optimal. Finally, in the case of many part types, the problem of scheduling parts for a specific sequence of robot moves in a two machine cell is formulated as a solvable case of the traveling salesman problem.

As business imperatives change and new high-capability information technologies (IT) appear, organizations recognize the need to remain at the forefront of change by reengineering their business processes and implementing enabling responsive IT infrastructures. However, experience in this context indicates a lack of comprehension of essential elements and their mutual relationships that can contribute to the success of business-process change-implementation efforts. This article proposes a framework for managing IT for effective business-process redesign (BPR) implementation. After establishing BPR principles, components, and the relationship of BPR to some organizational and technological approaches, it presents the role and benefits of IT in BPR. The article then discusses in detail the core elements of the framework. Its theme is that an IT infrastructure that covers issues of BPR strategy development, IT strategic alignment, IT infrastructure development, IT sourcing, legacy systems reengineering, IS integration, and IS function competence is essential and critical for effective implementation.

As manufacturing systems have grown in size and complexity, material flow control has become one of the key issues for system efficiency, and determination of the number of vehicles required is an important issue in the design of the AGV (automatic guided vehicle) systems for automated material flow control. In an AGV system, a part issues a delivery request for its transportation, and then an empty vehicle is assigned based on a pre-determined vehicle selection rule and provides delivery service. This research presents a fleet sizing procedure for an AGV system with multiple pickup and delivery stations. A queueing model is used to estimate part waiting times. The fleet sizing procedure estimates the minimum number of vehicles needed to ensure a predefined part waiting time limit. While most stochastic models assume first-come-first-served or random vehicle selection rules for the selection of an empty vehicle, this model considers such additional rules as the nearest vehicle selection rule, which is the most popular among all vehicle selection rules. The performance of the proposed model is examined through computational experiments.