Flexible Manufacturing System Environment Research Articles

PRODUCTION SCHEDULING IN A FLEXIBLE MANUFACTURING SYSTEM WITH SETUPS

A model is presented for the planning and scheduling of production batches in a flexible manufacturing system environment in which setup costs and times are nonnegligible and alternate routings are possible. The formulation is an integer program with a multicommodity flow network structure. A heuristic procedure based on price-directive decompostion using column generation is used to obtain solutions. Numerical experimentation is performed to assess die quality of the heuristic versus optimal solutions, and to determine the impact of routing flexibility on total cost, inventory levels, bottlenecks, capacity utilization, throughput time, number of setups and split lots. Important cost-benefit trade-off implications are shown for the design of flexible manufacturing systems.

A constructive heuristic algorithm for concurrently selecting and sequencing jobs in an FMS environment

This paper deals with the concurrent solution of the loading and scheduling problems in a flexible manufacturing system ( FMS) environment. It is assumed that the FMS environment has production planned periodically and each job in the system has a number of operations to be processed on flexible machines. A heuristic approach using a constructive scheduling method is developed to solve the FMS loading and scheduling problems concurrently. The computational results are compared to an existing procedure that considers a hierarchical approach with a similar problem environment. The comparison study shows a significant improvement over the existing hierarchical procedure. This experiment indicates that a concurrent solution approach can solve the FMS loading and scheduling problems very effectively.

Routing flexibility and production scheduling in a flexible manufacturing system

A multiproduct, multiperiod, multistage network model is presented for the planning of order release and production scheduling in a flexible manufacturing system environment under the existence of alternate routings. It is assumed that premanufacturing decisions such as machine grouping and tool loading have been made, so that setup costs and setup times are neglible and can be included in the processing times. The decision process addressed by the model is the disaggregation of weekly production requirments to daily production requirments, the determination of production batch sizes for each operation of each part type, and the daily assignment of each batch to machine groups given the flexibility of alternate routings. The model also provides the interface and linkage between an MRP component planning system and the shop scheduling system. The model is solved using a price-directive decomposition technique with column generation. Experimentation is performed with the model for varying problem sizes to determine the impact of shop flexibility on total cost, inventory levels, existence of bottlenecks, shop utilization, and the number of setups and split lot production. The results indicate important cost-benefit trade-off implications for system design and acquisition. For example, if in fact setup costs and times are nonnegligible, then it is shown that increasing the routing flexibility of a system without a parallel decrease in setup costs and times is unlikely to reap significant benefits.

Experimental investigation of FMS machine and AGV scheduling rules against the mean flow-time criterion

Although a significant amount of research has been carried out in the scheduling of flexible manufacturing systems (FMSs), it has generally been focused on developing intelligent scheduling systems. Most of these systems use simple scheduling rules as a part of their decision process. While these scheduling rules have been investigated extensively for a job shop environment, there is little guidance in the literature as to their performance in an FMS environment. This paper attempts to investigate the performances of machine and AGV scheduling rules against the mean flow-time criterion. The scheduling rules are tested under a variety of experimental conditions by using an FMS simulation model.

Flexibility trade-offs in a random flexible manufacturing system: A simulation study

SUMMARY Flexible manufacturing systems (FMSs) are allowing firms to take advantage of diversified, low volume production of products with short life-cycles. The flexibility provided by an FMS improves the ability of a system to respond to change. Several types of manufacturing flexibility (M F) have been identified in the literature. Ideally, it is desirable to provide an FMS with all types of flexibility. However, there is a general consensus on the discordant nature of some of the flexibilities. This makes it imperative for management to recognize which flexibilities it deems critical to the manufacturing strategy of the firm. This allows the management to trade-off some of the less important flexibilities for the more important ones, as the need arises. This study is an attempt toward an empirical examination of the flexibility trade-offs in an FMS. By using computer simulation, we study the flexibility tradeoffs in the stochastic environment of a random FMS. Various configurations of the FMS are simulated under the influence of a variety of loading and dispatching strategies. The results show that there is a trade-off among the various flexibility types under most circumstances. However, some scenarios illustrate that an appropriate combination of the variables like scheduling environment, product variety, and system configuration, can be effective in containing these trade-offs. In other cases, it is seen that the FMS can accommodate more than one flexibility type simultaneously.

A goal programming approach to production planning for flexible manufacturing systems

The increased use of flexible manufacturing systems (FMS) reduces production time variability as the fixed processing time of automated systems replaces the variable processing time of human resources in production systems. This reduction in the variability permits the application of deterministic, constrained optimization models that recognize the conflicting multi-objective nature of resource utilization in an FMS environment. One such multi-objective optimization model is linear goal programming. Unlike most linear programming models, linear goal programming is designed to handle conflicting multi-objective problems. A goal programming model is developed a and presented in this paper as an aid in FMS planning. An application is also presented to illustrate the informational benefits of using the proposed model.

Automated guided vehicle system design for FMS applications

The issues of designing and installing a system of Automated Guided Vehicles (AGVs) in a Flexible Manufacturing System (FMS) are examined in this work. The development, advantages and future trends of AGVs are briefly reviewed. Then the basic features of an AGV in an FMS environment, as well as a computerized procedure for the optimum vehicle selection, are discussed. Guide path layout considerations are made and docking/pallet transfer methods are suggested. A routing algorithm is presented to facilitate both routing and control of the vehicles, and the most commonly used methods for zone blocking, loading/unloading and traffic control and communications are outlined. Operation rules are reviewed and an example of an AGVS in an FMS is presented to demonstrate the potential of simulation in both design and evaluation of such systems.

Autonomous navigation in a manufacturing environment

Current approaches towards achieving mobility in the workplace are reviewed. The role of automatic guided vehicles (AGVs) and some of the preliminary work of other groups in autonomous vehicles are described. An overview is presented of the autonomous robot architecture (AuRA), a general-purpose system designed for experimentation in the domain of intelligent mobility. The means by which navigation is accomplished within this framework is specifically addressed. A description is given of the changes made to AuRA to adapt it to a flexible manufacturing environment, the types of knowledge that need to be incorporated, and the new motor behaviors required for this domain. Simulations of both navigational planning and reactive/reflexive motor schema-based navigation in a flexible manufacturing systems environment, followed by actual navigational experiments using the mobile vehicle, are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A production planning problem in FMS

We present in this work a hierarchical approach for generating alternatives for production planning in a generic floor shop problem within the environment of Flexible Manufacturing Systems (hereafter, FMS). Briefly, the problem can be stated as follows: Given the resources of a FMS and the characteristics of the parts to be produced along a planning horizon, obtain the loading ordering of the parts in the FMS, the execution ordering of the operations and the processing route of each part (i.e., the working stations where each operation is to be executed), such that the production and transport costs are minimized and the modules workload is levelized. The problem is decomposed into three subproblems which are arranged in a hierarchy; a variety of models is presented, as well as the input/output relations that allow to integrate them; we also propose some algorithmic ideas to exploit the special structure of the problem. Computational experience is reported.

An Artificial Intelligence Approach to the Scheduling of Flexible Manufacturing Systems

Abstract Scheduling in a flexible manufacturing system (FMS)must take into account the shorter lead-time, the multiprocessing environment, the flexibility of machine tools, and the dynamically changing states. The scheduling approach described in this paper employs a knowledge-based system to carry out the nonlinear planning method developed in artificial intelligence. The state-space process for plan-generation, by either forward- or backward-chaining, can handle scheduling requirements unique to the FMS environment. A prototype of this scheduling system has been implemented on a LISP machine and is applied to solve the scheduling problem in flexible manufacturing cells. This scheduling method is characterized by its knowledge-based organization, symbolic representation, state-space inferencing, and its ability for dynamic scheduling and plan revision. It provides a foundation for integrating intelligent planning, scheduling, and machine learning in FMSs.

A pattern-directed approach to FMS scheduling

Scheduling in flexible manufacturing systems (FMS) must take into account the shorter lead time, the multiprocessing environment, and the dynamically changing states. In this paper, a pattern-directed approach is presented which incorporates a nonlinear planning method developed in the artificial intelligence field. The scheduling system described here is knowledge-based and utilizes both forward-and backward-chaining for generating schedules (treated as state-space plans). The pattern-directed approach is dynamically adjustable and thus can handle scheduling requirements unique to the FMS environment, such as dynamic scheduling, failure-recovery scheduling, or prioritized scheduling for meeting deadlines.

Vision system based on a single-chip microcomputer

The paper describes a compact and inexpensive binary vision system in which all interfacing and data processing functions are performed by an Intel 8751 single-chip microcomputer. The vision sensor is a 64 kbit dynamic RAM chip which is capable of providing a picture resolution of up to 256 × 128 pixels. Picture processing algorithms are implemented within the vision system and object statistics are transferred to the host computer over a serial interface. The resulting system is implemented using only four integrated circuits and is eminently suitable for component identification and inspection in a flexible manufacturing system environment.