This study has presented an integrated capacity planning procedure for a series-type robotic assembly system under the consideration of line balance. The proposed approach determines both robot type and number of robots required in each workstation such that the standard deviation of workstation output rates of all workstations is minimized. This capacity planning procedure is a two-stage approach including (1) a capacity planning integer programming model, and (2) a simulation adjustment process. The mathematical model determines an initial prototype of a robotic assembly line in deterministic fashion. This is then automatically converted to a simulation model. Heuristic rules and adjustment procedure are also developed to identify potential bottleneck workstations and to propose new robot assignments (both type and number) in the simulation adjustment stage. Performance evaluation of 10 cases has shown promising results for the proposed procedure.

A line-balance-based capacity planning procedure for series-type robotic assembly line

This paper introduces a simulation generator for dual-card, kanban-controlled flow shops. Modelling kanban-controlled, pull manufacturing systems is a challenging task. The simulation generator eliminates modelling and the coding stages of simulation analysis and generates codes in SIMAN simulation language. An easy to use data-driven environment allows users to develop, edit, save and execute models without requiring any programming skills. The simulation generator is capable of simulating multi-product, multi-stage flow shops with multiple kanban cards (withdrawal and production order kanbans) and variable container sizes.

A simulation generator for dual-card kanban-controlled flow shops

The incorporation of concepts such as flexible manufacturing (FMS), computer-integrated manufacturing (CIM), just-in-time (JIT), and total quality control (TQC) in modern manufacturing environments render simulation modeling of such systems a complex and time consuming task. Continuous flow manufacturing (CFM) is a manufacturing strategy that employs the above concepts. This paper describes a generic simulator for modeling CFM systems. The simulator allows the details of the system to be represented parametrically, following a “core” modeling approach to represent the manufacturing logic. The focus in developing the simulator has been to represent the complex physical and operational characteristics of CFM systems in a realistic manner. In addition, quick and easy model development without involving repetitive programming for similar CFM systems has been an important consideration.

A generic simulator for continuous flow manufacturing

This paper presents a knowledge-based scheduling system that generates a detailed production schedule for jobs in a flexible assembly system (FAS) by exploiting routing and operation flexibilities. Past research in flexible manufacturing did not take into account the flexibilities of such environments that allow alternative sequencing and routing of operations presumably to avoid the computational overhead that would result from these added dimensions of complexity. Moreover, jobs in an FAS require both machining and assembly operations. In our system, the resulting computational burden is reduced by using (1) a two-level hierarchical scheduling approach, (2) a search strategy that uses knowledge from the knowledge base to control the amount of search required, and (3) a computationally efficient post analysis of the schedule that often results in local improvements and is based on knowledge about solution structures stored in the knowledge base. The system has been implemented and tested using Common Lisp on a Macintosh SE/30 with 4 MB main memory. Computational experience with the system is reported.

Flexible assembly scheduling using a knowledge-based approach

Using simulation to model large-scale manufacturing systems raises issues regarding simulation design, data modeling, and software integration that have been investigated during the development of a design environment for continuous flow manufacturing, called SimCFM. SimCFM combines simulation with optimization and cost analysis to design large-scale facilities for electronics manufacturing. The simulation is a modular, parametric model that shares a common data model with mathematical and cost analysis models.

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An integrated environment for modeling large scale electronics manufacturing

Parametric simulation for flexible assembly systems

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Parametric simulation for flexible assembly systems