FMS design model with multiple objectives using compromise programming

Abstract

There has been a great change in manufacturing systems toward computer-controlled¯exible manufacturing systems (FMS). The design and operation of the FMS involve intricate and interconnected decisions that result in the maximum performance of the system. However, the design and operational decisions have been made separately in consideration of a single-system performance measure. This paper presents a method for simultaneously determining design and control parameters of an FMS with the multiple performance objectives via full-factorial design of experiments, regression analysis and compromise programming. For a numerical example, the SIMAN simulator models a hypothetical FMS with six workstations. Eight design and control parameters are simultaneously determined by compromising four performance measures that are formulated using regression analysis.

A meta-model based simulation optimization using hybrid simulation-analytical modeling to increase the productivity in automotive industry

“…Kleijnen and Sargent [16] used a sequence of two-level fractional factorial designs to estimate a firstorder meta-model that can be used to analyze and optimize the metal tube production process of Dutch companies. Sridharan and Babu [29] and Park et al [27] presented a detailed simulation study on a typical FMS. Lin and Chiu [21] modeled a manufacturing cell with a fixed flow pattern considering dynamic effects of machine breakdowns and job changes [6].…”

The simulation design and analysis of a Flexible Manufacturing System with Automated Guided Vehicle System

“…A Flexible Manufacturing System (FMS) consists of a Numerically Controlled (NC) machine, a Material Handling System (MHS), and a computer control system for integrating the NC machine and the MHS [1]. The Integration of these machines and facilities generally involves the use of a controller, complex software and an overall computer control network that coordinates the machine tools, the material handling, and the parts [2,3].…”

Determination of efficient simulation model fidelity for flexible manufacturing systems

“…For example, decisions to be made in the design stage include part-type to be produced, the type and the size of buffers, and the number of pallets and the number and design of fixtures (Park et al 2001). Decisions to be made in the operation stage include the input sequence of parts into the system, the schedule of parts to machines based upon alternative routings, the sequence of parts on a machine, and the scheduling of material handling devices (Park et al 2001). Both design and operation-related decision parameters should be simultaneously considered to achieve global optimization in the development of FMSs.…”