Mehmet Savsar

Reliability analysis of a flexible manufacturing cell

Abstract In this study, mathematical models are developed to study and compare the operations of a fully reliable and an unreliable flexible manufacturing cell (FMC), each with a flexible machine, a loading/unloading robot, and a pallet handling system. The operation times, loading/unloading times, and material handling times by the pallet are assumed to be random. The operation of the reliable cell is compared to that of an unreliable cell with respect to utilization of the cell components, including the machine, robot, and pallet handling system. The unreliable cell is assumed to operate under random (machine and robot) failures with constant failure rates for the machine and the robot. The pallet handling system is assumed to be completely reliable.

Simulation analysis of a pull-push system for an electronic assembly line

Abstract This study deals with simulation modeling and analysis of an electronic assembly line. Weekly scheduled demands for printed circuit boards (PCBs) are met from its Final Board Inventory (FBI). The demand triggers production at the first station by signaling for replenishment of a quantity equal to the demand. The assembly operations themselves are performed using a push system of production control. The objective of the simulation analysis is to determine the minimum number of batches in the system, including the Work-In-Process (WIP) and Finished Board Inventory, to meet a certain percentage of demand on time. The assembly line has enough capacity to meet the annual demand, but random fluctuations in assembly operations disturb the weekly production schedule. Therefore, the aim is to meet weekly scheduled demand on time to avoid additional cost of reshipments.

Simulation analysis of maintenance policies in just‐in‐time production systems

Presents a simulation model and the results on the effects of different maintenance policies on performance measures of just‐in‐time production systems under different operating conditions. Develops a simulation model for a line, which consists of five stations in series and is operated according to demand from the last station. Shows that the introduction of preventive maintenance to the production and assembly machines on the line increases line performance under all conditions studied. The effects of maintenance are more pronounced in a longer line.

Effects of kanban withdrawal policies and other factors on the performance of JIT systems—a simulation study

This paper presents the results of a simulation study of a just–in–time (JIT) production control system and its performance under different operational conditions. In particular, the effects of two different kanban withdrawal policies on such performance measures as throughput rate, station utilizations and total work in process levels are investigated. Also, simulation experiments are carried out to determine the effects of processing time variability, number of different types of kanbans allowed at each station and production line length on the mentioned performance measures of JIT production control method. The simulation experiments were carried out for production lines in which processing times of stations were assumed to follow gamma and Erlang distributions.

Modeling of machine failures in a flexible manufacturing cell with two machines served by a robot

In this study, a stochastic model is developed to analyze performance measures of a flexible manufacturing cell (FMC) under different operational conditions, including machine failures and repairs. The FMC consists of two machines served by a robot for loading and loading purposes, and a pallet handling system. The model is based on Markov processes and determines closed-form solutions for the probabilities of system states that are used to calculate system performance measures, such as production output rate and utilizations of system components under different parametric conditions and equipment failures and repairs.

A neural network procedure for kanban allocation in JIT production control systems

In this paper, we develop a Generalized Systematic Procedure (GSP) for determining the optimum kanban allocation in just-in-time (JIT) controlled production lines. This procedure is based on a meta-model that incorporates (1) a factorial design approach to select the appropriate kanban combinations, (2) a simulation model to simulate the JIT production line, and (3) a trained neural network model to evaluate the line performance over the entire domain of possible kanban combinations. The GSP is then applied to a case problem and the results are presented.

Flexible facility layout by simulation

Abstract Facility layout problems are rather difficult in terms of analytical modeling. When the number of departments is large, the material flow volumes between departments are random, and a flexible layout is desired, the problem becomes much more difficult and usually intractable analytically. Therefore, simulation has been used as a modeling alternative to determine a good layout among a very large number of alternatives. This paper presents a systematic method and a computer simulation algorithm to solve flexible facility layout problems. Such objectives as the minimization of total material handling costs, maximization of total closeness ratings between departments, minimization of expected future relayout costs, and minimization of expected total material handling costs in possible future relayouts are incorporated into the model.

Stochastic models for reliable and unreliable flexible manufacturing cells with two machines and two robots

Manufacturing systems are usually designed in automated units, called cells, and flexibility is introduced so that a variety of parts can be produced on the same set of equipment. In this paper, we consider a Flexible Manufacturing Cell (FMC) consisting of two machines, each served by a robot arm for loading and unloading purposes, and a common pallet handling system, which moves a batch of mixed parts into and out of the system. Two mathematical models are developed to study and compare operations of two cases of the described FMC. In the first case, both machines are assumed to be reliable all the time while in the second case both machines are assumed to be unreliable and subject to unexpected failures. Machining times, robot loading and unloading times, machine failure and repair times and pallet handling times are assumed to be random variables. Stochastic model of each system turns out to be a Markov chain with a transition matrix, whose size depends on the pallet capacity. Case examples are considered for each FMC, exact numerical solutions are obtained, and the production performance results, such as the production rate and equipment utilisations, are compared.

Two-stage production lines with a single repair crew

SUMMARY This paper describes the analytic modelling of two-stage production lines with storage of workpieces between stages. The effect of stage failure, and repair by a single repair crew, on production rate and intermediate storage level are shown.

A stochastic model for the analysis of a two-machine flexible manufacturing cell

This paper presents a stochastic model to determine the performance of a flexible manufacturing cell (FMC) under variable operational conditions, including random machining times, random loading and unloading times, and random pallet transfer times. The FMC under study consists of two machines, pallet handling system, and a loading/unloading robot. After delivering the blanks by the pallet to the cell, the robot loads the first machine followed by the second. Unloading of a part starts with the machine that finishes its part first, followed by the next machine. When the machining of all parts on the pallet is completed, the handling system moves the pallet with finished parts out and brings in a new pallet with blanks. A model with these characteristics turns out to be a Markov chain with a transition matrix of size 5n+3, where n is the number of parts on the pallet. In this paper, we present exact numerical solutions and economic analysis to evaluate FMC systems, to determine optimal pallet capacity and robot speed that minimize total FMC cost per unit of production.