Akif Asil Bulgak

Cellular manufacturing systems design with routing flexibility, machine procurement, production planning and dynamic system reconfiguration

This paper investigates the problem of designing cellular manufacturing systems with multi-period production planning, dynamic system reconfiguration, operation sequence, duplicate machines, machine capacity and machine procurement. An important aspect of this problem is the introduction of routing flexibility in the system by the formation of alternate contingency process routings in addition to alternate main process routings for all part types. Contingency routings serve as backups so as to effectively address the reality of part process routing disruptions (in the main routings) owing to machine breakdowns and allow the cellular manufacturing system to operate in a continuous manner even in the event of such breakdowns. The paper also provides in-depth discussions on the trade-off between the increased flexibility obtained versus the additional cost to be incurred through the formation of contingency routings for all parts. Some sensitivity analysis is also performed on some of the model parameters. The problem is modelled and solved through a comprehensive mixed integer programming formulation. Computational results presented by solving some numerical examples show that the routing and process flexibilities can be incorporated within the cellular manufacturing system design without significant increase in the system cost.

Optimization of buffer sizes in assembly systems using intelligent techniques

When the systems under investigation are complex, the analytical solutions to these systems become impossible. Because of the complex stochastic characteristics of the systems, simulation can be used as an analysis tool to predict the performance of an existing system or a design tool to test new systems under varying circumstances. However, simulation is extremely time consuming for most problems of practical interest. As a result, it is impractical to perform any parametric study of system performance, especially for systems with a large parameter space. One approach to overcome this limitation is to develop a simpler model to explain the relationship between the inputs and outputs of the system. Simulation metamodels are increasingly being used in conjunction with the original simulation, to improve the analysis and understanding of decision-making processes. In this study, an artificial neural network (ANN) metamodel is developed for the simulation model of an asynchronous assembly system and an ANN metamodel together with simulated annealing (SA) is used to optimize the buffer sizes in the system.

Buffer size optimization in asynchronous assembly systems using genetic algorithms

Abstract Selecting appropriate buffer sizes for the transport systems of automated manufacturing systems is a complex task that must account for random fluctuations in production rates by the individual stations as well as for transport delays that are a part of material handling system. If buffer sizes are too large, then transport delays are excessive and more in-process inventories must be input into the system to accommodate the large buffer sizes. If the buffer sizes are too small, then small processing delays will cause the buffer to fill, and upstream workstations will be blocked from releasing complete workpiece. This article presents the buffer size optimization problems of Asynchronous Assembly Systems. Genetic Algorithms (GAs) of Holland are applied to the problem in an attempt to extend their domain of application the complex design optimization problems of a certain class of manufacturing systems. In addition to a detailed description of the buffer size allocation problems, a brief introduction to GAs is given. A number of examples are presented. Also, future research directions are indicated.

Buffer allocation and performance modeling in asynchronous assembly system operations: An artificial neural network metamodeling approach

This article investigates metamodeling opportunities in buffer allocation and performance modeling in asynchronous assembly systems (AAS). Practical challenges to properly design these complex systems are emphasized. A critical review of various approaches in modeling and evaluation of assembly systems reported in the recently published literature, with a special emphasis on the buffer allocation problems, is given. Various applications of artificial intelligence techniques on manufacturing systems problems, particularly those related to artificial neural networks, are also reviewed. Advantages and the drawbacks of the metamodeling approach are discussed. In this context, a metamodeling application on AAS buffer design/performance modeling problems in an attempt to extend the application domain of metamodeling approach to manufacturing/assembly systems is presented. An artificial neural network (ANN) metamodel is developed for a simulation model of an AAS. The ANN and regression metamodels for each AAS are compared with respect to their deviations from the simulation results. The analysis shows that the ANN metamodels can successfully be used to model of AASs. Consequently, one concludes that practising engineers involved in assembly system design can potentially benefit from the advantages of the metamodeling approach.

Activity-Based Costing and Management applied in a hybrid Decision Support System for order management

This article introduces a new Cost Management and Decision Support System (DSS) applicable to Order Management. This model is better fit and compatible with todays competitive, and constantly changing, business environment. The presented Profitable-To-Promise (PTP) approach is a novel modeling approach which integrates System Dynamics (SD) simulation with Mixed-Integer Programming (MIP). This Order Management model incorporates Activity-Based Costing and Management (ABC/M) as a link to merge the two models, MIP and SD. This combination is introduced as a hybrid Decision Support System. Such a system can evaluate the profitability of each Order Fulfillment policy and generate valuable cost information. Unlike existing optimization-based DSS models, the presented hybrid modeling approach can perform on-time cost analysis. This will lead to better business decisions based on the updated information.

Analysis and design of split and merge unpaced assembly systems by metamodelling and stochastic search

The study presents a new approach in optimal interstage buffer allocation problem of split-and-merge unpaced open assembly systems, which are increasingly being used in modern manufacturing systems, particularly in automotive industries. Allocations of interstage buffers to accommodate the work-in-process inventories are optimized in an attempt to maximize the overall system production rate. A simulation model developed is used in conjunction with genetic algorithms (GA) to find optimal interstage buffer configurations yielding a maximum production rate. However, simulation is extremely time-consuming due to lengthy computational requirements, especially when used in a stochastic search algorithm. In an attempt to overcome this problem, an alternative approach in simulation metamodelling based on artificial neural networks (ANN) is developed. The optimization problem previously conducted through simulation and GA is reconsidered by integrating the metamodelling approach into the GA, replacing the simulation model. The new ANN-GA approach not only gives solutions with no statistically significant difference in comparison with the original simulation GA approach, but also demands significantly less computational time. The proposed methodology intends to help practising system design engineers to take quicker decisions regarding the assembly system design parameters. The potential of metamodelling to solve manufacturing systems problems are also discussed.

Modeling and design optimization of asynchronous flexible assembly systems with statistical process control and repair

This article presents the implementation of hybrid procedures involving the use of analytical performance evaluation techniques, discrete event simulation, and Monte Carlo optimization methods for the stochastic design optimization of asynchronous flexible assembly systems (AFASs) with statistical process control (SPC) and repair loops. AFASs are extremely complex and difficult to analyze in that such systems are subject to starvation and blocking effects, random jam occurrences at workstations, and splitting and merging of the assembly flow due to repair loops. Hence, an integrated approach simultaneously analyzing the interactions between product quality and optimal/near optimal system design is pursued. In the analytical analysis stage, a model based on GI/G/1 queueing network theory is used. In the Monte Carlo optimization stage, two alternative stochastic optimization approaches, namely, heuristic versions of stochastic quasigradient and simulated annealing algorithms, are implemented and compared in terms of their capabilities of solving complex AFAS design problems. The hybrid procedures presented appear to perform reasonably well in designing AFASs to reach a target production rate.

Impact of quality improvement on optimal buffer designs and productivity in automatic assembly systems

Abstract Mismatches between sampling time requirements and production cycle times have automated inspection processes and represent a substantial bottleneck in automatic assembly systems (AASs). Consequently, indirect ways must be developed to assess average outgoing quality in order not to slow the entire line down to the speed of the test process. A clear tradeoff exists between the average outgoing quality limit (AOQL) and the production rate in AASs. In this article, these tradeoffs are analyzed with particular emphasis on the new trends in moving the quality upstream into the products and processes. A simultaneous investigation of product quality and optimal buffer designs is made. Modeling and Monte Carlo optimization approaches involving discrete event simulation and stochastic quasigradient methods are used to carry out the analyses.

An analytical performance model for assembly systems with automatic inspection stations and repair loops

Abstract Automatic Assembly Systems (AASs) form an important class of manufacturing systems. Due to the difficulty of modeling them via simulation, some analytical tools have been developed for the performance evaluation of such systems. Modeling of a certain class of AASs that have test stations and counter-flow repair loops deserves special attention. Incorporating a counter-flow secondary loop in the material handling system in addition to the main loop brings the phenomena of splitting and merging of the flow of assemblies which make the analytical model more intricate. In this paper, a closed system version of Whitts second order approximations for GI/G/1 queues is applied to AASs with repair loops as an analytical tool for performance evaluation. The results of the analytical model are compared with those of simulation. The analytical method yields approximate but fast results and appears to be a promising technique for the initial performance evaluation of AASs with repair loops.

The effect of system configuration and ramp-up time on manufacturing system acquisition under uncertain demand

Abstract The objective of this paper is to investigate the optimal allocation of capacity investments at the tactical decision-making level by incorporating the configuration characteristics of selected system alternatives comprising Dedicated Manufacturing Systems (DMS) and Reconfigurable Manufacturing Systems (RMS). Particularly, sequencing of stages in a series or a parallel configuration impacts the responsiveness in addressing capacity change requirements. We analyze what type of configuration is more suitable for a manufacturer in terms of service level and cost. We propose a mixed integer programming model by incorporating various ramp-up time patterns, which define system scalability lead time. By solving the MIP model to optimality, we aim to see how capacity is allocated to RMS and DMS based on system cost, system responsiveness, and reconfiguration speed. A discrete event simulation model is used to validate the MIP results under uncertain demand scenarios.