Taguchi's parameter design: a panel discussion
Technometrics
Evaluation of optimization methods for machining economics models
Computers and Operations Research
Simulation optimization by genetic search
Mathematics and Computers in Simulation
ICC&IE-94 Selected papers from the 16th annual conference on Computers and industrial engineering
Genetic algorithms in optimizing simulated systems
WSC '95 Proceedings of the 27th conference on Winter simulation
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Comparison of global search methods for design optimization using simulation
WSC '91 Proceedings of the 23rd conference on Winter simulation
Designing computer simulation experiments
WSC '88 Proceedings of the 20th conference on Winter simulation
Simulation optimization methodologies
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
A modified genetic algorithm for single machine scheduling
Computers and Industrial Engineering
Selection of cutting tools and conditions of machining operations using an expert system
Computers in Industry
Simulation optimization: a survey of simulation optimization techniques and procedures
Proceedings of the 32nd conference on Winter simulation
Empirical comparison of search algorithms for discrete event simulation
Computers and Industrial Engineering
Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computer-Aided Manufacturing
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks
Genetic Algorithms
Building valid models: how to build valid and credible simulation models
Proceedings of the 33nd conference on Winter simulation
Theory of Modeling and Simulation
Theory of Modeling and Simulation
Computers and Industrial Engineering
Computers and Industrial Engineering
Design and Analysis of Experiments
Design and Analysis of Experiments
Computers and Industrial Engineering
Genetic algorithms for coordinated scheduling of production and air transportation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 0.01 |
This paper presents a Parameter Design (PD) approach that provides near-optimal settings to the process parameters of a single lathe machine with high-volume production. Optimized process parameters include both machining parameters (cutting speed, feed rate, and depth of cut) and production parameters (material order size and inventory safety stock and reorder point). In high-volume production, machining parameters have amplified impacts on the machine performance in terms of productivity (cycle time), reliability (tool life), and product quality (surface finish). In addition, production parameters become critical in high-volume production since they directly impact the overall order fulfillment (production makespan and delivery reliability). Hence, this paper extends the conventional per-part machining cost model into a per-order production cost model by consolidating the production economics of both machining parameters and production controls. Discrete Event Simulation (DES) is utilized to capture the stochastic and dynamic production attributes and to transfer the static machine PD model into a dynamic PD-DES production model. The model is also utilized to accumulate the per-order running cost over production time while incorporating the impacts of process variability in tool life, labor efficiency, machining conditions, order lead time, and demand rate. Using the PD-DES model as a dynamic fitness function, a Simple Genetic Algorithm (SGA) is developed and applied to a CNC lathe machine to determine near-optimal settings to both machining and production process parameters so that the overall per-order production cost is minimized. Results showed effective SGA convergence profile with relatively low number of search generations. Sensitivity analysis with SGA parameters is conducted to demonstrate the search's robustness. The benefits of the per-order cost model are illustrated by repeating the SGA solution using machine productivity as a fitness criterion. The new SGA solution resulted in a better productivity but at a higher per-order cost. Finally, the effectiveness of SGA search is illustrated by outperforming the solutions obtained from two-level and three-level full factorial designs.