WSC 2009 Final Abstracts
Applications - Manufacturing Applications Track
Monday 10:30:00 AM 12:00:00 PM
Analysis Methods for Manufacturing
Chair: Banu Ekren (University of Louisville)
Simulation Analysis of a Multi-Item MRP System Based on Factorial Design
Li Sun, Sunderesh S. Heragu, and Lijian Chen (University of Louisville) and Mark L. Spearman (Factory Physics, Inc)
MRP (Material Requirements Planning) has been widely used as a production scheduling system in many companies for at least the past twenty-five years. However, it is a deterministic planning tool, and therefore its ability as an effective planning tool when there is a high degree of uncertainty is questionable. In this study, we develop a multi-item MRP simulation model and design experiments to determine the effects of factors such as forecast errors, process variability and updating on key performance measures. The analysis of variance (ANOVA) results show that these factors affect the inventory and fill rate significantly.
Kriging Metamodeling in Multi-Objective Simulation Optimization
Mehdi Zakerifar, William E. Biles, and Gerald W. Evans (University of Louisville)
This paper describes an experiment exploring the potential of kriging metamodeling for multi-objective simulation optimization. The experiment studies an (s, S) inventory system with the objective of finding the optimal values of reorder point s and maximum inventory level S so as to minimize the total cost of the system while maximizing customer satisfaction. This experiment compares classical response surface methodology to kriging metamodeling as experimental approaches. The results of this experiment indicate that kriging metamodeling offers new opportunities for solving multi-objective optimization problems in stochastic simulation.
Multi Criteria Preventive Maintenance Scheduling Through Arena Based Simulation Modeling
Gonca Altuger and Constantin Chassapis (Stevens Institute of Technology)
Line performance and equipment utilization have been major points of interest for many companies due to their direct impact on productivity. Achieving the highest possible utilization while maximizing throughput will improve the line performance; will also show significant increase on the line productivity. There are many variables that affect the line utilization and performance and preventive maintenance schedule is one of them. In this paper a multi criteria decision making approach will be implemented to select the preventive maintenance schedule that gives the best utility and performance values. To demonstrate the selection process a bread packaging line is used as a case study. Environmental conditions and line behavior are developed and simulated by using an Arena-based simulation model. The Arena model is to be used as a support tool for the multi criteria decision making process.
Monday 1:30:00 PM 3:00:00 PM
Performance Improvement in Manufacturing
Chair: Ahad Ali (Lawrence Tech University)
Performance Effects of Setup Time Reduction with and Without Decision Variable Re-Optimization: A Simulation-Optimization Study
Chandandeep Singh Grewal, Silvanus Enns, and Paul Rogers (University of Calgary)
This study investigates the benefits of setup time reduction in a simple, capacity-constrained supply chain using reorder point replenishment. A discrete-event simulation model was developed and linked to an optimization engine for simulation-optimization experiments. Performance trade-off curves between total inventory and customer service levels were generated under different setup times. Comparisons were made with and without re-optimizing the reorder point and lot size decision variables after reducing setups. It was found that the benefits of setup reduction can be amplified with re-optimizing. Analysis of variance (ANOVA) was used to analyze the main and interaction effects. The behavior of the optimal decision variables under setup time reduction was also examined. Insights should provide guidance for industrial practitioners wishing to maximize the benefits of setup time reduction.
Identifying Cost Reduction and Performance Improvement Opportunities Through Simulation
Ethan Brown (Deloitte Consulting) and David Sturrock (Simio LLC)
During difficult economic times, companies have few positive cost reducing options that simultaneously improve operational performance. This paper addresses how Deloitte Consulting partnered with Simio LLC to model multiple process improvement opportunities for a HVAC manufacturer in order to reduce the facility’s operating costs. Through the use of simulation, the team was able to determine the impact of reducing the cost burden for the HVAC company by minimizing WIP inventory, eliminating over-time labor and increasing throughput. Four separate improvement opportunities were modeled independently and conjointly to provide insight into the size of the savings opportunities as well as to enable the prioritization of those efforts.
Monday 3:30:00 PM 5:00:00 PM
Chair: William Biles (University of Louisville)
Adaptive Flow Control in Flexible Flow Shop Production Systems - A Knowledge-Based Approach
Mark Aufenanger, Hendrik Varnholt, and Wilhelm Dangelmaier (Heinz Nixdorf Institute, University of Paderborn)
Today simulation is essential when researching manufacturing processes or designing production systems. But in the field of manufacturing, simulation can not only be used for purposes of research or design, it can also be utilized by flow control systems in order to make better and faster decisions. In this paper we focus on real-time scheduling in a special kind of flexible flow shop systems. These consist of production stages, which represent groups of machines doing the same work, but working at different speeds. Flow control in these flexible flow shop environments with uniform machines is exceedingly complex and it is even more complex when uncertainties are taken into consideration. For this reason we develop an adaptive scheduling heuristic, utilizing both simulation and artificial intelligence in order to make globally good decisions without causing noticeable manufacturing delays.
Enabling Flexible Manufacturing Systems by Using Level of Automation as Design Parameter
Björn Johansson, Ĺsa Fasth, and Johan Stahre (Chalmers University of Technology), Juhani Heilala (Technical Research Centre of Finland) and Swee Leong, Tina Lee, and Frank Riddick (National Institute of Standards and Technology)
Handling flexibility in an ever changing manufacturing environment is one of the key challenges for a successful industry. By using tools for virtual manufacturing, industries can analyze and predict outcomes of changes before taking action to change the real manufacturing systems. This paper describes a simulation tool that can be used to study the effect of level of automation issues on the design of manufacturing systems, including their effect on the overall system performance, ergonomics, environment, and economic measures. Determining a suitable level of automation can provide a manufacturing system with the flexibility needed to respond to the unpredictable events that occur in factory systems such as machine failures, lack of quality, lack of materials, lack of resources, etc. In addition, this tool is designed to use emerging simulation standards, allowing it to provide a neutral interface for both upstream and downstream data sources.
Economic Evaluation of the Increase in Production Capacity of a High Technology Products Manufacturing Cell Using Discrete Event Simulation
José Arnaldo Barra Montevechi, Rafael Floręncio da Silva Costa, Fabiano Leal, and Alexandre Ferreira de Pinho (Universidade Federal de Itajubá) and José Tadeu de Jesus (PadTec)
This paper presents an application of the modeling and simulation methodology along with the Design of Experiments (DOE) to aid the decision makers to know the economic risk they are taking when there are many scenarios. Firstly, the production process was studied and documented by a SIPOC, an IDEF0 and a Flowchart. These techniques were combined to elaborate the simulation conceptual model. After that, the probability distributions were chosen and fed the computer model, built to emulate the real system. The simulation model was verified and statistically validated. Sixty four possible scenarios were tested, and in this case, the DOE may contribute to select the scenarios which are relevant to the economic analysis. The simulator used was Promodel® which provided the output to fed the cash flow of each scenario. Finally, the Net Present Value (NPV) and the economic risk of each scenario were calculated.
Tuesday 8:30:00 AM 10:00:00 AM
Redesign of Manufacturing Systems Using Simulation and Optimization
Chair: Li Sun (University of Louisville)
Redesign of PCB Production Line with Simulation and Taguchi Design
Berna Dengiz (Başkent University)
This paper presents the problem of determining the optimum condition of printed circuit board (PCB) Manufacturing process in an electronic company in Ankara, Turkey. In the optimization stage of the study Taguchi method is integrated with simulation model considering minimum total cost under stochastic breakdowns. Using this methodology we investigate the system performance of the current PCB line and determine the optimum working conditions with reduced cost, time and effort.
Coupling Simulation with Heuristiclab to Solve Facility Layout Problems
Andreas Beham, Monika Kofler, Stefan Wagner, and Michael Affenzeller (Upper Austria University of Applied Sciences)
In this paper we describe the optimization of a facility layout scenario, which involves coupling simulation with the optimization environment HeuristicLab. For this purpose we show a problem formulation that acts as an interface between these two domains of problem modeling and optimization, and discuss optimization methodologies and their results for a number of artificial test problems as well as more complex real-world problems. HeuristicLab was designed with both practitioners and algorithm developers in mind. Practitioners benefit from a graphical user interface that facilitates so-called interactive algorithm engineering, where algorithms can be adjusted without actually writing code. Algorithm developers are aided in the development process by the plug-in based, easily extensible architecture and integrated parallelization functionality.
Evaluating Capacity and Expansion Opportunities at Tank Farm: A Decision Support System Using Discrete Event Simulation
Bikram Sharda and Adriana Vazquez (The Dow Chemical Company)
This paper presents a discrete event simulation based Decision Support System to evaluate tank farm operations. The Decision Support System was developed in order to reduce capital expenditures and assist in decision making for assessing the impact of different improvement opportunities. The simulation based framework captures tank farm dynamics and can be easily scaled for additional products and different tank assignments/configurations. The model was successfully used to evaluate existing tank farm operations at a Freeport site of Dow Chemical company. In this paper, we discuss the general approach used for modeling tank farm operations and different output metrics generated by the Decision Support System.
Tuesday 10:30:00 AM 12:00:00 PM
Benchmarking, Value Stream Mapping, and Simulation for Production Improvement
Chair: Edward Williams (Production Modeling Corp.)
Changes Simulation in the Organization of Production – Case Study
Marek Fertsch and Pawel Pawlewski (Poznan University of Technology)
The authors of the present paper investigate the case of using simulation in order to analyze the efficiency of changes in the organization before their implementation. The research considers changes in the organization of a selected company. The article discusses the method of assessing the efficiency with use of simulation before the actual changes are implemented. Finally, conclusions on the general characteristics of the analyzed method are provided.
Concepts for Simulation Based Value Stream Mapping
Petter Solding (Swerea SWECAST AB/Swedish Foundry Association) and Per Gullander (Swerea IVF AB)
Traditionally Value Stream Mapping (VSM) is used for quick analyses of product flows through a manufacturing system, from raw material to delivery. Discrete Event Simulation (DES) is often used for analyses of complex manufacturing systems with several products and a complex planning. These two methods have similarities but also differences. This paper presents a concept for creating dynamic value stream maps of a system using simulation. Creating dynamic value stream maps makes it possible to analyze more complex systems than traditional VSMs are able to and still visualize the results in a language the Lean coordinators recognize. The value stream map is presented in an spread sheet that can be altered in the way the team wants. Some standard icons are predefined, based on traditional VSM icons. One or more products can be visualized at the same time and simulation runs and results compared immediately, helping choosing the best solution.
Generating, Benchmarking and Simulating Production Schedules - From Formalisation to Real Problems
Gert Zülch, Peter Steininger, Thilo Gamber, and Michael Leupold (University of Karlsruhe)
Production scheduling has attracted the interest of production economics communities for decades, but there is still a gap between academic research, real-world problems, operations research and simulation. Genetic Algorithms (GA) represent a technique that has already been applied to a variety of combinatorial problems. Simulation can be used to find a solution to problems through repetitive simulation runs or to prove a solution computed by an optimization algorithm. We will explain the application of two special GAs for job-shop and resource-constrained project scheduling problems trying to bridge the gap between problem solving by algorithm and by simulation. Possible goals for scheduling problems are to minimize the makespan of a production program or to increase the due-date reliability of jobs or possibly any goal which can be described in a mathematical expression. The approach focuses on integrating a GA into a commercial software product and verifying the results with simulation.
Tuesday 1:30:00 PM 3:00:00 PM
Applications in Manufacturing
Chair: Amarnath Banerjee (Texas A&M University)
Representation, Simulation and Control of Manufacturing Process with Different Forms of Uncertainties
Hyunsoo Lee, Hongsuk Park, and Amarnath Banerjee (Texas A&M University)
This paper suggests a new methodology for effectively describing and analyzing manufacturing processes with uncertainties. Uncertain information in the form of variance and vagueness are captured using probability distribution and fuzzy logic. The captured uncertainties are incorporated into a new Petri Net model referred to as Fuzzy colored Petri Net with stochastic time delay (FCPN-std). Through FCPN-std, general manufacturing uncertainties such as unclear operation rules, unfixed re-source plan and processing time variances can be incorporated. This paper focuses on how FCPN-std model is generated and simulated for analyzing system performances. The procedure is illustrated using an example process. The main advantages of FCPN-std model are in the ability to capture and analyze manufacturing uncertainties, and provide an opportunity to im-prove process performance in the presence of uncertainties. These characteristics help modelers design exactly manufactur-ing process without approximations/ignorance and guide how manufacturing process can be improved through incorporated information.
Simulating an Applied Model to Optimize Cell Production and Parts Supply (mizusumashi) for Laptop Assembly
Hidetaka Ichikawa (Kagoshima University)
This study investigates the optimal production system in Japan using a large-scale sampling survey of laptop assembly production firm. In Japan, many companies adopt a cell production system which is studied and emulated by other manufacturing companies around the world. Companies which successfully introduced the cell production process have improved their manufacturing, but related processes such as parts supply have not been optimized. If the supply of parts is inefficient, overall efficiency suffers even if cell production works optimally. In fact, it is absolutely necessary to pursue a total optimization. Thus, this paper considers the manufacturing system's needs in terms of the most suitable number of material handlers to supply parts(Mizusumashi) from receiving area to the cells as it is linked to efficient cell production for laptop assembly using simulation.