1,396 research outputs found
Optimization Versus Robustness in Simulation: A Practical Methodology, With a Production-Management Case-Study
Whereas Operations Research has always paid much attention to optimization, practitioners judge the robustness of the 'optimum' solution to be of greater importance.Therefore this paper proposes a practical methodology that is a stagewise combination of the following four proven techniques: (1) discrete-event simulation, (2) heuristic optimization, (3) risk or uncertainty analysis, and (4) bootstrapping.This methodology is illustrated through a case study on production control systems.That study defines robustness as the system's capability to maintain a short-term service measure, in a variety of environments (scenarios).More precisely, this measure is the probability of the short-term fill rate remaining within a prespecified range.Besides satisfying this probabilistic constraint, the system should minimize long-term work-in-process.Actually, the case study compares four systems: Kanban, Conwip, Hybrid, and Generic.These systems are studied for a well-known example, namely a production line with four stations and a single product.The conclusion of this case study is that Hybrid is best when risk is not ignored, but otherwise Generic is best: risk considerations do make a difference.simulation;experimental design;statistical methods;optimization;risk analysis;bootstrap;production control;robustness
Short-Term Robustness of Production Management Systems: New Methodology
This paper investigates the short-term robustness of production planning and control systems. This robustness is defined here as the systems ability to maintain short-term service probabilities (i.e., the probability that the fill rate remains within a prespecified range), in a variety of environments (scenarios). For this investigation, the paper introduces a heuristic, stagewise methodology that combines the techniques of discrete-event simulation, heuristic optimization, risk or uncertainty analysis, and bootstrapping. This methodology compares production control systems, subject to a short-term fill-rate constraint while minimizing long- term work-in-process (WIP). This provides a new tool for performance analysis in operations management. The methodology is illustrated via the example of a production line with four stations and a single product; it compares Kanban, Conwip, Hybrid, and Generic production control schemes.manufacturing;inventory;risk analysis;robustness and sensitivity analysis;scenarios
Validation of Models: Statistical Techniques and Data Availability
This paper shows which statistical techniques can be used to validate simulation models, depending on which real-life data are available. Concerning this availability three situations are distinguished (i) no data, (ii) only output data, and (iii) both input and output data. In case (i) - no real data - the analysts can still experiment with the simulation model to obtain simulated data; such an experiment should be guided by the statistical theory on the design of experiments. In case (ii) - only output data - real and simulated output data can be compared through the well-known two-sample Student t statistic or certain other statistics. In case (iii) - input and output data - trace-driven simulation becomes possible, but validation should not proceed in the popular way (make a scatter plot with real and simulated outputs, fit a line, and test whether that line has unit slope and passes through the origin); alternative regression and bootstrap procedures are presented. Several case studies are summarized, to illustrate the three types of situations.Statistical methods;simulation models
Multivariate Statistical Process Control Charts: An Overview
In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS
Short-Term Robustness of Production Management Systems:New Methodology
This paper investigates the short-term robustness of production planning and control systems. This robustness is defined here as the systems ability to maintain short-term service probabilities (i.e., the probability that the fill rate remains within a prespecified range), in a variety of environments (scenarios). For this investigation, the paper introduces a heuristic, stagewise methodology that combines the techniques of discrete-event simulation, heuristic optimization, risk or uncertainty analysis, and bootstrapping. This methodology compares production control systems, subject to a short-term fill-rate constraint while minimizing long- term work-in-process (WIP). This provides a new tool for performance analysis in operations management. The methodology is illustrated via the example of a production line with four stations and a single product; it compares Kanban, Conwip, Hybrid, and Generic production control schemes
The role of statistical methodology in simulation
statistical methods;simulation;operations research
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