Edward Huang

System and simulation modeling using SysML

Simulation languages and the GUIs supporting them may be excellent tools for creating simulation codes, but are not necessarily the best tools to use for creating descriptions of systems, i.e., for modeling. In 2006, OMG published the initial standard specification (OMG 2006) for SysML (Systems Modeling Language), an extension of UML (OMG 2007) designed specifically to support systems engineering. SysML shows great promise for creating object-oriented models of systems that incorporate not only software, but also people, material, and other physical resources, expressing both structure and behavior for such systems. In this paper, we explore the use of SysML both to model a system to be simulated and to support the automatic generation of simulation models.

Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

Recent advances in simulation optimization research and explosive growth in computing power have made it possible to optimize complex stochastic systems that are otherwise intractable. In the first part of this paper, we classify simulation optimization techniques into four categories based on how the search is conducted. We provide tutorial expositions on representative methods from each category, with a focus in recent developments, and compare the strengths and limitations of each category. In the second part of this paper, we review applications of simulation optimization in various contexts, with detailed discussions on health care, logistics, and manufacturing systems. Finally, we explore the potential of simulation optimization in the new era. Specifically, we discuss how simulation optimization can benefit from cloud computing and high-performance computing, its integration with big data analytics, and the value of simulation optimization to help address challenges in engineering design of complex systems.

Strategic robust supply chain design based on the Pareto-optimal tradeoff between efficiency and risk

The strategic design of a robust supply chain has to determine the configuration of the supply chain so that its performance remains of a consistently high quality for all possible future conditions. The current modeling techniques often only consider either the efficiency or the risk of the supply chain. Instead, we define the strategic robust supply chain design as the set of all Pareto-optimal configurations considering simultaneously the efficiency and the risk, where the risk is measured by the standard deviation of the efficiency. We model the problem as the Mean–Standard Deviation Robust Design Problem (MSD-RDP). Since the standard deviation has a square root expression, which makes standard maximization algorithms based on mixed-integer linear programming non-applicable, we show the equivalency to the Mean–Variance Robust Design Problem (MV-RDP). The MV-RDP yields an infinite number of mixed-integer programming problems with quadratic objective (MIQO) when considering all possible tradeoff weights. In order to identify all Pareto-optimal configurations efficiently, we extend the branch-and-reduce algorithm by applying optimality cuts and upper bounds to eliminate parts of the infeasible region and the non-Pareto-optimal region. We show that all Pareto-optimal configurations can be found within a prescribed optimality tolerance with a finite number of iterations of solving the MIQO. Numerical experience for a metallurgical case is reported.

MO2TOS: Multi-Fidelity Optimization with Ordinal Transformation and Optimal Sampling

Simulation optimization can be used to solve many complex optimization problems in automation applications such as job scheduling and inventory control. We propose a new framework to perform efficient simulation optimization when simulation models with different fidelity levels are available. The framework consists of two novel methodologies: ordinal transformation (OT) and optimal sampling (OS). The OT methodology uses the low-fidelity simulations to transform the original solution space into an ordinal space that encapsulates useful information from the low-fidelity model. The OS methodology efficiently uses high-fidelity simulations to sample the transformed space in search of the optimal solution. Through theoretical analysis and numerical experiments, we demonstrate the promising performance of the multi-fidelity optimization with ordinal transformation and optimal sampling (MO2TOS) framework.

Optimal inventory control in a multi-period newsvendor problem with non-stationary demand

The optimal control of inventory in supply chains plays a key role in the competiveness of a corporation. The inventory cost can account for half of companys logistics cost. The classical inventory models, e.g., newsvendor and EOQ models, assume either a single or infinite planning periods. However, these models may not be applied to perishable products which usually have a certain shelf life. To optimize the total logistic cost for perishable products, this paper presents a multi-period newsvendor model, and the problem is formulated as a multi-stage stochastic programming model with integer recourse decisions. We extend the progressive hedging method to solve the model efficiently. A numerical example and its sensitivity analysis are demonstrated.

Ontologies and simulation: a practical approach

The challenges in cost-effectively deploying simulation technology are well known. Two major challenges are creating an appropriate conceptual model and translating that conceptual model correctly into a computational model. Ontologies have been widely discussed as one mechanism for capturing modelling knowledge in a reusable form, making it effectively available in the conceptual and computational modelling phases. In this paper, we show how ontologies can be effectively deployed in simulation using recent innovations from systems engineering and software engineering. We use OMG SysML™ to create an ontology implementation referred to as a domain-specific language, or DSL, for a class of simulation applications; the DSL is used to create a specific (conceptual) user model for a problem in the domain. We then use model transformation to automate the translation to a computational simulation model. Two proof-of-concept implementations are described, one using a legacy simulation language, and another using an object-oriented simulation language.

Efficient multi-fidelity simulation optimization

Simulation models of different fidelity levels are often available for a complex system. High-fidelity simulations are accurate but time-consuming. Therefore, they can only be applied to a small number of solutions. Low-fidelity simulations are faster and can evaluate a large number of solutions. But their results may contain significant bias and variability. We propose an Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling (MO2TOS) framework to exploit the benefits of high- and low-fidelity simulations to efficiently identify a (near) optimal solution. MO2TOS uses low-fidelity simulations for all solutions and then assigns a fixed budget of high-fidelity simulations to solutions based on low-fidelity simulation results. We show the benefits of MO2TOS via theoretical analysis and numerical experiments with deterministic simulations and stochastic simulations where noise is negligible with sufficient replications. We compare MO2TOS to Equal Allocation (EA) and Optimal Computing Budget Allocation (OCBA). MO2TOS consistently outperforms both EA and OCBA.

Airport baggage handling system simulation modeling using SysML

In recent years, the number of passengers visiting the major international airport, Taiwan Taoyuan International Airport (TPE), has increased dramatically. The passenger traffic has gone beyond the capacity of the airport as it was constructed for. One important infrastructure in the airport is the baggage handling system which consists of a set of conveyors that transport checked baggage to the baggage unloading zones or the buffer zones. How to efficiently operate the baggage handling system is of critical importance. Simulation models are widely used to evaluate the system performances and to compare alternatives. However, modeling and analyzing the baggage handling system is not straightforward. However, the means of modeling and analyzing the baggage handling system is not in good form. The simulation model itself is a black box, making the validation of a model a challenge. In this study, we propose to use the formal modeling language, System Modeling Language (SysML), to model the baggage handling system. System Modeling Language provides a formal representation of the system and is able to enhance the validation of the system. We also use the SysML model to generate the corresponding simulation models. We compare different control rules and report the analysis result.

An ordinal transformation framework for multi-fidelity simulation optimization

Simulation models of different levels of fidelity are often available for evaluating alternative solutions of a complex system. High-fidelity simulations generate accurate predictions but can be very time-consuming to run. Therefore, they can only be applied to a small number of solutions. Low-fidelity simulations are much faster and can evaluate a large number of solutions. But simulation results may contain significant bias and variability. We propose a novel ordinal transformation framework to exploit the benefits of both high- and low-fidelity simulation models to efficiently identify a (near) optimal solution. A two-stage simulation optimization method under the ordinal transformation framework is described. Through preliminary theoretical analysis and numerical experiments, we demonstrate the promising performance of ordinal transformation, which opens up a new and potentially fruitful research avenue.

Robust global supply network design

It has been widely documented that disruptions and changes in the environment of manufacturing in a global enterprise impact the performance of the enabling supply network. The need exists for a methodology that can not only provide an objective and transparent evaluation of the efficiency and risks of a supply network configuration but that can also design supply network configurations with provably superior performance. Supply chain engineering SCE fundamentally applies the perspective and methodology of systems engineering to the domain of supply chains. SCE is the integrator of various corporate functions such as purchasing, production, distribution, and sustainment over the total product life cycle from design, through deployment, production, sustainment, and disposal. A supply chain is actually a network that consists of a diverse set of organizational and geographical components that typically change over time. The systematic design and management of such an inherently complex system requires the principles and tools of model-based systems engineering. A comprehensive and integrated model of the structure and the behavior of the supply network of a manufacturing enterprise has been developed. In this model the inherent uncertainty of the future environmental conditions in which the supply network will have to function is captured in a large number of scenarios. The variability of the profits achieved by the supply network for the various scenarios is used as a proxy for the supply network risk. The model allows for the explicit tradeoff of the systems life cycle profit with the variability of this profit. An efficient solution algorithm eliminates the vast majority of feasible supply network configurations from consideration and identifies all the Pareto-optimal configurations. However, the final selection of the network configuration depends on the risk preferences of the designer and the complexity of the enterprise. Selecting the supply network configuration with the appropriate robustness becomes one of the principal risk mitigation policies for the network and, in turn, the enterprise. The combination of the socio designer risk preferences and analysis and technical Pareto-optimization of all feasible supply network configurations creates a transparent engineering design process that finds the supply network configuration best matched to the objectives of the manufacturing enterprise.