Fang Zhang

This paper describes a supply chain simulation by using hybrid-models that combine discrete-event models and system dynamics models. The discrete-event models represent operational processes inside of supply chain, and the system dynamics models represent supply chain reactions under management circumstance. The scope is a real supply chain system with a large scale and complicated operational rules. The simulation results clarified supply chain features in a long-term manufacturing management environment.

FORESIGHT VERSUS SIMULATION: CONSTRUCTION PLANNING USING GRAPHICAL CONSTRAINT-BASED MODELING

Planning construction projects typically makes use of the activity network-based Critical Path Method (CPM) since it is simple to use and reasonably versatile. Most other planning techniques are either aimed at specialized types of construction work (such as linear scheduling) or are peripheral tools to be used conjunctively (such as nD-CAD). Discrete-event simulation has also been used for construction planning, and while it is extremely versatile, it lacks the simplicity in use of CPM and so has not been widely adopted within the industry. This paper goes back to first principles, identifying the needs of construction project planning and how existing tools meet (or fail to meet) these requirements. Based on this, it proposes a new modeling paradigm, Foresight, better suited to contemporary construction project planning. The principles of the method and its relative merits are demonstrated relative to conventional simulation in a series of construction case studies.

AMERICAN OPTION PRICING WITH RANDOMIZED QUASI-MONTE CARLO SIMULATIONS

We study the pricing of American options using least-squares Monte Carlo combined with randomized quasi-Monte Carlo (RQMC), viewed as a variance reduction method. We find that RQMC reduces both the variance and the bias of the option price obtained in an out-of-sample evaluation of the retained policy, and improves the quality of the returned policy on average. Various sampling methods of the underlying stochastic processes are compared and the variance reduction is analyzed in terms of a functional ANOVA decomposition.

IIMPORTANCE SAMPLING FOR RISK CONTRIBUTIONS OF CREDIT PORTFOLIOS

Value-at-Risk is often used as a risk measure of credit portfolios, and it can be decomposed into a sum of risk contributions associated with individual obligors. These risk contributions play an important role in risk management of credit portfolios. They can be used to measure risk-adjusted performances of subportfolios and to allocate risk capital. Mathematically, risk contributions can be represented as conditional expectations, which are conditioned on rare events. In this paper, we develop a restricted importance sampling (IS) method for simulating risk contributions, and devise estimators whose mean square errors converge in a rate of n −1. Furthermore, we combine our method with the IS method in the literature to improve the efficiency of the estimators. Numerical examples show that the proposed method works quite well.

RESPONSE SURFACE COMPUTATION VIA SIMULATION IN THE PRESENCE OF CONVEXITY

We consider the problem of computing a response surface when the underlying function is known to be convex. We introduce a methodology that incorporates the convexity into the function estimator. The proposed response surface estimator is formulated as a finite dimensional quadratic program and exhibits convergence properties as a global approximation to the true function. Numerical results are presented to illustrate the convergence behavior of the proposed estimator and its potential application to simulation optimization.

PROJECT MANAGEMENT SIMULATION WITH PTB PROJECT TEAM BUILDER

This paper presents a new tool for the teaching professional and students of Project Management�a tool that can easily integrate traditional teaching based on any course or textbook available in the market. The Project Team Builder software tool combines an interactive, dynamic case study and a simple yet effectiv

A SPECTRUM OF TRAFFIC FLOW MODELING AT MULTIPLE SCALES

This paper presents a broad perspective on traffic flow modeling at a spectrum of four scales. Modeling objectives and model properties at each scale are discussed and existing efforts are reviewed. In order to ensure modeling consistency and provide a microscopic basis for macroscopic models, it is critical to address the coupling among models at different scales, i.e. how less detailed models are derived from more detailed models and, conversely, how more detailed models are aggregated to less detailed models. With this understanding, a consistent modeling approach is proposed based on field theory and modeling strategies at each of the four scales are discussed. In addition, a special case is formulated at both microscopic and macroscopic scales. Numerical and empirical results suggest that these special cases perform satisfactorily and aggregate to realistic macroscopic behavior. By ensuring model coupling and modeling consistency, the proposed approach is able to establish the theoretical foundation for traffic modeling and simulation at multiple scales seamlessly within a single system.

MANUAL ASSEMBLY LINE OPERATOR SCHEDULING USING HIERARCHICAL PREFERENCE AGGREGATION

Successful companies are the ones that can compete in the global market by embracing technological advancements, employing the lean principles, maximizing resource utilizations without sacrificing customer satisfaction. Lean principles are widely applied in semi or fully automated production processes. This research highlights the application of the lean principles to manual assembly processes, where the operator characteristics are considered as deterministic of operator schedule. In this study, a manual circuit breaker assembly line is examined, where operator skill levels, attention spans, classified as reliability measures considered to select the most suitable resource allocation and break schedules. The effect of operator attributes on the process is modeled and simulated using Arena, followed by a hierarchical preference aggregation technique as the decision making tool. This paper provides an operator schedule selection approach which can be both employed in manual and semi – automated production processes.

RANDOM SEARCH IN HIGH DIMENSIONAL STOCHASTIC OPTIMIZATION

We consider the use of random search for high dimensional optimization problems where the objective function to be optimized can only be computed with error. Random search is easy to carry out, but extraction of information concerning the objective function is not so straightforward. We propose fitting a statistical model to the objective function values obtained in such a search, and show how the fitted model can be used to estimate the best value obtained when the search effort is limited and how this value compares with the unknown true optimum value. A possible use of this approach is in combinatorial optimization problems. The dimension in such a problem is not usually considered, but if a dimension can be associated with it, then it is likely to be high. We illustrate our method with a numerical example involving a travelling salesman problem.

EXAMINING THE RELATIONSHIP BETWEEN ALGORITHM STOPPING CRITERIA AND PERFORMANCE USING ELITIST GENETIC ALGORITHM

A major disadvantage of using a genetic algorithm for solving a complex problem is that it requires a relatively large amount of computational time to search for the solution space before the solution is finally attained. Thus, it is necessary to identify the tradeoff between the algorithm stopping criteria and the algorithm performance. As an effort of determining the tradeoff, this paper examines the relationship between the algorithm performance and algorithm stopping criteria. Two algorithm stopping criteria, such as the different numbers of unique schedules and the number of generations, are used, while existing studies employ the number of generations as a sole stopping condition. Elitist genetic algorithm is used to solve 30 projects having 30-Activity with four renewable resources for statistical analysis. The relationships are presented by comparing means for algorithm performance measures, which include the fitness values, total algorithm runtime in millisecond, and the flatline starting generation number.