Moeed Haghnevis

A Modeling Framework for Engineered Complex Adaptive Systems

The objective of this paper is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in a certain engineered complex adaptive system. A conceptual framework is provided to describe the structure of a class of engineered complex systems and predict their future adaptive patterns. The proposed modeling approach allows examining complexity in the structure and the behavior of components as a result of their connections and in relation to their environment. Electrical power demand is used to illustrate the applicability of the modeling approach. We describe and use the major differences of natural complex adaptive systems (CASs) with artificial/engineered CASs to build our framework. The framework allows focus on the critical factors of an engineered system, but also enables one to synthetically employ engineering and mathematical models to analyze and measure complexity in such systems without complex modeling. This paper adopts concepts of complex systems science to management science and system-of-systems engineering.

An improved model for production systems with mixed queuing priorities: an integrated simulation, AHP and Value Engineering approach

The objective of this study was to provide a scheduling solution for the mixed priority queues discipline. This simulation model integrates Analytic Hierarchy Process (AHP) and Value Engineering (VE) for modelling and assessment of complex production systems with mixed queue priorities and service discipline. In such systems, the arriving entity will be placed in the queue according to the spectrum of similar properties; this spectrum is arranged according to the entity currently being serviced. In addition, each machine is only allowed to service identical products up to a limited period and the service turn over moves clockwise.

Batch Production Scheduling for Semiconductor Back-End Operations

A good production schedule in a semiconductor back-end facility is critical for the on time delivery of customer orders. However, the scheduling process is usually difficult due to the wide product mix, large number of parallel machines, product family-related setups, and high weekly demand consisting of thousands of lots. In this paper, we present a new mixed-integer-linear-programming (MILP) model for the batch production scheduling of a semiconductor back-end facility with serial production stages. Computational results are provided for finding optimal solutions to small problem instances. Due to the limitation on the solvable size of the MILP formulation, a deterministic scheduling system (DSS), including an optimizer and a scheduler, is proposed to provide suboptimal solutions in a reasonable time for large real-world problem instances. Small problem instances are randomly generated to compare the performances of the optimization model and the DSS. An experimental design is utilized to understand the behavior of the DSS under different production scenarios.

Scenario analysis of technology products with an agent-based simulation and data mining framework

A framework is presented to simulate and analyze the effect of multiple business scenarios on the adoption behavior of a group of technology products. Diffusion is viewed as an emergent phenomenon that results from the interaction of consumers. An agent-based model is used in which potential adopters of technology product are allowed to be influenced by their local interactions within the social network. Along with social influence, the effect of product features is important and we ascribe feature sensing attributes to the consumer agents along with sensitivities to social influence. The model encompasses utility theory and discrete choice models in the decision-making process for the consumers. We use expressive machine learning algorithms that can handle complex, nonlinear, and interactive effects to identify important inputs that contribute to the model and to graphically summarize their effects. We present a realistic case study that demonstrates the ability of this framework to model changes in market shares for a group of products in response to business scenarios such as new product introduction and product discontinuation under different pricing strategies. The models and other tools developed here are envisioned to be a part of a recommender system that provides insights into the effects of various business scenarios on shaping market shares of different product groups.

Modelling priority queues with dependent service discipline by computer simulation

Manufacturing processes have become more complex in the advance technology. Spectrum disciplines make a big challenge in some manufacturing and production systems that have to follow dependent sequences. This study presents a method for modelling of complex production systems with mix queue priorities and service discipline. In such systems, each group of jobs is allowed to be in service for limited amount of time. Jobs that have similar group number with the job in service will be located in the highest priority position and other groups are ranked in a queue according to spectrum discipline. The objective of this study is modelling of complex manufacturing and production systems with dependent priority queues where no closed form expression and mathematical models have been developed. A case study is presented to show applicability of the model. The approach of this study for such complex settings may be easily extended to other similar production systems.

Machine qualification management for a semiconductor back-end facility

In order to process a product in a semiconductor back-end facility, a machine needs to be qualified first by having a product-specific software program installed on it. Then a tool set must be available and attached on the machine while it is processing the product. In general not all machines are qualified to process all products due to the high machine qualification cost and tool set availability. However, the machine qualification decision is very important because it affects capacity allocation in the facility and subsequently affects daily production scheduling. To balance the tradeoff between the machine qualification costs and backorder costs, a product-machine qualification optimization model is proposed in this paper.

A unique fuzzy simulation approach for concurrent improvement of customer satisfaction in integrated information and production processes with ambiguity

In a real business process, the parameters for probability distributions are unknown or ambiguous. Therefore, conventional modeling of these systems using deterministic parameters is questionable. Fuzzy simulation enables modeling uncertainties in such systems. The objective of this study is to model and improve concurrently the performance of integrated information, business, and production process of a manufacturing system using fuzzy simulation. Here, performance is defined as customer satisfaction. The superiority of the fuzzy simulation approach over conventional models used in prior studies is discussed. The integrated fuzzy approach in this study enables evaluating customer lead times in six dimensions with uncertainties considerations. Major effects of business process reengineering and information technology are evaluated before the actual implementation. The comparison results indicate that the fuzzy model is closer to the actual system. This is the first study that presents an integrated fuzzy model for improvement of customer satisfaction in integrated information, business, and production processes with ambiguity.

An Integrated Optimization and Agent-Based Framework for the U.S. Power System

Abstract Today, many of the engineered systems are comprised of a large number of components that interact with each other and have the ability to exhibit emergent behavior thus enabling a system to adapt to changing environments. Using the example of the US power grid as a complex adaptive system, we demonstrate how components in a multi-layered power grid structure dynamically interact, evolve and adapt over time. In our model, electricity regulators strive to balance workload by dynamically adjusting service attributes in response to demand uctuations. Additionally, they seek to change long-term consumption patterns by providing incentives and social education. Moreover, consumer agents focus on maximizing quantitative and qualitative utilities. By embedding a non-convex optimization model with the agent-based framework we study cooperativeness or competition in the consumers game environment. Our framework allows us to study the behavior of consumers under di_erent control and incentive strategies. We expand model dynamics to include intrinsic environment and control factors. This study also examines circumstances in which agent-based and equilibrium models present similar outcomes or are unable to converge to same results. This method is used to study the robustness of the results, present equilibriums of interoperability equations, and study dynamics of traits.

An Agent Based Simulation for Building Energy System Modeling

Extensive research has been done on the centralized building energy system modeling and simulation. However the centralized structure is limited to study and simulate the energy interaction between different buildings at different locations. This paper reviews the building energy consumption model, energy storage system and energy generation system in the Net-zero buildings. Incorporate with the real-time price rate model, this paper develops an agent based simulation framework for distributed building energy system under uncertainty. Each sub system is developed as an agent in the simulation model, and a virtual decision agent is designed to simulate the operation strategy. The energy flow between different agents can be easily monitored from the simulation. The differences between on-peak and off-peak control are demonstrated from the simulation result.© 2010 ASME