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Enterprise simulation: a hybrid system approach

Manufacturing enterprise decisions can be classified into four groups: business decisions, design decisions, engineering decisions, and production decisions. Numerous physical and software simulation techniques have been used to evaluate specific decisions by predicting their impact on either system performance or product performance. In this paper, we focus on the impact of production decisions, evaluated using discrete-event-simulation models, on enterprise-level performance measures. We argue that these discrete-event models alone are not enough to capture this impact. To address this problem, we propose integrating discrete-event simulation models with system dynamics models in a hybrid approach to the simulation of the entire enterprise system. This hybrid approach is conceptually consistent with current business trend toward integrated systems. We show the potentials for using this approach through an example of a semiconductor enterprise.

Ontologies for supply chain simulation modeling

Simulation might be an effective decision support tool in supply chain management. The review of supply chain simulation modeling methodologies revealed some issues one of which is the practicability of simulation in the supply chain environment. The supply chain environment is dynamic, information intensive, geographically dispersed, and heterogeneous. In order to develop usable supply chain simulation models, the models should be feasibly applicable in the supply chain environment. Distributed simulation models have been used by several researchers, however, their complexity and usability hindered their continuation. In this paper, a new approach is proposed. The approach is based on ontologies to integrate several supply chain views and models, which captures the required distributed knowledge to build simulation models. The ontology core is based on the SCOR model as the widely shared supply chain concepts. The ontology can define any supply chain and help the user to build the required simulation models

Enhancing the undergraduate industrial engineering curriculum: Defining desired characteristics and emerging topics

Purpose – This paper aims to present the results of an initial research study conducted to identify the desired professional characteristics of an industrial engineer with an undergraduate degree and the emerging topic areas that should be incorporated into the curriculum to prepare industrial engineering (IE) graduates for the future workforce.Design/methodology/approach – A survey was administered to faculty and industry professionals across the USA to describe the desired characteristics and define the important emerging topic areas. The modified three‐round Delphi technique was applied to obtain consensus and ranking of the emerging topics.Findings – The research findings that identify the desired characteristics and the most important emerging topics to be incorporated into the reengineered curriculum discussed in this paper. Statistical analysis of the results indicates some differences in opinions expressed by persons in academic settings and those working in business and industry.Originality/value...

Assessment Framework for the Evaluation and Prioritization of University Inventions for Licensing and Commercialization

Abstract: The rise in university technology transfer and licensing activities has overstretched the human and financial resources of the licensing and technology management offices of many U.S. universities. This research proposes a framework to properly predict and identify which of the universitys intellectual properties, inventions, or technology discoveries have an above-average licensing and commercialization potential, so limited human and financial resources could be allocated for the pursuit of truly important breakthrough discoveries. This research will identify the determinants and decision factors that influence or impact the licensing and commercialization of university technologies, determine their relative importance, identify the most current and up-to-date technology selection criteria used by the most influential corporate technology licensing professionals, and develop a framework to assist in the assessment of the potential viability of the universitys technologies for licensing and commercialization.

Intelligent scheduling for flexible manufacturing systems

A scheme for the scheduling of flexible manufacturing systems (FMSs) have been developed. It integrates neural networks, parallel Monte-Carlo simulation, genetic algorithms, and machine learning. Modular neural networks are used to generate a small set of attractive plans and schedules from a larger list of such plans and schedules. Parallel Monte-Carlo simulation predicts the impact of each on the future evolution of the manufacturing system. Genetic algorithms are utilized to combine attractive alternatives into a single best decision. Induction mechanisms are used for learning and simplify the decision process for future performance. The development of a modular neural network architecture for candidate rule selection for a FMS cell is investigated. A scheduling example illustrates the scheme capabilities including speed, adaptability, and computational efficiency.<<ETX>>

Multiobjective simulation optimization using an enhanced genetic algorithm

This paper presents an improved genetic algorithm approach, based on new ranking strategy, to conduct multiobjective optimization of simulation modeling problems. This approach integrates a simulation model with stochastic nondomination-based multiobjective optimization technique and genetic algorithms. New genetic operators are introduced to enhance the algorithm performance of finding Pareto optimal solutions and its efficiency in terms of computational effort. An elitism operator is employed to ensure the propagation of the Pareto optimal set, and a dynamic expansion operator to increase the population size. An importation operator is adapted to explore some new regions of the search space. Moreover, new concepts of stochastic and significant dominance are introduced to improve the definition of dominance in stochastic environments.

Stability analysis of the supply chain by using neural networks and genetic algorithms

Effectively managing a supply chain requires visibility to detect unexpected variations in the dynamics of the supply chain environment at an early stage. This paper proposes a methodology that captures the dynamics of the supply chain, predicts and analyzes future behavior modes, and indicates potentials for modifications in the supply chain parameters in order to avoid or mitigate possible oscillatory behaviors. Neural networks are used to capture the dynamics from the system dynamic models and analyze simulation results in order to predict changes before they take place. Optimization techniques based on genetic algorithms are applied to find the best setting of the supply chain parameters that minimize the oscillations. A case study in the electronics manufacturing industry is used to illustrate the methodology.

Handling Uncertainty In The Analytic Hierarchy Process: A Stochastic Approach

This paper describes a methodology for handling the propagation of uncertainty in the analytic hierarchy process (AHP). In real applications, the pairwise comparisons are usually subject to judgmental errors and are inconsistent and conflicting with each other. Therefore, the weight point estimates provided by the eigenvector method are necessarily approximate. This uncertainty associated with subjective judgmental errors may affect the rank order of decision alternatives. A new stochastic approach is presented to capture the uncertain behavior of the global AHP weights. This approach could help decision makers gain insight into how the imprecision in judgment ratios may affect their choice toward the best solution and how the best alternative(s) may be identified with certain confidence. The proposed approach is applied to the example problem introduced by Saaty for the best high school selection to illustrate the concepts introduced in this paper and to prove its usefulness and practicality.

Integrating scheduling and control functions in computer integrated manufacturing using artificial intelligence

Abstract Proper integration of scheduling and control in Flexible Manufacturing Systems will make available the required level of decision-making capacity to provide a flexibly-automated, efficient, and quality manufacturing process. To achieve this level of integration, the developments in computer technology and sophisticated techniques of artificial intelligence (AI) should be applied to such FMS functions as scheduling. In this paper, we present an Intelligent Scheduling System for FMS under development that makes use of the integration of two AI technologies. These two AI technologies — Neural Networks and Expert Systems — provide the intelligence that the scheduling function requires in order to generate goodschedules within the restrictions imposed by real-time problems. Because the system has the ability to plan ahead and learn, it has a higher probability of success than conventional approaches. The adaptive behavior that will be achieved contribute to the integration of scheduling and control in FMS.

Using system dynamics, neural nets, and eigenvalues to analyse supply chain behaviour. A case study

This paper presents a new methodology to predict behavioural changes in manufacturing supply chains due to endogenous and/or exogenous influences in the short and long term horizons. Additionally, the methodology permits the identification of the causes that may induce a negative behaviour when predicted. Initially, a dynamic model of the supply chain is developed using system dynamics simulation. Using this model, a neural network is trained to make online predictions of behavioural changes at a very early decision making stage so that an enterprise would have enough time to respond and counteract any unwanted situations. Eigenvalue analysis is used to investigate any undesired foreseen behaviour, and principles of stability and controllability are used to study several decision configurations that eliminate or mitigate such behaviour. A case study of an actual electronics manufacturing company demonstrates how to apply this methodology and its real benefits for enterprises.