This is a report on the work done at the International Institute for Applied Systems Analysis in 1976 to 1977 under a grant from Volkswagenwerk Foundation, whose objective was to explore the potential and the mechanisms of logistic analysis to describe the structure and the evolution of energy systems. 

Volume One contains the phenomenological part. About 300 cases were examined, some of which are reported in detail. The quality of the logistic description is generally excellent, even for cases extending 150 years into the past and with all the perturbations such a long time span entails, and consequently we thought it appropriate to extend the description into the future and use it for prediction. 

This was not really the objective of the grant but 'it naturally arises from the work and provides food for thought. Projections in the current literature appear to be in fact strongly inconsistent with the past, which casts doubts on their realizability, and are even internally inconsistent, which reinforces these doubts. 

The fact that numerous "free" choices at the social level lead to very regular overall patterns should perhaps temper the feeling of being caught in a deterministic clockwork. 

In Volume Two, devoted to the theoretical work, F. Fleck deals specifically with this problem showing the final regularity derived from a set of stochastic, i.e. "free", decisions. V. Peterka, on the other hand, operates at a more aggregated level, where one can start to speak of economic determinism. He describes a form of fate we are more ready to accept, if only grudgingly. 

Our exploration has generated more problems than we have solved; thus the field appears very fertile for future research.

The Dynamics of Energy Systems and the Logistic Substitution Model. Executive Summary

This article examines the use of expert systems in international marketing. In view of the increasing importance of internationalization of business around the world, a new generation of computer information systems known as Expert Systems offer a unique potential in international market entry and constantly changing global market conditions. The specific purposes of this paper are to develop a set of evaluative criteria for the use of expert systems, examine the nature of decisions in major international marketing decision areas, and evaluate each decision area against the set of criteria to determine if expert systems are suitable for that decision area.

Expert systems in international marketing

Expert system can be defined as an intelligent computer program, a repository of knowledge, a set of rules, like a human consultant, all aiming at delivering accurate solutions/suggestions to a problem at any level, say during plan, design, manufacture, and quality control. Some of the important definitions are quoted here. “An expert system is a computer system used to distribute the expertise of a human or group of humans throughout a group of users” (Wang et al., 1991) “Expert systems (ES) are a branch of applied artificial intelligence involving that expertise, which is the vast body of task-specific knowledge, is transferred from a human to a computer. This knowledge is then stored in the computer and users call upon the computer for specific advice as needed” (Liao, 2005) “An expert system is one that has expert rules and avoids blind search, reasons by manipulating symbols, grasps fundamental domain principles, and has complete weaker reasoning methods to fall back on when expert rules fail and to use in producing explanations. It deals with difficult problems in a complex domain, can take a problem description in lay terms and convert it to an internal representation appropriate for processing with its expert rules, and it can reason about its own knowledge (or lack thereof), especially to reconstruct inference paths rationally for explanation and self-justification” (Dym, 1985) The general structure of an expert system is shown in Fig. 1 (Dym, 1985; Tisza, 1995). The components of the expert system include input/output facilities, which allow the user to communicate with the expert system and to use and create a database for a specific problem under investigation; an inference engine, which contains rules or reasoning methods, that acts upon the input query and knowledge base, data base, to provide solution and justification to the problem stated. This is like an executive that runs the expert system. It fires existing rules according to the problem stated that not only acts on the knowledge/data base to generate solution, but also updates the knowledge/data base by adding new knowledge/data to it; a knowledge base, containing the basic knowledge about the field including the facts, beliefs, procedures, expert opinion etc.; a knowledge acquisition system, which does the job of acquiring knowledge automatically from varied resources like libraries, experts, other data bases and so on; a data base, which is similar to knowledge base, where in quantified data (e.g., material properties, tool conditions, forming machine parameters etc.) pertaining to that field are located that will be used by the inference engine

/pdf/expert-system-applications-in-sheet-metal-forming-35i3pyfykd.pdf

Expert System Applications in Sheet Metal Forming

Tailor-welded blanks (TWB) are blanks with sheets of similar or dissimilar thicknesses, materials, coatings welded in a single plane before forming. Applications of TWB include car door inner panel, deck lids, bumper, side frame rails etc. in automotive sector (Kusuda et al., 1997; Pallet & Lark, 2001). Aluminium TWBs are widely used in automotive industries because of their great benefits in reducing weight and manufacturing costs of automotive components leading to decreased vehicle weight, and reduction in fuel consumption. The general benefits of using TWBs in the automotive sector are: (1) weight reduction and hence savings in fuel consumption, (2) distribution of material thickness and properties resulting in part consolidation which results in cost reduction and better quality, stiffness and tolerances, (3) greater flexibility in component design, (4) re-usage of scrap materials to have new stamped products and, (5) improved corrosion resistance and product quality1. The forming behaviour of TWBs is affected by weld conditions viz., weld properties, weld orientation, weld location, thickness difference and strength difference between the sheets (Bhagwan, Kridli, & Friedman, 2003; Chan, Chan, & Lee, 2003). The weld region in a TWB causes serious concerns in formability because of material discontinuity and additional inhomogeneous property distribution. Above said TWB parameters affect the forming behaviour in a synergistic manner and hence it is difficult to design the TWB conditions that can deliver a good stamped product with similar formability as that of un-welded blank. Designers will be greatly benefited if an expert system is available that can deliver forming behaviour of TWB for varied weld and blank conditions. Artificial neural network (ANN) modelling technique is found to show better prediction of any response variable that is influenced by large number of input parameters. Artificial Neural Networks are relatively crude electronic models based on the neural structure of the brain. The building blocks of the neural networks is the neuron, which are highly interconnected. In the artificial neural networks, the neurons are arranged in layers: an input layer, an output layer, and several hidden layers. The nodes of the input layer receive information as input patterns, and then transform the information through the links to other connected nodes layer by layer to the output nodes. The transformation behavior of the network depends on the structure of the

/pdf/prediction-of-tensile-and-deep-drawing-behaviour-of-2l8bc04er5.pdf

Prediction of Tensile and Deep Drawing Behaviour of Aluminium Tailor-Welded Blanks

Several examples of successful expert systems applications are presented. Examples of expert systems as applied in process planning, operations planning, inventory control, process design, quality control and scheduling are covered, and the performance of these expert systems is described. Expert systems will become an essential part of computer‐integrated manufacturing (CIM) and flexible manufacturing systems (FMS) because they can perform several of the tasks mentioned above.

Expert Systems: Implications for Operations Management

This paper aims to forecast the evolution of expert systems over the coming 5–10 years. To do so, the authors introduce a conceptual model that identifies four major areas that contribute to expert systems. The paper discusses advances in each of these four areas and relates these to changes in expert systems technology. The paper then ties quantitative trends with these qualitative advances to anticipate what expert systems will be like in the 1990s.

Expert systems: Present and future

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Expert systems: Present and future