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and labor. To install CIM, these databases must be consolidated. Unfortunately, bringing multiple databases into one CIM DB that remains available to everyone and consistent in all levels presents a significant obstacle because of the large investment needed in time and computer hardware and software.
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Structure of Multiobjective Support Decision Systems
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The success of CIM also depends largely on the ability to incorporate sensor technology with a database. The database is utilized in making decisions on all levels decisions that are used to update to the database. Decision support systems can provide a framework for efficient database utilization by allowing storage and retrieval of information and problem solving through easy communications. Decision-making problems in manufacturing can be grouped into two general classes: Structured decisions are those that are constrained by physical or practical limitations and can be made almost automatically with the correct input. An example is generating a group technology part code given the geometry of the part. Unstructured decisions typically are those that contain a large degree of uncertainty. Decisions considered by strategic planners are almost always unstructured. Deciding whether or not to expand a certain product line, for example, may be based on demand forecasting and on the expected growth of competitors. Due to the predictive nature of these decisions, they inherently contain more uncertainty than structured decisions. Long-range planning consists primarily of unstructured information. Decision support systems mainly consist of three separate parts: Language systems. The function of a language system (LS) is to provide a means for the user to communicate with the DSS. Some considerations for the choice of a language are that the formulation should be easily understood, implementable, and modifiable. Moreover, processing the language should be possible on a separate level or on the problem processing system (PPS) level. An obvious choice for a language would be the spoken language of the user. This would require little or no training for the user to interact with a computer, but the complexity of sentences and the use of words that have multiple meanings present difficult problems that, when solved, would introduce unwanted inefficiency into the language system. An alternative would be to use a more formal language based on logic (e.g., PROLOG). The advantage here is
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that a language can be used at all levels of the DSS. In the design and use of an LS for the user interface, one can consider objectives such as ease of communication, the level of complexity that can be presented by the LS, and the time needed for the user to learn it. Knowledge systems. The basic function of a knowledge system (KS) is the representation and organization of the knowledge in the system. Two possible approaches are storing in the information in database form or representing the data as a base for artificial intelligence using methods from, for example, predicate calculus. The objective of KS is to ease accessibility of data for the DSS. The KS should be able to organize and classify databases and problem domains according to objectives that are sensible and convenient for the user. Some of the objectives in the design of the KS are to reduce the amount of computer memory required, increase the speed with which the data can be retrieved or stored, and increase the number of classifications of data and problem domains possible. Problem processing systems. The problem processing system of a DSS provides an interface between the LS and the KS. The primary function is to receive the problem from the user via the LS and use the knowledge and data from the KS to determine a solution. Once a solution is found, the PPS sends it through the KS to be translated into a form the user can recognize. More importantly, in the model formulation, analysis, and solution procedure of PPS, the conflicting objectives of stated problems must be considered. The PPS should provide methodology that can optimize all conflicting objectives and generate a compromise solution acceptable to the user. Some of the objectives in the development of such multiobjective approaches are to reduce the amount of time that the user must spend to solve the problem, increase the degree of interactiveness (e.g., how many questions the user should answer), reduce the difficulty of questions posed to the user, and increase the robustness of the underlying assumptions and procedures.
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