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A simulation expertsystem - sandeep.ladu - 08-16-2017 A simulation expertsystem based approach [attachment=16170] Introduction This research is interested in one of the major design issues of production systems (PSs): the resource sizing problem. It is defined as the specification of the number of each type of resources to be used in a production process for a given time period (Miller and Davis, 1977). Sizing is required while designing a new or expanding an existing system (Feyzioglu et al., 2005). The approaches that tackled this problem can be classified in two principal categories: analytical and simulation-based. Proposed approach Three main types of information are required for the application of the SESA: PS data, demand pattern and performance limits (Table I). The simulation tool uses the PS data and demand pattern to simulate the realization of a typical set of MOs over a given planning horizon. Simulation results are then considered as performance measures of the system. These results, in addition to the performance limits and relevant PS and demand pattern data constitute the ES required inputs. The ES is in charge of analyzing the PS situation. If the simulated system performance is found to be improvable, the ES recommends a modification to its resources in order to overcome the problem considered to be responsible, at the largest extent, for the low performance. Consequently, a new cycle is run until the ES becomes unable to suggest any modifications (Figure 1). Finally, it is worth noting that the approach can be started from any initial PS configuration assuring the feasibility of all MOs. Production system modeling for simulation The production of the typical MO pattern by the PS being sized was modelled for simulation using the commercial tool arena (User s Guide, 2002). The model involves three main components discussed in the following subsections (Figure 2(a)). Expert system The developed ES is an object-oriented decision-making tool. It is composed of four main parts. First, the object base is the static knowledge component. The objects are organized hierarchically into classes and sub-classes representing all problem elements such as global PS data and departments of machines (Abel and Abel, 1988). Thus, the department i object is a sub-class of the class Departments of machines which is in turn a sub-class of the Resources class. Besides, the rule base is the ES component representing the know-how . This expertise is expressed in terms of inference rules of the form: IF [condition] THEN [action] grouped in several packs, each representing one of the main functions of the ES (Figure 3) |