RFACS

AcronymDefinition
RFACSRoyal Fine Art Commission for Scotland
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The aim of the pre-processing module is to describe all the required components of the scheduling problem model in the RFACs. These components are: parameters, decision variables, constraints and objective functions, as shown in Figure 2.
For example, RFACs generally consist of main resources and tools that are used to perform the jobs.
In this research, the decision variable is represented by the job priority, illustrating the priority status of a product to be selected for the next assembly operation in RFACs. The scheduling module section will explain how to determine the job's priority using scheduling rules.
In this research, the RFACs scheduling problem is subject to three resource constraints: tooling resource constraints, robot movement constraints and robot access constraints (Abd et al., 2011a, Abd et al., 2011b).
In this research, five objective functions, namely makespan, percentage of robots idle time, total tardiness, maximum tardiness and percentage of tardy jobs, are to be minimized, to evaluate the RFACs' performance under different scheduling policies.
In scheduling RFACs, when a robot becomes free and more than one job is waiting for processing, the jobs will be scheduled, from the highest priority to the lowest priority.
In order to simulate RFACs, three customer orders are assumed and labelled as order #1, #2 and #3, as shown in Table 3.
Implementation of Fuzzy Approach for the Scheduling of RFACs
The job priority is the fuzzy output variable, representing the priority status of a product to be selected for the next assembly operation in the RFACs. The membership function editor is used to construct the shapes of all the input/output variables.
The discussion will focus on analysing the results and comparing the RFACs performance based on the proposed rule (FSR) and existing scheduling rules.
In order to demonstrate the effectiveness of the developed methodology presented in Part I, a hypothetical case study of RFACs was used.
The first is to integrate the Taguchi method with a fuzzy logic approach to achieve multi-objective optimisation of scheduling problems in RFACs. This can be done by developing a multiple performance characteristics index (MPCI) based on fuzzy logic approach to derive the optimal solution.