Analysis of priority decision rules using MCDM approach for a dual- resource constrained flexible job shop scheduling by simulation method

Abstract

In the Dual-Resource Constrained Flexible Job Shop Problem (DRCFJSP), two different types of resources, such as workers and machines, are required to process each job's operations. The analysis and assessment of priority decision rules for scheduling production jobs in DRCFJSPs is the primary goal of this thesis research. The evaluation criteria in this research consists of demand, due date, cycle time, number of operations, and setup time. Then, a set of priority rules for development is chosen after a review of the previous literature, including both Composite Dispatching Rules (CDRs) and Multi Criteria Decision Making (MCDM)-based priority rules like Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with four different normalization procedures, Evaluation based on Distance from Average Solution (EDAS), Weighted Average Method (WAM), Preference Selection Index (PSI), and Proximity Index Value (PIV), which using the weights of criteria obtained by fuzzy Stepwise Weight Assessment Ratio Analysis (Fuzzy SWARA). Next, a Discrete Event Simulation (DES) model has been developed to assess the performance characteristics of the DRCFJSPs. Benchmark issues and real-world problems (with 114 jobs, 28 machines, and 23 people) are used to determine the best-performing prioritization rule. Based on the benchmarkes’ outcomes, no rule outperforms all of the objective measures investigated in this study. For real-world problem instance, CDRs consistently outperform MCDM-based rules for all performance measures. However, according to the average performance measure, the best performing MCDM priority rule is the proposed PSI-based rule. Overall, the suggested approach yields solid results and is easy to adapt to real-world scenarios.