Paper_IJPE_Repository_3_Complete_Data_Generated_by_the_Simulator_of_Scenarios

Description

These data were generated using a custom-built simulator developed in Python 3.12 for three distinct scenarios. The experiments were conducted on a PC equipped with an Intel i5-1145G7 @ 2.60GHz processor and 32GB of RAM. The simulator implements the proposed RM-WSM approach and compares its performance against the traditional WSM (T-WSM) method, providing a comprehensive evaluation of both techniques. The 3 scenario are described as follows: Scenario 1 – Standard Risk Scenario: • The simulation runs for 100 epochs. • The global order (𝑄t)==> follows a range of RUD: [500, 1,020,000]. • The risk probability (𝑝𝑡)==> follows a range of RUD: [0.01 and 0.05]. • The purchase cost (𝜌𝑡)==> follows a range of RUD: [10, 15]. • The risk loss cost (𝑒𝑡)==> follows a range of RUD: [500, 800]. • The predefined threshold (𝜗𝑡) ) is 100. • A conditional risk condition is applied when Location > 51. Scenario 2 – High-Risk Frequency Scenario: • The simulation runs for 100 epochs. • The global order (𝑄t)==> follows a range of RUD: [500, 12,000], • The risk probability (𝑝𝑡)==> follows a range of RUD: [0.06 to 0.08]. • The purchase cost (𝜌𝑡)==> follows a range of RUD: [10, 15]. • The risk loss cost (𝑒𝑡)==> follows a range of RUD: [500, 800]. • The predefined threshold (𝜗𝑡) ) is 100. • A conditional risk condition is applied when Location > 51. Scenario 3 – Reduced Global Order: • The simulation runs for 100 epochs. • The global order (𝑄t)==> follows a range of RUD: [300, 800]. • The risk probability (𝑝𝑡)==> follows a range of RUD: [0.01, 0.05]. • The purchase cost (𝜌𝑡)==> follows a range of RUD: [10, 15]. • The risk loss cost (𝑒𝑡)==> follows a range of RUD: [500, 800]. • The predefined threshold (𝜗𝑡) ) is 100. • A conditional risk condition is applied when Location > 51.

Steps to reproduce

The steps to reproduce are available in the academic paper titled: FP-Growth-Based Risk Pattern Discovery for Dual Cost-Risk Mitigation in Resilient Multi-Sourcing Order Allocation under Time-Varying Demand

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