Designing a Multi-Product Supply Chain Model Despite Disruption in the Supplier With A Stable Mathematical Optimization Approach in Refinery Maintenance Industries

Subject Areas : Industrial Management

Simin Orji ¹ , Hojat Nabovati ²

1 - Department of Industrial Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran
2 - Faculty member of Islamic Azad university Saveh branch

Received: 2024-03-31 Accepted : 2024-05-31 Published : 2024-10-20

Keywords: Supply Chain, Robust Mathematical Optimization, Disturbance, Random Two-Stage Model.,

Abstract :

The sequence of work operations on production machines plays a crucial role in managing the volume of jobs in process and ensuring the timely fulfillment of customer demand. One of the most common production models is the workshop model. This research focuses on an extension of the multi-path job shop problem, specifically examining energy consumption, which is directly linked to environmental pollution. The objective is to determine the sequence of operations and assign jobs to machines in a way that minimizes the weighted sums of delivery time and energy consumption. It is important to note that while this assumption complicates the problem, it also aligns more closely with real-world production environments. Given the complexity of this issue and the time required to solve it exactly, this study emphasizes the use of meta-heuristic algorithms. We discuss the solution of the nonlinear model using two meta-heuristic algorithms: the Particle Swarm Optimization (PSO) algorithm and the Wall algorithm. To evaluate the effectiveness of these algorithms, we examined one of the 30 well-known benchmark problems. Our findings indicate that the Wall algorithm reduced the total time to complete all tasks by 18% compared to the Particle Swarm Optimization algorithm. The proposed Wall algorithm demonstrates superior capability in solving these complex problems. Moreover, in terms of CPU time, both algorithms produced satisfactory results within an acceptable timeframe.

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