A multi-product, multi-period and multi-hub routing and scheduling model for offshore logistics

الموضوعات :

Alireza Rashidi Komijan ¹ , Mehdi Razi ² , Peyman Afzal ³ , Vahidreza Ghezavati ⁴ , Kaveh Khalili Damghani ⁵

1 - Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
2 - School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
3 - School of Mine Engineering, South Tehran Branch, Islamic Azad University,Tehran, Iran
4 - School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
5 - Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

تاريخ الإرسال : 10 الأربعاء , ربيع الثاني, 1445 تاريخ التأكيد : 25 السبت , شوال, 1445 تاريخ الإصدار : 22 الإثنين , رمضان, 1445

الکلمات المفتاحية: Routing, Scheduling, Mathematical model, Offshore logistics, Genetic Algorithm,

ملخص المقالة :

Logistics in upstream oil industry is a critical task as rigs need consistent support for ongoing production. In this paper, a multi-period, multi-product and multi-hub routing and scheduling model is presented for offshore logistics problem. As rigs can be served in specific time intervals, time windows constraints are considered in the proposed model. Despite classic VRP models, vessels are not forced to return hubs at the end of duty days. Also, a vessel may leave and return back to hubs several times during the planning horizon. Moreover, the model determines which vessels are applied in each day. In other words, a vessel may be applied in some days and be inactive in other days of planning horizon. To develop a compromise model, fueling issue is considered in the model. As a rig can be supplied by different vessels in real world cases, the proposed model is split delivery. Based on these challenges and contributions, this research deploys an integrated optimization of routing and scheduling of vessels for offshore logistics. This paper deals with a combinatorial optimization model which is NP-hard. Hence, Genetic Algorithm is applied as the solution approach. The average gap between objective functions of GAMS and GA is only 1.18 percent while saving CPU time in GA is much more than GAMS (about 78.16 percent on average). The results confirm the applicability and efficiency of the GA.

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