Integrated loading and distribution scheduling of high consumption oil products from distributed warehouses

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Islamic Azad University Science and Research Branch, Tehran, Iran

2 Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran

3 Department of Industrial Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran

4 Department of industrial engineering, Tarbiat Modares University (TMU), Tehran, Iran

Abstract

In this research, we study the scheduling of loading and distribution for high consumption oil products while distributed warehouses are assumed. The high consumption of oil products requires direct shipment of orders issued by different customers in different geographical locations. The integrated nonlinear model is proposed to minimize the total cost of the supply chain including purchasing cost, loading, transportation, and late delivery costs. Because of the NP-hard nature of the problem, we proposed a new simulation based algorithm utilizing meta-heuristic algorithms. Then, we conducted several experiments to evaluate the efficiency proposed solution approach. Moreover, we evaluated the performance of the latest versions of genetic algorithm (GA-MPC) and particles swarm optimization (SPSO 2011) as well as the traditional versions within the proposed algorithm to solve the real-world scale problems. The results illustrate the better performance of genetic algorithm for large-scale problems.

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Main Subjects

References

[1]   Bausch, D.O., Brown, G.G., Ronen, D., (1995). “Consolidating and dispatching truck shipments of Mobil heavy petroleum products”, Interfaces, 25(2): 1-17.
[2]   Behnamian, J., Ghomi, S.F., (2016). “A survey of multi-factory scheduling”, Journal of Intelligent Manufacturing, 27(1): 231-249.
[3]   Behnamian, J., Ghomi, S.F., (2013). “The heterogeneous multi-factory production network scheduling with adaptive communication policy and parallel machine”, Information Sciences, 219: 181-196.
[4]   Chwen, Z.L., (2010). “Integrated production and outbound distribution scheduling: review and extensions”, Operations Research, 58(1): 130-148.
[5]   Guo, Z., Shi, L., Chen, L., Liang, Y., (2017). “A harmony search-based memetic optimization model for integrated production and transportation scheduling in MTO manufacturing”, Omega, 66: 327-343.
[6]   Kerkhove, L.P., Vanhoucke, M., (2014). “Scheduling of unrelated parallel machines with limited server availability on multiple production locations: a case study in knitted fabrics”, International Journal of Production Research, 52(9): 2630-2653.
[7]   Sun, X.T., Chung, S.H., Chan, F.T., (2015). “Integrated scheduling of a multi-product multi-factory manufacturing system with maritime transport limits”, Transportation Research Part E: Logistics and Transportation Review, 79: 110-127.
[8]   Terrazas-Moreno, S., Grossmann, I.E. (2011). “A multiscale decomposition method for the optimal planning and scheduling of multi-site continuous multiproduct plants”, Chemical Engineering Science, 66(19): 4307-4318.
[9]   Yazdani, M., Gohari, S., Naderi, B. (2015). “Multi-factory parallel machine problems: Improved mathematical models and artificial bee colony algorithm”, Computers & Industrial Engineering, 81: 36-45.
[10] Bean, J.C., (1994). “Genetic algorithms and random keys for sequencing and optimization”, ORSA Journal on Computing, 6(2): 154-160.
[11] Tadeusz, S., (2015). “Integrated supply, production and distribution scheduling under disruption risks”, Omega.
[12] Wilkinson, S.J., Cortier, A., Shah, N., Pantelides, C.C., (1996). “Integrated production and distribution scheduling on a Europe-widebasis”, Computers & chemical Engineering, 20: 1275-1280.
[13] Elsayed, S.M., Sarker, R.A., Essam, D.L., (2014). “A new genetic algorithm for solving optimization problems”, Engineering Applications of Artificial Intelligence, 27: 57-69.
[14] Maurice, C., (2012). “Standard particle swarm optimization”, 15 pages.
[15] Tavakkoli-Moghaddam, R., Yazdani, M., Molla-Alizadeh-Zavardehi, S., (2012). “Scheduling an Integrated Production and Air Transportation in Supply Chain with Sequence-Dependent Setup Times”, Journal of Industrial engineering, 23(3): 351-362.
[16] Beheshtinai, Arabi (2017). “A Genetic Algorithm for Integration of Vehicle Routing Problem and Production Scheduling in Supply Chain (Case Study: Medical Equipment Supply Chain)”, Journal of Industrial engineering. 51(2): 147-160.
[17] Moons, S., Ramaekers, K., Caris, A., Arda, Y., (2016). “Integrating production scheduling and vehicle routing decisions at the operational decision level: a review and discussion”, Computers & Industrial Engineering.
[18] بهنامیان، جواد، (1394). "زمانبندی چند هدفه شبکه های تولید چند کارخانه‌ای با استفاده از الگوریتم ژنتیک زیر جمعیت و روش ارتجاعی"، نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید 3(6): 133-147.
[19] بهنامیان، جواد، فاطمی قمی، سیدمحمدتقی، (1392). "ارائه الگوریتم ترکیبی بر پایه بهینه سازی گروه ذرات و روش هایپرهیوریستیک برای زمانبندی کارخانه‌های توزیع‌شده با اتحاد مجازی"، نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید، 1(1): 1-11.
[20] سایت رسمی شرکت ملی پخش فرآورده‌های نفتی ایران،   https://www.niopdc.ir.
Journal of Industrial Engineering Research in Production Systems
Volume 6, Issue 12
September 2018
Pages 103-117

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