Optimization of Multi-period Three-echelon Citrus Supply Chain Problem

Subject Areas : Executive Management

Navid Sahebjamnia ¹ , Fariba Goodarzian ² , Mostafa Hajiaghaei-Keshteli ³

1 - Department of Indusrtial Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran.
2 - Department of Indusrtial Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran.
3 - Department of Indusrtial Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran.

Received: 2017-02-07 Accepted : 2017-11-02 Published : 2020-06-01

Keywords: Citrus supply chain network design, Location- allocation problem, Multi-objective meta-heuristic algorithms,

Abstract :

In this paper, a new multi-objective integer non-linear programming model is developed for designing citrus three-echelon supply chain network. Short harvest period, product specifications, high perished rate, and special storing and distributing conditions make the modeling of citrus supply chain more complicated than other ones. The proposed model aims to minimize network costs including waste cost, transportation cost, and inventory holding cost, and to maximize network’s profits. To solve the model, firstly the model is converted to a linear programming model. Then three multi-objective meta-heuristic algorithms are used including MOPSO, MOICA, and NSGA-II for finding efficient solutions. The strengths and weaknesses of MOPSO, MOICA, and NSGA-II for solving the proposed model are discussed. The results of the algorithms have been compared by several criteria consisting of number of Pareto solution, maximum spread, mean ideal distance, and diversification metric.Computational results show that MOPSO algorithm finds competitive solutions in compare with NSGA-II and MOICA.

References:

Afshar-Nadjafi, B. and A. Razmi-Farooji, A Comparison of NSGA II and MOSA for Solving Multi-depots Time-dependent Vehicle Routing Problem with Heterogeneous Fleet. Journal of Optimization in Industrial Engineering, 2014. 7(16): p. 65-73.

Afshar-Nadjafi, B. and A. Razmi-Farooji, Using NSGA II and MOSA for solving multi-depots time-dependent vehicle routing problem with heterogeneous fleet. Journal of Optimization in Industrial Engineering, 2016.

Ahumada, O. and J.R. Villalobos, Operational model for planning the harvest and distribution of perishable agricultural products. International Journal of Production Economics, 2011. 133(2): p. 677-687.

Alaghebandha, M., S.H.R. Pasandideh, and V. Hajipour, A Continuous Review inventory Control Model within Batch Arrival Queuing Framework: A Parameter-Tuned Imperialist Competitive Algorithm. Journal of Optimization in Industrial Engineering, 2012. 5(11): p. 11-24.

Atashpaz-Gargari, E. and C. Lucas. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. in Evolutionary computation, 2007. CEC 2007. IEEE Congress on. 2007: IEEE.

Bortolini, M., et al., Fresh food sustainable distribution: cost, delivery time and carbon footprint three-objective optimization. Journal of Food Engineering, 2016. 174: p. 56-67.

Bozorgi-Amiri, A., et al., A modified particle swarm optimization for disaster relief logistics under uncertain environment. The International Journal of Advanced Manufacturing Technology, 2012. 60(1): p. 357-371.

Christopher, M., Logistics & supply chain management. 2016: Pearson UK.

Deb, K., et al., A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 2002. 6(2): p. 182-197.

Derwik, P. and D. Hellström, Competence in supply chain management: A systematic review. Supply Chain Management: An International Journal, 2017. 22(2).

Eberchart, R. and J. Kennedy. Particle swarm optimization. in IEEE International Conference on Neural Networks, Perth, Australia. 1995.

Eberhart, R. and J. Kennedy. A new optimizer using particle swarm theory. in Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on. 1995: IEEE.

Fattahi, P. and P. Samouei, A Multi-Objective Particle Swarm Optimization for Mixed-Model Assembly Line Balancing with Different Skilled Workers. Journal of Optimization in Industrial Engineering, 2016. 9(20): p. 9-18.

Ghodratnama, A., F. Jolai, and R. Tavakkoli-Moghaddam, Solving a new multi-objective multi-route flexible flow line problem by multi-objective particle swarm optimization and NSGA-II. Journal of Manufacturing Systems, 2015. 36: p. 189-202.

González-Araya, M.C., W.E. Soto-Silva, and L.G.A. Espejo, Harvest Planning in Apple Orchards Using an Optimization Model, in Handbook of Operations Research in Agriculture and the Agri-Food Industry. 2015, Springer. p. 79-105.

Govindan, K., et al., Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 2014. 152: p. 9-28.

Govindan, K., Sustainable consumption and production in the food supply chain: A conceptual framework. International Journal of Production Economics.

Hafezalkotob, A., K. Khalili-Damghani, and S.S. Ghashami, A Three-Echelon Multi-Objective Multi-Period Multi-Product Supply Chain Network Design Problem: A Goal Programming Approach. Journal of Optimization in Industrial Engineering, 2016. 10(21): p. 67-78.

Harris, I., C.L. Mumford, and M.M. Naim, A hybrid multi-objective approach to capacitated facility location with flexible store allocation for green logistics modeling. Transportation Research Part E: Logistics and Transportation Review, 2014. 66: p. 1-22.

Ju, M., M. Osako, and S. Harashina, Food loss rate in food supply chain using material flow analysis. Waste Management, 2017. 61: p. 443-454.

Khalili-Damghani, K., A.-R. Abtahi, and M. Tavana, A new multi-objective particle swarm optimization method for solving reliability redundancy allocation problems. Reliability Engineering & System Safety, 2013. 111: p. 58-75.

Latha Shankar, B., et al., Location and allocation decisions for multi-echelon supply chain network – A multi-objective evolutionary approach. Expert Systems with Applications, 2013. 40(2): p. 551-562.

Litchy, A.J. and M.H. Nehrir. Real-time energy management of an islanded microgrid using multi-objective Particle Swarm Optimization. in 2014 IEEE PES General Meeting | Conference & Exposition. 2014.

Lowe, T.J. and P.V. Preckel, Decision technologies for agribusiness problems: A brief review of selected literature and a call for research. Manufacturing & Service Operations Management, 2004. 6(3): p. 201-208.

Mahdavi, I., et al., A new mathematical model for integrating all incidence matrices in multi-dimensional cellular manufacturing system. Journal of Manufacturing Systems, 2012. 31(2): p. 214-223.

Manders, J.H.M., M.C.J. Caniëls, and P.W.T. Ghijsen, Exploring supply chain flexibility in a FMCG food supply chain. Journal of Purchasing and Supply Management, 2016. 22(3): p. 181-195.

Melo, M.T., S. Nickel, and F. Saldanha-Da-Gama, Facility location and supply chain management–A review. European journal of operational research, 2009. 196(2): p. 401-412.

Mirzapour Al-E-Hashem, S., H. Malekly, and M. Aryanezhad, A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. International Journal of Production Economics, 2011. 134(1): p. 28-42.

Mousavi, S.M., et al., A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network. Journal of Intelligent Manufacturing, 2017. 28(1): p. 191-206.

Nadal-Roig, E. and L.M. Plà-Aragonés, Optimal Transport Planning for the Supply to a Fruit Logistic Centre, in Handbook of Operations Research in Agriculture and the Agri-Food Industry. 2015, Springer. p. 163-177.

Naderi, B., The project portfolio selection and scheduling problem: mathematical model and algorithms. Journal of Optimization in Industrial Engineering, 2013. 6(13): p. 65-72.

Negi, S. and N. Anand, Issues and challenges in the supply chain of fruits & vegetables sector in India: A Review. International Journal of Managing Value and Supply Chains, 2015. 6(2): p. 47-62.

Pishvaee, M.S., Honey global supply chain network design using fuzzy optimization approach. Journal of Industrial and Systems Engineering, 2017. 10(3): p. 0-0.

Saeedi Mehrabad, M., A. Aazami, and A. Goli, A location-allocation model in the multi-level supply chain with multi-objective evolutionary approach. Journal of Industrial and Systems Engineering, 2017. 10(3): p. 0-0.

Sarrafha, K., A. Kazemi, and A. Alinezhad, A multi-objective evolutionary approach for integrated production-distribution planning problem in a supply chain network. Journal of Optimization in Industrial Engineering, 2014. 7(14): p. 89-102.

Soto-Silva, W.E., et al., Operational research models applied to the fresh fruit supply chain. European Journal of Operational Research, 2016. 251(2): p. 345-355.

Torabi, S.A., et al., A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem. Applied Soft Computing, 2013. 13(12): p. 4750-4762.

Trisna, T., et al., Multi-objective optimization for supply chain management problem: A literature review. Decision Science Letters, 2016. 5(2): p. 283-316.