Coordination and Profit Optimization by Producer-Distributor System of Agricultural Products in Bangladesh
American Journal of Applied Mathematics
Volume 8, Issue 1, February 2020, Pages: 22-28
Received: Oct. 28, 2019;
Accepted: Nov. 15, 2019;
Published: Feb. 4, 2020
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Mohammad Khairul Islam, Department of Mathematics, Directorate of Secondary and Higher Education, Dhaka, Bangladesh
Mohammad Mahmud Alam, Department of Mathematics, Dhaka University of Engineering and Technology, Gazipur, Bangladesh
Mohammed Forhad Uddin, Department of Mathematics, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
Gazi Mohammad Omar Faruque, Department of Computer Science and Engineering, University of South Asia, Dhaka, Bangladesh
This study, presents three different mathematical models: Producer, Distributor and Coordination modelwhich negotiate with a Producer-Distributor system for producing and distributing ofagricultural products in Bangladesh. In this paper, we investigated supply chain network (SCN) are two distinct freelance supply organizations. SCN management has the difficulties for the disconnected and freelance economic people. Further, fast technological changes and high fight build SCN a lot of complicated. The problem of locating distribution centers (DCs) is one among the foremost necessary problems in design of SCN. Current study, SCN was modeled using a formulation in mixed integer linear programming (MILP) problem, in which the facilities are coordinated by mutually sharing information with each other between producer and wholesaler. We think, this research presents a real life coordination optimization problem. The formulated MILP model is solved by using a mathematical programming language (AMPL) and results obtained by appropriate solver MINOS.
Mohammad Khairul Islam,
Mohammad Mahmud Alam,
Mohammed Forhad Uddin,
Gazi Mohammad Omar Faruque,
Coordination and Profit Optimization by Producer-Distributor System of Agricultural Products in Bangladesh, American Journal of Applied Mathematics.
Vol. 8, No. 1,
2020, pp. 22-28.
Brandenburg, M. et al., Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233, (2014) 299–312.
Wang, G., Huang, S. H. &Dismukes, J. P., Product-driven supply chain selection usingintegrated multi-criteria decision-making methodology. International Journal ofProduction Economics, 91, (2004) 1–15.
Nickel, S., Saldanha-da-Gama, F. & Ziegler, H. P., A multi-stage stochastic supplynetwork design problem with financial decisions and risk management. Omega, 40, (2012) 511–524.
Papageorgiou, L. G., Supply chain optimisation for the process industries: Advancesand opportunities. Computers and Chemical Engineering, 33, (2009) 1931–1938.
Gupta, A. &Maranas, C. D., Managing demand uncertainty in supply chain planning. Computers & Chemical Engineering, 27, (2003) 1219–1227.
Klibi, W., Martel, a & Guitouni, a. The design of robust value-creating supply chainnetworks: A critical review. European Journal of Operational Research, 203, (2010) 283–293.
Guillén-Gosalbez, G. & Grossmann, I. E., Optimal design and planning of sustainablechemical supply chains under uncertainty. AIChE Journal, 55, (2009) 99–121.
Sundarakani, B. et al., Modeling carbon footprints across the supply chain. InInternational Journal of Production Economics, 128, (2010) 43–50.
Ramudhin, A. et al., Carbon Market Sensitive Green Supply Chain Network Design. 2008 IEEE International Conference on Industrial Engineering and EngineeringManagement, (2008) pp. 1093-1097.
Hassini, E., Surti, C. & Searcy, C., A literature review and a case study of sustainablesupply chains with a focus on metrics. International Journal of Production Economics, 140, (2012) 69–82.
Bojarski, A. D. et al., Incorporating environmental impacts and regulations in a holisticsupply chains modeling: An LCA approach. Computers and Chemical Engineering, 33,(2009) 1747–1759.
Wang, F., Lai, X. & Shi, N., A multi-objective optimization for green supply chainnetwork design. Decision Support Systems, 51, (2011) 262–269.
Akkerman, R., Farahani, P. &Grunow, M., Quality, safety and sustainability in fooddistribution: a review of quantitative operations management approaches andchallenges. OR Spectrum, 32, 863–904.
Ahumada, O. & Villalobos, J. R., 2011. A tactical model for planning the productionand distribution of fresh produce. Annals of Operations Research, 190, (2011) 339–358.
Rong, A., Akkerman, R. &Grunow, M., An optimization approaches for managing freshfood quality throughout the supply chain. International Journal of Production Economics, 131, (2011) pp. 421–429.
Aung, M. M. & Chang, Y. S., Traceability in a food supply chain: Safety and qualityperspectives. Food Control, 39, (2014) 172–184.
Van der Vorst, J., Tromp, S. O. & Zee, D.-J. Van der, Simulation modeling for foodsupply chain redesign; integrated decision making on product quality, sustainability andlogistics. International Journal of Production Research, 47, (2009) 6611–6631.
Shukla, M. &Jharkharia, S., Agri-fresh produce supply chain management: a state-ofthe-art literature review. International Journal of Operations & Production Management, 33, (2013) 114–158.
Goyal, S. K., An integrated inventory model for a Single supplier-single customer problem, International Journal of Production Research, 15 1 (1976) 107-111.
Sajadieh, M. S. and Jokar, M. R. A., Optimizing shipment, ordering and pricing policies in a two stage supply chain with price sensitive demand, Transportation Research Part E, Vol. 45, (2009) 564-571.
Drezner, Z., and Hamacher, H. (eds.), Facility Location: Applications and Theory, Springer Verlag, Berlin, 2002.
Hung, B., and Liu, N., “Bilevel programming approach to optimizing a logistic distribution network with balancing requirements”, Transportation Research Record: Journal of the Transportation Research Board, 1894 (2004) 188-197.
Jose, C. S., Haider, A. B., Rui, B., and Alexandre, S., “A multi objective approach to solve capacitated vechile routing problems with time windows using mixd integer linear programming”, International Journal of Advanced Science and Technology, 28 (2011) 1-8.