|تعداد مشاهده مقاله||106,222,107|
|تعداد دریافت فایل اصل مقاله||83,125,203|
Designing a Multi-Level Blood Supply Chain Network with the Likelihood of Shortage and Perishability in the Inventory
|Advances in Industrial Engineering|
|دوره 54، شماره 2، تیر 2020، صفحه 185-204 اصل مقاله (839.42 K)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22059/jieng.2021.323832.1764|
|Majid Alimohammadi Ardekani* 1؛ Mehdi Kabiri Naeini2|
|1Department of Industrial Engineering, Faculty of Engineering, Ardakan University, Ardakan, Iran|
|2Department of Industrial Engineering, Faculty of Engineering, Payame Noor University, Yazd Center, Yazd, Iran|
|Blood is a vital substance for human life. A blood unit goes through various stages from its donation by the donor until its reception by the person in need of blood. This process can be explored the context of supply chain management. For this purpose, a mathematical model is developed in this study to design a blood supply chain network. The noticeable feature of this network is the inclusion of the shortage and perishability of blood products as two important indicators. The mathematical model proposed in this regard has the two objective functions of minimizing the blood supply chain costs and, at the same time, maximizing the average amount of blood sent from blood centers to hospitals. The model examines the problem in the case of a single product. The modified weighted Chebyshev, the improved version of ε-constraint (AUGEMCON2), and unscaled goal programming are used to solve the mathematical model. Then, to evaluate and compare the proposed solution methods and select the best one, the statistical hypothesis test and the VIKOR technique are used respectively. The results show that the model proposed for the blood supply chain is efficient and acceptable; hence, it can be of benefit in different types of blood supply chains where the shortage and perishability of blood products are taken into account.|
|Blood supply chain management؛ Multi-objective decision-making؛ VIKOR technique؛ Exact solution methods؛ Shortage and perishability of blood products|
 Abdolazimi, O., & Abraham, A. (2020). Designing a multi-objective supply chain model for the oil indus-try in conditions of uncertainty and solving it by meta-heuristic algorithms.
 Abdolazimi, O., Esfandarani, M. S., & Shishebori, D. (2020a). Design of a supply chain network for determining the optimal number of items at the inventory groups based on ABC analysis: a comparison of exact and meta-heuristic methods. Neural Computing and Applications, 1-16.
 Abdolazimi, O., Esfandarani, M. S., Salehi, M., & Shishebori, D. (2020b). Robust design of a multi-objective closed-loop supply chain by integrating on-time delivery, cost, and environmental aspects, case study of a Tire Factory. Journal of Cleaner Production, 121566.
 Abdolazimi, O., Esfandarani, M. S., & Abraham, A. (2020d). Design of a Closed Supply Chain with regards to the Social and Environmental Impacts under Uncertainty.
 Abdolazimi, O., Salehi Esfandarani, M., Salehi, M., & Shishebori, D. (2020c). A Comparison of Solution Methods for the Multi-Objective Closed Loop Supply Chains. Advances in Industrial Engineering, 54(1), 75-98.
 Alfonso, E., Xie, X., Augusto, V., & Garraud, O. (2012). Modeling and simulation of blood collection systems. Health care management science, 15(1), 63-78.
 American Red Cross, (2021). Blood Components. https://www.redcrossblood.org/donate-blood/how-to-donate/types-of-blood-donations/blood-components.html. Accessed February 1, 2021.
 Aouni, B., Colapinto, C., & La Torre, D. (2014). Financial portfolio management through the goal programming model: Current state-of-the-art. European Journal of Operational Research, 234(2), 536-545.
 Bhattacharjee, S., & Ramesh, R. (2000). A multi-period profit maximizing model for retail supply chain management: An integration of demand and supply-side mechanisms. European journal of operational research, 122(3), 584-601.
 Charnes, A., Cooper, W. W., & Ferguson, R. O. (1955). Optimal estimation of executive compensation by linear programming. Management science, 1(2), 138-151.
 Dutta, P., & Nagurney, A. (2019). Multitiered blood supply chain network competition: Linking blood service organizations, hospitals, and payers. Operations Research for Health Care, 23, 100230.
 Fahimnia, B., Jabbarzadeh, A., Ghavamifar, A., & Bell, M. (2017). Supply chain design for efficient and effective blood supply in disasters. International Journal of Production Economics, 183, 700-709.
 Farahani, R. Z., Rezapour, S., Drezner, T., & Fallah, S. (2014). Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega, 45, 92-118.
 Ghare, P. M. (1963). A model for an exponentially decaying inventory. J. ind. Engng, 14, 238-243.
 Haghjoo, N., Tavakkoli-Moghaddam, R., Shahmoradi-Moghadam, H., & Rahimi, Y. (2020). Reliable blood supply chain network design with facility disruption: A real-world application. Engineering Applications of Artificial Intelligence, 90, 103493.
 Haijema, R., Van Der Wal, J., & Van Dijk, N. M. (2007). Blood platelet production: Optimization by dynamic programming and simulation. Computers & Operations Research, 34(3), 760-779.
 Hamdan, B., & Diabat, A. (2020). Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation. Transportation Research Part E: Logistics and Transportation Review, 134, 101764.
 Hemmelmayr, V., Doerner, K. F., Hartl, R. F., & Savelsbergh, M. W. (2010). Vendor managed inventory for environments with stochastic product usage. European Journal of Operational Research, 202(3), 686-695.
 Hosseinifard, Z., & Abbasi, B. (2018). The inventory centralization impacts on sustainability of the blood supply chain. Computers & Operations Research, 89, 206-212.
 Hosseini-Motlagh, S. M., Samani, M. R. G., & Cheraghi, S. (2020). Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-Economic Planning Sciences, 70, 100725.
 Hwang, C. L., & Masud, A. S. M. (2012). Multiple objective decision making—methods and applications: a state-of-the-art survey (Vol. 164). Springer Science & Business Media.
 Kaliszewski, I. (1987). A modified weighted Tchebycheff metric for multiple objective programming. Computers & operations research, 14(4), 315-323.
 Karimi-Nasab, M., Shishebori, D., & Jalali-Naini, S. G. R. (2013). Multi-objective optimisation for pricing and distribution in a supply chain with stochastic demands. International Journal of Industrial and Systems Engineering, 13(1), 56-72.
 Khakestari, M., & Abdolazimi, O. (2020). Determine the optimal number of item groups in the werehouse based on ABC analysis within the framework of a supply chain network. Industrial Management Studies, 18(57), 307-344.
 Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465.
 Mavrotas, G., & Florios, K. (2013). An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems. Applied Mathematics and Computation, 219(18), 9652-9669.
 Mehrjerdi, Y. Z., & Shafiee, M. (2021). A resilient and sustainable closed-loop supply chain using multiple sourcing and information sharing strategies. Journal of Cleaner Production, 289, 125141.
 Montgomery, D. C. (2017). Design and analysis of experiments. John wiley & sons.
 Nahmias, S. (1982). Perishable inventory theory: A review. Operations research, 30(4), 680-708.
 Niakan, F., & Rahimi, M. (2015). A multi-objective healthcare inventory routing problem; a fuzzy possibilistic approach. Transportation Research Part E: Logistics and Transportation Review, 80, 74-94.
 Nurjanni, K. P., & Carvalho, M. S. (2016). Author’ s Accepted Manuscript. Intern. Journal of Production Economics.
 Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with outranking methods. European journal of operational research, 178(2), 514-529.
 Pirabán, A., Guerrero, W. J., & Labadie, N. (2019). Survey on blood supply chain management: Models and methods. Computers & Operations Research, 112, 104756.
 Puranam, K., Novak, D. C., Lucas, M. T., & Fung, M. (2017). Managing blood inventory with multiple independent sources of supply. European Journal of Operational Research, 259(2), 500-511.
 Sarker, B. R., Jamal, A. M. M., & Wang, S. (2000). Supply chain models for perishable products under inflation and permissible delay in payment. Computers & Operations Research, 27(1), 59-75.
 Shishebori, D., & Jabalameli, M. S. (2013). A new integrated mathematical model for optimizing facility location and network design policies with facility disruptions. Life Sci J, 10(1), 1896-1906.
 Shishebori, D., & Ghaderi, A. (2015). An integrated approach for reliable facility location/network design problem with link disruption. International Journal of Supply and Operations Management, 2(1), 640-661.
 Shishebori, D., Yousefi Babadi, A., & Noormohammadzadeh, Z. (2018). A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem. Scientia Iranica, 25(3), 1750-1767.
 Subulan, K., Taşan, A. S., & Baykasoğlu, A. (2015). A fuzzy goal programming model to strategic planning problem of a lead/acid battery closed-loop supply chain. Journal of Manufacturing Systems, 37, 243-264.
 Teimoury, E., Nedaei, H., Ansari, S., & Sabbaghi, M. (2013). A multi-objective analysis for import quota policy making in a perishable fruit and vegetable supply chain: A system dynamics approach. Computers and electronics in agriculture, 93, 37-45.
 Thangam, A., & Uthayakumar, R. (2009). Two-echelon trade credit financing for perishable items in a supply chain when demand depends on both selling price and credit period. Computers & Industrial Engineering, 57(3), 773-786.
 Van Zyl, G. J. J. (1964). Inventory Control for Perishable Commodities, Unpublished Ph. D. Dissertation, University of North Carolina, Chapel Hill, NC.
 Wang, W., Fung, R. Y., & Chai, Y. (2004). Approach of just-in-time distribution requirements planning for supply chain management. International journal of production economics, 91(2), 101-107.
 Whitin, T. M. (1957). Theory of inventory management. Princeton University Press.
 Zografidou, E., Petridis, K., Petridis, N. E., & Arabatzis, G. (2017). A financial approach to renewable energy production in Greece using goal programming. Renewable energy, 108, 37-51.
تعداد مشاهده مقاله: 342
تعداد دریافت فایل اصل مقاله: 297