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Tytuł artykułu

Design of a Supply Chain-Based Production and Distribution System Based on Multi-Stage Stochastic Programming

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Supply chains are one of the key tools in optimizing production and distribution simultaneously. However, information uncertainty is always a challenge in production and distribution management. The main purpose of this paper is to design a two-echelon supply chain in a multi-cycle state and in conditions of demand uncertainty. The task includes determining the number and location of distribution centers, planning capacity for active distribution centers, and determining the amount of shipments between different levels so that the total costs of the chain are minimized. Uncertainty is applied through discrete scenarios in the model and the problem is formulated by multi-stage stochastic programming method in the form of a mixed integer linear model. The results acquired using two indicators called VMS and VSS demonstrated that modeling the supply chain design problem with the multi-stage stochastic approach can result in significant costs reduction. Plus, utilizing mathematical expectation can generate misleading results, therefore resulting in the development of supply chain designs incapable of satisfying demand due to its overlooked limitations.
Rocznik
Strony
357--370
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
  • SKM, MARS, Public Health Department, Faculty of Health Science, University of Pembangunan Nasional Veteran Jakarta, Indonesia
  • Computer Engineering Department, Al-Rafidain University College, Baghdad, Iraq
  • Departamento de Energía, Universidad de la Costa, Barranquilla, Colombia
  • School of Accounting, Jiujiang University, 551 Qianjindonglu, Jiujiang, Jiangxi, China
  • Faculty of Business Administration, Kasetsart University, Thailand
  • National Research Ogarev Mordovia State University, 68, Bolshevitskaya str., 430005, Republic of Mordovia, Saransk, Russia
  • Dentistry Department, Kut University College, Kut, Wasit, Iraq College of technical engineering, The Islamic University, Najaf, Iraq
  • Al-Nisour University College, Baghdad, Iraq
autor
  • Department of Pharmacology, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, India
Bibliografia
  • [1] Albareda-Sambola M., Fernández E., Hinojosa Y., Puerto J., The multi-period incremental service facility location problem, Computers & Operations Research, 36, 5, 2009, 1356-1375.
  • [2] Alumur S. A., Nickel S., Saldanha-da-Gama F., Verter V., Multi-period reverse logistics network design, European Journal of Operational Research, 220, 1, 2012, 67-78.
  • [3] Ambrosino D., Scutella M. G., Distribution network design: New problems and related models, European journal of operational research, 165, 3, 2005, 610-624.
  • [4] Cardoso S. R., Barbosa-Póvoa A. P. F., Relvas S., Design and planning of supply chains with integration of reverse logistics activities under demand uncertainty, European journal of operational research, 226, 3, 2013, 436-451.
  • [5] El-Sayed M., Afia N., El-Kharbotly A., A stochastic model for forward-reverse logistics network design under risk, Computers & Industrial Engineering, 58, 3, 2010, 423-431.
  • [6] Erlenkotter D., A comparative study of approaches to dynamic location problems, European Journal of Operational Research, 6, 2, 1981, 133-143.
  • [7] Gebennini E., Gamberini R., Manzini R., An integrated production-distribution model for the dynamic location and allocation problem with safety stock optimization, International Journal of Production Economics, 122, 1, 2009, 286-304.
  • [8] Goli A., Malmir B., A covering tour approach for disaster relief locating and routing with fuzzy demand, International Journal of Intelligent Transportation Systems Research, 18, 1, 2020, 140-152.
  • [9] Hinojosa Y., Kalcsics J., Nickel S., Puerto J., Velten S., Dynamic supply chain design with inventory, Computers & operations research, 35, 2, 2008, 373-391.
  • [10] Huang K., Ahmed S., The value of multi-stage stochastic programming in capacity planning under uncertainty, Stochastic Programming E-Print Series, 15, 2005, 23-44.
  • [11] Mahar S., Bretthauer K. M., Venkataramanan M. A., An algorithm for solving the multiperiod online fulfillment assignment problem, Mathematical and Computer Modelling, 50, 9, 2009, 1294-1304.
  • [12] Melo M. T., Nickel S., Da Gama F. S., Dynamic multi-commodity capacitated facility location: a mathematical modeling framework for strategic supply chain planning, Computers & Operations Research, 33, 1, 2006, 181-208.
  • [13] Nickel S., Saldanha-da-Gama F., Ziegler H. P., A multi-stage stochastic supply network design problem with financial decisions and risk management, Omega, 40, 5, 2012, 511-524.
  • [14] Pahlevan S. M., Hosseini S. M. S., Goli A., Sustainable supply chain network design using products’ life cycle in the aluminum industry, Environmental Science and Pollution Research, 2021, 1-25.
  • [15] Paksoy T., Chang C. T., Revised multi-choice goal programming for multi-period, multi-stage inventory controlled supply chain model with popup stores in Guerrilla marketing, Applied Mathematical Modelling, 34, 11, 2010, 3586-3598.
  • [16] Pimentel B. S., Mateus G. R., Almeida F. A., Stochastic capacity planning and dynamic network design, International Journal of Production Economics, 145, 1, 2013, 139-149.
  • [17] Sha Y., Huang J., The multi-period location-allocation problem of engineering emergency blood supply systems, Systems Engineering Procedia, 5, 2012, 21-28.
  • [18] Vila D., Martel A., Beauregard R., Designing logistics networks in divergent process industries: a methodology and its application to the lumber industry, International journal of production economics, 102, 2, 2006, 358-378.
  • [19] Nguyen T. K. L., Nguyen X. H., Pham H. V., An application of the negative malmquist model for vietnamese garment and textiles industry, Management Systems in Production Engineering, 30, 1, 2022, 74-79.
  • [20] Nozari H., Ghahremani-Nahr J., Szmelter-Jarosz A., A multi-stage stochastic inventory management model for transport companies including several different transport modes, International Journal of Management Science and Engineering Management, 18, 2, 2023, 134-144.
  • [21] Goldbeck N., Angeloudis P., Ochieng W., Optimal supply chain resilience with consideration of failure propagation and repair logistics, Transportation Research Part E: Logistics and Transportation Review, 133, 2020, 101830.
  • [22] Khalilabadi S. M. G., Zegordi S. H., Nikbakhsh E., A multi-stage stochastic programming approach for supply chain risk mitigation via product substitution, Computers & Industrial Engineering, 149, 2020, 106786.
  • [23] Torkaman S., Ghomi S. F., Karimi B., Hybrid simulated annealing and genetic approach for solving a multi-stage production planning with sequence-dependent setups in a closed-loop supply chain, Applied Soft Computing, 71, 2018, 1085-1104.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2358e308-56d2-408d-82ab-47da5b28f1e6
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