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Robust design and planning for a multi-mode multi-product supply network: a dairy industry case study

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Abstract

As a salient matter of decision, supply chain design and planning has been a point of attraction for both researchers and practitioners. In real-world problems, the data based on which the decision is made are subject to uncertainty. Robust optimization is a well-known approach developed for modeling the uncertainty in such cases. In this research, a robust supply chain network design (RSCND) problem considering multiple products, multiple transportation modes, monetary value of time and uncertainty in transportation costs, demand and supply is studied. To endorse applicability of the proposed model, a case study of dairy products packaging and distribution network is studied and comprehensive analyses are provided. In addition, through using the proposed linearization technique, the model can be solved within a reasonable amount of time by utilizing conventional exact methods for small- and medium-size problems.

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Notes

  1. The LINGO code for the proposed model is available upon request to the corresponding author.

References

  • Accorsi R, Ferrari E, Gamberi M, Manzini R, Regattieri A (2016) 18—A closed-loop traceability system to improve logistics decisions in food supply chains: a case study on dairy products A2—Espiñeira, Montserrat. In: Santaclara FJ (ed) Advances in food traceability techniques and technologies. Woodhead Publishing, pp 337–351

  • Aissi H, Bazgan C, Vanderpooten D (2009) Min–max and min–max regret versions of combinatorial optimization problems: a survey. Eur J Oper Res 197(2):427–438

    Google Scholar 

  • Almansoori A, Shah N (2012) Design and operation of a stochastic hydrogen supply chain network under demand uncertainty. Int J Hydrogen Energy 37(5):3965–3977

    Google Scholar 

  • Ayağ Z, Samanlioglu F, Büyüközkan G (2012) A fuzzy QFD approach to determine supply chain management strategies in the dairy industry. J Intell Manuf 24(6):1111–1122

    Google Scholar 

  • Baghalian A, Rezapour S, Farahani RZ (2013) Innovative applications of OR: robust supply chain network design with service level against disruptions and demand uncertainties: a real-life case. Eur J Oper Res 227(1):199–215

    Google Scholar 

  • Bai X, Liu Y (2016) Robust optimization of supply chain network design in fuzzy decision system. J Intell Manuf 27(6):1131–1149

    Google Scholar 

  • Bank Loans and Economic Affairs Office (2006) Feasibility study of pasturized cream, yoghurt and milk production. Ministry of Cooperatives, Tehran

    Google Scholar 

  • Bashiri M, Badri H, Talebi J (2012) A new approach to tactical and strategic planning in production–distribution networks. Appl Math Model 36(4):1703–1717

    Google Scholar 

  • Bender T, Hennes H, Kalcsics J, Melo MT, Nickel S (2002) Location software and interface with GIS and supply chain management. In: Drezner Z, Hamacher HW (eds) Facility location: applications and theory. Springer, New York, pp 233–274

    Google Scholar 

  • Ben-Tal A, El Ghaoui L, Nemirovski A (2009) Robust optimization. Princeton University Press, Princeton

    Google Scholar 

  • Bertsimas D, Brown DB, Caramanis C (2011) Theory and applications of robust optimization. SIAM Rev 53(3):464–501

    Google Scholar 

  • Beyer H-G, Sendhoff B (2007) Robust optimization—a comprehensive survey. Comput Methods Appl Mech Eng 196(33):3190–3218

    Google Scholar 

  • Bidhandi HM, Yusuff RM (2011) Integrated supply chain planning under uncertainty using an improved stochastic approach. Appl Math Model 35(6):2618–2630

    Google Scholar 

  • Bilgen B, Çelebi Y (2013) Integrated production scheduling and distribution planning in dairy supply chain by hybrid modelling. Ann Oper Res 211(1):55–82

    Google Scholar 

  • Boukherroub T, Ruiz A, Guinet A, Fondrevelle J (2015) An integrated approach for sustainable supply chain planning. Comput Oper Res 54:180–194

    Google Scholar 

  • Cardona-Valdés Y, Álvarez A, Ozdemir D (2011) A bi-objective supply chain design problem with uncertainty. Transp Res Part C 19(5):821–832

    Google Scholar 

  • Cardoso SR, Barbosa-Póvoa APFD, Relvas S (2013) Design and planning of supply chains with integration of reverse logistics activities under demand uncertainty. Eur J Oper Res 226(3):436–451

    Google Scholar 

  • Chen C, Fan Y (2012) Bioethanol supply chain system planning under supply and demand uncertainties. Transp Res Part E 48(1):150–164

    Google Scholar 

  • Choy KL, Kumar N, Chan FTS (2007) Decision-making approach for the distribution centre location problem in a supply chain network using the fuzzy-based hierarchical concept. Proc Inst Mech Eng Part B J Eng Manuf 221(4):725–739

    Google Scholar 

  • Chrwan-Jyh HO (1989) Evaluating the impact of operating environments on MRP system nervousness. Int J Prod Res 27(7):1115–1135

    Google Scholar 

  • Dal-Mas M, Giarola S, Zamboni A, Bezzo F (2011) Strategic design and investment capacity planning of the ethanol supply chain under price uncertainty. Biomass Bioenergy 35(5):2059–2071

    Google Scholar 

  • De Rosa V, Gebhard M, Hartmann E, Wollenweber J (2013) Robust sustainable bi-directional logistics network design under uncertainty. Int J Prod Econ

  • Delgado C, Rosegrant M, Steinfeld H, Ehui S (1999) Livestock to 2020, the next food revolution. IFPRI, FAO, ILRI, discussion paper 28

  • Doganis P, Sarimveis H (2007) Optimal scheduling in a yogurt production line based on mixed integer linear programming. J Food Eng 80(2):445–453

    Google Scholar 

  • Döyen A, Aras N, Barbarosoglu G (2012) A two-echelon stochastic facility location model for humanitarian relief logistics. Optim Lett 6(6):1–23

    Google Scholar 

  • Erenguc SS, Simpson NC, Vakharia AJ (2006) Integrated production/distribution planning in supply chains: an invited review. Eur J Oper Res 115(2):219–236

    Google Scholar 

  • Feng P, Rakesh N (2010) Robust supply chain design under uncertain demand in agile manufacturing. Comput Oper Res 37(4):668–683

    Google Scholar 

  • Francesconi GN, Heerink N, D’Haese M (2010) Evolution and challenges of dairy supply chains: evidence from supermarkets, industries and consumers in Ethiopia. Food Policy 35(1):60–68

    Google Scholar 

  • Georgiadis MC, Tsiakis P, Longinidis P, Sofioglou MK (2011) Optimal design of supply chain networks under uncertain transient demand variations. Omega 39(3):254–272

    Google Scholar 

  • Gitashenasi (2009) General map of Iran. Gitashenasi Geographical & Cartographic Institute, Tehran

    Google Scholar 

  • Habibzadeh Boukani F, Farhang Moghaddam B, Pishvaee MS (2016) Robust optimization approach to capacitated single and multiple allocation hub location problems. Comput Appl Math 35(1):45–60

    Google Scholar 

  • Hasani A, Khosrojerdi A (2016) Robust global supply chain network design under disruption and uncertainty considering resilience strategies: a parallel memetic algorithm for a real-life case study. Transp Res Part E Logist Transp Rev 87:20–52

    Google Scholar 

  • Hasani A, Zegordi SH, Nikbakhsh E (2015) Robust closed-loop global supply chain network design under uncertainty: the case of the medical device industry. Int J Prod Res 53(5):1596–1624

    Google Scholar 

  • Hassannayebi E, Zegordi SH, Amin-Naseri MR, Yaghini M (2017) Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach. Oper Res Int J 17(2):435–477

    Google Scholar 

  • Hsu C-I, Li H-C (2011) Reliability evaluation and adjustment of supply chain network design with demand fluctuations. Int J Prod Econ 132(1):131–145

    Google Scholar 

  • Iran Statistics Center (2006) Industrial milk farms data gathering plan of the country. Iran Statistics Center, Tehran

    Google Scholar 

  • Iran Statistics Center (2012a) Investment in industrial workshops based on patents issued by the Industries and Mines Organization. Iran Statistics Center

  • Iran Statistics Center (2012b) Resident and non-resident household population by cities: Persian date Aban 1385. Iran Statistics Center

  • Iran Statistics Center (2012c) Resident and non-resident household population by cities: Persian date Aban 1390. Iran Statistics Center

  • Iran Statistics Center (2012d) Table of milk production per animal type, 1989–2011. Iran Statistics Center

  • Jabbarzadeh A, Fahimnia B, Seuring S (2014) Dynamic supply chain network design for the supply of blood in disasters: a robust model with real world application. Transp Res Part E Logist Transp Rev 70:225–244

    Google Scholar 

  • Jouzdani J, Fathian M (2012) A robust mathematical model for route planning of a third-party logistics provider. Proc. Comput Ind Eng 42, Cape Town International Convention Centre (CTICC)

  • Jouzdani J, Fathian M (2014) A linear MmTSP formulation of robust location-routing problem: a dairy products supply chain case study. Int J Appl Decis Sci 7(3):327–342

    Google Scholar 

  • Jouzdani J, Fathian M (2016) Hybrid electromagnetism-like algorithm for dynamic supply chain network design under traffic congestion and uncertainty. Math Prob Eng

  • Jouzdani J, Sadjadi SJ, Fathian M (2013) Dynamic dairy facility location and supply chain planning under traffic congestion and demand uncertainty: a case study of Tehran. Appl Math Model In Press

  • Keyvanshokooh E, Ryan SM, Kabir E (2016) Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition. Eur J Oper Res 249(1):76–92

    Google Scholar 

  • Kim J, Realff MJ, Lee JH (2011) Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty. Comput Chem Eng 35(9):1738–1751

    Google Scholar 

  • Kouvelis P, Yu G (1997) Robust discrete optimization and its applications. Springer, Dordrecht

    Google Scholar 

  • Le TPN, Lee T (2011) Model selection with considering the CO2 emission alone the global supply chain. J Intell Manuf. https://doi.org/10.1007/s10845-10011-10613-10846

    Article  Google Scholar 

  • Le T, Lee T-R (2013) Model selection with considering the CO2 emission alone the global supply chain. J Intell Manuf 24(4):653–672

    Google Scholar 

  • Lee C-T, Chiu H-N, Yeh RH, Huang D-K (2012) Application of a fuzzy multilevel multiobjective production planning model in a network product manufacturing supply chain. Proc Inst Mech Eng Part B J Eng Manuf 226(12):2064–2074

    Google Scholar 

  • Liao S, Hsieh C, Lai P (2011) An evolutionary approach for multi-objective optimization of the integrated location-inventory distribution network problem in vendor-managed inventory. Expert Syst Appl 38:6768–6776

    Google Scholar 

  • Longinidis P, Georgiadis MC (2011) Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertainty. Int J Prod Econ 129(2):262–276

    Google Scholar 

  • Melo MT, Nickel S, Saldanha-da-Gama F (2009) Facility location and supply chain management—a review. Eur J Oper Res 196(2):401–412

    Google Scholar 

  • Mirzapour Al-e-hashem SMJ, Malekly H, Aryanezhad MB (2011) A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. Int J Prod Econ 134(1):28–42

    Google Scholar 

  • Mohseni S, Pishvaee MS, Sahebi H (2016) Robust design and planning of microalgae biomass-to-biodiesel supply chain: a case study in Iran. Energy 111:736–755

    Google Scholar 

  • Mousazadeh M, Torabi SA, Zahiri B (2015) A robust possibilistic programming approach for pharmaceutical supply chain network design. Comput Chem Eng 82:115–128

    Google Scholar 

  • Mulvey JM, Vanderbei RJ, Zenios SA (1995) Robust optimization of large-scale systems. Oper Res 43(2):264–281

    Google Scholar 

  • Noorul Haq A, Kannan G (2006) Design of an integrated supplier selection and multi-echelon distribution inventory model in a built-to-order supply chain environment. Int J Prod Res 44(10):1963–1985

    Google Scholar 

  • OECD/FAO (2012) OECD–FAO agricultural outlook 2012–2021. OECD Publishing and FAO

  • Oliver RK, Webber MD (1982) Supply-chain management: logistics catches up with strategy. Outlook 5(1):42–47

    Google Scholar 

  • Paksoy T, Pehlivan NY, Özceylan E (2012) Application of fuzzy optimization to a supply chain network design: a case study of an edible vegetable oils manufacturer. Appl Math Model 36(3):2762–2776

    Google Scholar 

  • Park S, Lee T, Sung CS (2010) A three-level supply chain network design model with risk-pooling and lead times. Transp Res Part E 46(5):563–581

    Google Scholar 

  • Paydar MM, Babaveisi V, Safaei AS (2017) An engine oil closed-loop supply chain design considering collection risk. Comput Chem Eng 104:38–55

    Google Scholar 

  • Pishvaee MS, Torabi SA (2010) A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Sets Syst 161(20):2668–2683

    Google Scholar 

  • Pishvaee MS, Rabbani M, Torabi SA (2011) A robust optimization approach to closed-loop supply chain network design under uncertainty. Appl Math Model 35(2):637–649

    Google Scholar 

  • Pishvaee M, Razmi J, Torabi S (2014) An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: a case study of medical needle and syringe supply chain. Transp Res Part E Logist Transp Rev 67:14–38

    Google Scholar 

  • Ramezani M, Kimiagari AM, Karimi B, Hejazi TH (2014) Closed-loop supply chain network design under a fuzzy environment. Knowl-Based Syst 59:108–120

    Google Scholar 

  • Rentizelas AA, Tatsiopoulos IP (2010) Locating a bioenergy facility using a hybrid optimization method. Int J Prod Econ 123(1):196–209

    Google Scholar 

  • Salema MIG, Barbosa-Povoa AP, Novais AQ (2010) Simultaneous design and planning of supply chains with reverse flows: a generic modelling framework. Eur J Oper Res 203(2):336–349

    Google Scholar 

  • Samadi-Dana S, Paydar MM, Jouzdani J (2017) A simulated annealing solution method for robust school bus routing. Int J Oper Res 28(3):307–326

    Google Scholar 

  • Schelhaas H (1999) The dairy industry in a changing world. Smallholder Dairying in the Tropics. ILRI, Nairobi

    Google Scholar 

  • Shabani N, Sowlati T, Ouhimmou M, Rönnqvist M (2014) Tactical supply chain planning for a forest biomass power plant under supply uncertainty. Energy 78:346–355

    Google Scholar 

  • Simchi-Levi D, Kaminsky P, Simchi-Levi E (2003) Designing and managing the supply chain: concepts, strategies, and case sudies. McGraw-Hill/Irwin, Boston

    Google Scholar 

  • Subulan K, Baykasoğlu A, Özsoydan FB, Taşan AS, Selim H (2015) A case-oriented approach to a lead/acid battery closed-loop supply chain network design under risk and uncertainty. J Manuf Syst 37:340–361

    Google Scholar 

  • Talaei M, Farhang Moghaddam B, Pishvaee MS, Bozorgi-Amiri A, Gholamnejad S (2016) A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry. J Clean Prod 113:662–673

    Google Scholar 

  • van der Vorst JG, Da Silva CA, Trienekens JH (2007) Agro-industrial supply chain management: concepts and applications. FAO

  • Wang F, Lai X, Shi N (2011) A multi-objective optimization for green supply chain network design. Decis Support Syst 51(2):262–269

    Google Scholar 

  • White JA, Agee MH, Case KE (1983) Principles of engineering economics analysis. Wiley

  • Yang G, Liu Y (2013) Designing fuzzy supply chain network problem by mean-risk optimization method. J Intell Manuf 1–12

  • Yang G-Q, Liu Y-K, Yang K (2015) Multi-objective biogeography-based optimization for supply chain network design under uncertainty. Comput Ind Eng 85:145–156

    Google Scholar 

  • You F, Grossmann IE (2010) Integrated multi-echelon supply chain design with inventories under uncertainty: MINLP models, computational strategies. AIChE J 56(2):419–440

    Google Scholar 

  • Yu C-S, Li H-L (2000) A robust optimization model for stochastic logistic problems. Int J Prod Econ 64(1):385–397

    Google Scholar 

  • Zhang Y, Jiang Y (2017) Robust optimization on sustainable biodiesel supply chain produced from waste cooking oil under price uncertainty. Waste Manag 60:329–339

    Google Scholar 

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Jouzdani, J., Fathian, M., Makui, A. et al. Robust design and planning for a multi-mode multi-product supply network: a dairy industry case study. Oper Res Int J 20, 1811–1840 (2020). https://doi.org/10.1007/s12351-018-0395-0

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