Free Access
Issue
RAIRO-Oper. Res.
Volume 55, Number 2, March-April 2021
Page(s) 1015 - 1042
DOI https://doi.org/10.1051/ro/2021031
Published online 06 May 2021
  • L. Alnaji and M. Ridha, The role of supply chain applications in Jordanian pharmacies: a case study on pharmacies in the capital city Amman. Ind. Eng. Lett. 3 (2013) 65–71. [Google Scholar]
  • A.C.S. Amaro and A.P.F. Barbosa-Póvoa, Planning and scheduling of industrial supply chains with reverse flows: a real pharmaceutical case study. Comput. Chem. Eng. 32 (2008) 2606–2625. [Google Scholar]
  • Z. Azadehranjbar, A. Bozorgi-Amiri and A. Zandi, Warehouse redesigning in a three-echelon supply chain network with consideration of routing under uncertainty. RAIRO:OR 55 (2021) S147–S166. [Google Scholar]
  • A. Baniamerian, M. Bashiri and R. Tavakkoli-Moghaddam, Modified variable neighborhood search and genetic algorithm for profitable heterogeneous vehicle routing problem with cross-docking. Appl. Soft Comput. 75 (2019) 441–460. [Google Scholar]
  • S.H. Chung and C. Kwon, Integrated supply chain management for perishable products: dynamics and oligopolistic competition perspectives with application to pharmaceuticals. Int. J. Prod. Econ. 179 (2016) 117–129. [Google Scholar]
  • M. Dorigo, G. Di Caro, Ant colony optimization: a new meta-heuristic. In: Vol. 2 of Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406). IEEE, 1999, pp. 1470–1477. [Google Scholar]
  • M.B. Fakhrzad and F. Goodarzian, A fuzzy multi-objective programming approach to develop a green closed-loop supply chain network design problem under uncertainty: modifications of imperialist competitive algorithm. RAIRO: OR 53 (2019) 963–990. [Google Scholar]
  • M.B. Fakhrzad and F. Goodarzian, A new multi-objective mathematical model for a Citrus supply chain network design: metaheuristic algorithms. J. Optim. Ind. Eng. 14 (2020) 127–144. [Google Scholar]
  • M.B. Fakhrzad, P. Talebzadeh and F. Goodarzian, Mathematical formulation and solving of green closed-loop supply chain planning problem with production, distribution and transportation reliability. Int. J. Eng. 31 (2018) 2059–2067. [Google Scholar]
  • M.B. Fakhrzad, F. Goodarzian and A.M. Golmohammadi, Addressing a fixed charge transportation problem with multi-route and different capacities by novel hybrid meta-heuristics. J. Ind. Syst. Eng. 12 (2019) 167–184. [Google Scholar]
  • A.M. Fathollahi-Fard, A. Ahmadi, F. Goodarzian and N. Cheikhrouhou, A bi-objective home healthcare routing and scheduling problem considering patients’ satisfaction in a fuzzy environment. Appl. Soft Comput. 93 (2020) 106385. [PubMed] [Google Scholar]
  • M. Fazli-Khalaf, S.K. Chaharsooghi and M.S. Pishvaee, A new robust possibilistic programming model for reliable supply chain network design: a case study of lead-acid battery supply chain. RAIRO: OR 53 (2019) 1489–1512. [Google Scholar]
  • M. Forozandeh, E. Teimoury and A. Makui, A mathematical formulation of time-cost and reliability optimization for supply chain management in research-development projects. RAIRO: OR 53 (2019) 1385–1406. [Google Scholar]
  • G. Gatica, L.G. Papageorgiou and N. Shah, Capacity planning under uncertainty for the pharmaceutical industry. Chem. Eng. Res. Design 81 (2003) 665–678. [Google Scholar]
  • F. Goodarzian and H. Hosseini-Nasab, Applying a fuzzy multi-objective model for a production–distribution network design problem by using a novel self-adoptive evolutionary algorithm. Int. J. Syst. Sci.: Oper. Logistics 8 (2019) 1–22. [Google Scholar]
  • F. Goodarzian, H. Hosseini-Nasab, J. Muñuzuri and M.B. Fakhrzad, A multi-objective pharmaceutical supply chain network based on a robust fuzzy model: a comparison of meta-heuristics. Appl. Soft Comput. 92 (2020) 106331. [Google Scholar]
  • F. Goodarzian, H. Hosseini-Nasab and M.B. Fakhrzad, A multi-objective sustainable medicine supply chain network design using a novel hybrid multi-objective metaheuristic algorithm. Int. J. Eng. 33 (2020) 1986–1995. [Google Scholar]
  • F. Goodarzian, A. Abraham and A.M. Fathollahi-Fard, A biobjective home health care logistics considering the working time and route balancing: a self-adaptive social engineering optimizer. J. Comput. Design Eng. 8 (2021) 452–474. [Google Scholar]
  • F. Goodarzian, A.A. Taleizadeh, P. Ghasemi and A. Abraham, An integrated sustainable medical supply chain network during COVID-19. Eng. App. Artif. Intell. 100 (2021) . [Google Scholar]
  • P. Hansen and N. Mladenović, Variable neighborhood search: principles and applications. Eur. J. Oper. Res. 130 (2001) 449–467. [Google Scholar]
  • S.D. Hosseini, M.A. Shirazi and B. Karimi, Cross-docking and milk run logistics in a consolidation network: a hybrid of harmony search and simulated annealing approach. J. Manuf. Syst. 33 (2014) 567–577. [Google Scholar]
  • G. Jamali, S.S. Sana and R. Moghdani, Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand. RAIRO:OR 52 (2018) 473–497. [Google Scholar]
  • G. Jetly, C.L. Rossetti and R. Handfield, A multi-agent simulation of the pharmaceutical supply chain. In: Agent-Based Modeling and Simulation. Palgrave Macmillan, London (2014) 133–154. [Google Scholar]
  • R. Jovanovic, M. Tuba and S. Voß, An efficient ant colony optimization algorithm for the blocks relocation problem. Eur. J. Oper. Res. 274 (2019) 78–90. [Google Scholar]
  • P. Karakostas, A. Sifaleras and M.C. Georgiadis, A general variable neighborhood search-based solution approach for the location-inventory-routing problem with distribution outsourcing. Comput. Chem. Eng. 126 (2019) 263–279. [Google Scholar]
  • S. Khalifehzadeh, M.B. Fakhrzad, A stochastic bi-objective mathematical model for optimizing a production and distribution system with stochastic demand and stochastic lead time, Int. J. Eng. Sci. (2008–4870) 29 (2018). [Google Scholar]
  • M. Khorbatly, H. Dkhil, H. Alabboud and A. Yassine, A hybrid algorithm Tabu Search-GRASP for wounded evacuation in disaster response. RAIRO: OR 54 (2020) 19–36. [Google Scholar]
  • S. Kirkpatrick, Optimization by simulated annealing: Quantitative studies. J. Stat. Phys. 34 (1984) 975–986. [CrossRef] [Google Scholar]
  • N. Leite, F. Melcio and A.C. Rosa, A fast simulated annealing algorithm for the examination timetabling problem. Expert Syst. App. 122 (2019) 137–151. [Google Scholar]
  • J. Luan, Z. Yao, F. Zhao and X. Song, A novel method to solve supplier selection problem: hybrid algorithm of genetic algorithm and ant colony optimization. Math. Comput. Simul. 156 (2019) 294–309. [Google Scholar]
  • H. Ma, X. Li and Y. Liu, Multi-period multi-scenario optimal design for closed-loop supply chain network of hazardous products with consideration of facility expansion. Soft Comput. 24 (2020) 2769–2780. [Google Scholar]
  • M. Mousazadeh, S.A. Torabi and B. Zahiri, A robust possibilistic programming approach for pharmaceutical supply chain network design. Comput. Chem. Eng. 82 (2015) 115–128. [Google Scholar]
  • L.G. Papageorgiou, Supply chain optimization for the process industries: advances and opportunities. Comput. Chem. Eng. 33 (2009) 1931–1938. [Google Scholar]
  • L.G. Papageorgiou, G.E. Rotstein and N. Shah, Strategic supply chain optimization for the pharmaceutical industries. Ind. Eng. Chem. Res. 40 (2001) 275–286. [Google Scholar]
  • G.E. Rotstein, L.G. Papageorgiou, N. Shah, D.C. Murphy and R. Mustafa, A product portfolio approach in the pharmaceutical industry. Comput. Chem. Eng. 23 (1999) S883–S886. [Google Scholar]
  • B. Roy and B.C. Giri, A three-echelon supply chain model with price and two-level quality dependent demand. RAIRO:OR 54 (2020) 37–52. [Google Scholar]
  • N. Sahebjamnia, F. Goodarzian and M. Hajiaghaei-Keshteli, Optimization of multi-period three-echelon citrussupply chain problem. J. Optim. Ind. Eng. 13 (2020) 39–53. [Google Scholar]
  • E. Settanni, T.S. Harrington and J.S. Srai, Pharmaceutical supply chain models: a synthesis from a systems view of operations research. Oper. Res. Perspect. 4 (2017) 74–95. [Google Scholar]
  • S.K. Singh and M. Goh, Multi-objective mixed integer programming and an application in a pharmaceutical supply chain. Int. J. Prod. Res. 57 (2019) 1214–1237. [Google Scholar]
  • R.T. Sousa, S. Liu, L.G. Papageorgiou and N. Shah, Global supply chain planning for pharmaceuticals. Chem. Eng. Res. Design 89 (2011) 2396–2409. [Google Scholar]
  • N. Susarla, I.A. Karimi, Integrated supply chain planning for multinational pharmaceutical enterprises. In: Vol. 29 of Computer Aided Chemical Engineering. Elsevier (2011) 1075–1079. [Google Scholar]
  • E. Tsakirakis, M. Marinaki, Y. Marinakis and N. Matsatsinis, A similarity hybrid harmony search algorithm for the team orienteering problem. Appl. Soft Comput. 80 (2019) 776–796. [Google Scholar]
  • D. Weraikat, M.K. Zanjani and N. Lehoux, Two-echelon pharmaceutical reverse supply chain coordination with customer’s incentives. Int. J. Prod. Econ. 176 (2016) 41–52. [Google Scholar]
  • D. Weraikat, M.K. Zanjani and N. Lehoux, Improving sustainability in a two-level pharmaceutical supply chain through Vendor-Managed Inventory system. Oper. Res. Health Care 21 (2019) 44–55. [Google Scholar]
  • D.H. Wolpert and W.G. Macready, No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1 (1997) 67–82. [CrossRef] [Google Scholar]
  • E. Yadegari, A. Alem-Tabriz and M. Zandieh, A memetic algorithm with a novel neighborhood search and modified solution representation for closed-loop supply chain network design. Comput. Ind. Eng. 128 (2019) 418–436. [Google Scholar]
  • B. Zahiri, J. Zhuang and M. Mohammadi, Toward an integrated sustainable-resilient supply chain: a pharmaceutical case study. Transp. Res. Part E: Logistics Transp. Rev. 103 (2017) 109–142. [Google Scholar]
  • H. Zarei and M. Rasti-Barzoki, Mathematical programming and three metaheuristic algorithms for a bi-objective supply chain scheduling problem. Neural Comput. App. 31 (2019) 9073–9093. [Google Scholar]

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