Open Access
RAIRO-Oper. Res.
Volume 57, Number 4, July-August 2023
Page(s) 1957 - 1981
Published online 24 July 2023
  • H.M. Wagner and T.M. Whitin, Dynamic version of the economic lot size model. Manage. Sci. 5 (1958) 89–96. [Google Scholar]
  • G.B. Dantzig and J.H. Ramser, The truck dispatching problem. Manage. Sci. 6 (1959) 80–91. [Google Scholar]
  • Y. Adulyasak, J.-F. Cordeau and R. Jans, The production routing problem: a review of formulations and solution algorithms. Comput. Oper. Res. 55 (2015) 141–152. [CrossRef] [MathSciNet] [Google Scholar]
  • Y. Adulyasak, J.-F. Cordeau and R. Jans, Optimization-based adaptive large neighborhood search for the production routing problem. Transp. Sci. 48 (2014) 20–45. [CrossRef] [Google Scholar]
  • P. Chandra, A dynamic distribution model with warehouse and customer replenishment requirements. J. Oper. Res. Soc. 44 (1993) 681–692. [CrossRef] [Google Scholar]
  • P. Chandra and M.L. Fisher, Coordination of production and distribution planning. Eur. J. Oper. Res. 72 (1994) 503–517. [CrossRef] [Google Scholar]
  • C. Paterson, G. Kiesmüller, R. Teunter and K. Glazebrook, Inventory models with lateral transshipments: a review. Eur. J. Oper. Res. 210 (2011) 125–136. [CrossRef] [Google Scholar]
  • Y.T. Herer, M. Tzur and E. Yücesan, Transshipments: an emerging inventory recourse to achieve supply chain leagility. Int. J. Prod. Econ. 80 (2002) 201–212. [CrossRef] [Google Scholar]
  • J.J. Coyle, C.J. Langley, R.A. Novack and B. Gibson, Supply Chain Management: A Logistics Perspective. Nelson Education (2016). [Google Scholar]
  • O. Kaya, Outsourcing vs. in-house production: a comparison of supply chain contracts with effort dependent demand. Omega 39 (2011) 168–178. [CrossRef] [Google Scholar]
  • D. Bertsimas and A. Thiele, A robust optimization approach to inventory theory. Oper. Res. 54 (2006) 150–168. [CrossRef] [MathSciNet] [Google Scholar]
  • N. Brahimi and T. Aouam, Multi-item production routing problem with backordering: a MILP approach. Int. J. Prod. Res. 54 (2016) 1076–1093. [CrossRef] [Google Scholar]
  • Y. Qiu, J. Qiao and P.M. Pardalos, A branch-and-price algorithm for production routing problems with carbon cap-and-trade. Omega 68 (2017) 49–61. [CrossRef] [Google Scholar]
  • N. Absi, C. Archetti, S. Dauzère-Pérès, D. Feillet and M.G. Speranza, Comparing sequential and integrated approaches for the production routing problem. Eur. J. Oper. Res. 269 (2018) 633–646. [CrossRef] [Google Scholar]
  • M. Chitsaz, J.-F. Cordeau and R. Jans, A unified decomposition matheuristic for assembly, production, and inventory routing. INFORMS J. Comput. 31 (2019) 134–152. [CrossRef] [MathSciNet] [Google Scholar]
  • A. Majidi, P. Farghadani-Chaharsooghi and S.M.J. Mirzapour Al-e Hashem, Sustainable pricing-production-workforce-routing problem for perishable products by considering demand uncertainty; a case study from the dairy industry. Transp. J. 61 (2022) 60–102. [CrossRef] [Google Scholar]
  • P. Farghadani-Chaharsooghi, P. Kamranfar and S.M.J. Mirzapour Al-e Hashem, A joint production-workforce-delivery stochastic planning problem for perishable items. Int. J. Prod. Res. 60 (2022) 6148–6172. [CrossRef] [Google Scholar]
  • I. Brekkå, S. Randøy, K. Fagerholt, K. Thun and S.T. Vadseth, The fish feed production routing problem. Comput. Oper. Res. 144 (2022) 105806. [CrossRef] [Google Scholar]
  • S.T. Vadseth, H. Andersson, M. Stålhane and M. Chitsaz, A multi-start route improving matheuristic for the production routeing problem. Int. J. Prod. Res. (2022) 1–22. [Google Scholar]
  • Y. Adulyasak, J.-F. Cordeau and R. Jans, Formulations and branch-and-cut algorithms for multivehicle production and inventory routing problems. INFORMS J. Comput. 26 (2014) 103–120. [CrossRef] [MathSciNet] [Google Scholar]
  • I. Dayarian and G. Desaulniers, A branch-price-and-cut algorithm for a production-routing problem with short-life-span products. Transp. Sci. 53 (2019) 829–849. [Google Scholar]
  • Y. Qiu, L. Wang, X. Xu, X. Fang and P.M. Pardalos, Formulations and branch-and-cut algorithms for multi-product multi-vehicle production routing problems with startup cost. Expert Syst. App. 98 (2018) 1–10. [CrossRef] [Google Scholar]
  • M. Darvish, C. Archetti and L.C. Coelho, Trade-offs between environmental and economic performance in production and inventory-routing problems. Int. J. Prod. Econ. 217 (2019) 269–280. [CrossRef] [Google Scholar]
  • Y. Qiu, M. Ni, L. Wang, Q. Li, X. Fang and P.M. Pardalos, Production routing problems with reverse logistics and remanufacturing. Transp. Res. Part E: Logistics Transp. Rev. 111 (2018) 87–100. [CrossRef] [Google Scholar]
  • C.M. Schenekemberg, C.T. Scarpin, J.E. Pecora Jr., T.A. Guimarães and L.C. Coelho, The two-echelon production-routing problem. Eur. J. Oper. Res. 288 (2021) 436–449. [CrossRef] [Google Scholar]
  • E.G. Manousakis, C.D. Tarantilis and E.E. Zachariadis, The cyclic production routing problem. Int. J. Prod. Res. (2023) 1–20. [CrossRef] [Google Scholar]
  • D. Hrabec, L.M. Hvattum and A. Hoff, The value of integrated planning for production, inventory, and routing decisions: a systematic review and meta-analysis. Int. J. Prod. Econ. 248 (2022) 108468. [CrossRef] [Google Scholar]
  • G. Laporte, Fifty years of vehicle routing. Transp. Sci. 43 (2009) 408–416. [CrossRef] [Google Scholar]
  • A.L. Soyster, Technical note – convex programming with set-inclusive constraints and applications to inexact linear programming. Oper. Res. 21 (1973) 1154–1157. [Google Scholar]
  • J.M. Mulvey, R.J. Vanderbei and S.A. Zenios, Robust optimization of large-scale systems. Oper. Res. 43 (1995) 264–281. [Google Scholar]
  • A. Ben-Tal and A. Nemirovski, Robust solutions of uncertain linear programs. Oper. Res. Lett. 25 (1999) 1–13. [Google Scholar]
  • L. El Ghaoui, F. Oustry and H. Lebret, Robust solutions to uncertain semidefinite programs. SIAM J. Optim. 9 (1998) 33–52. [Google Scholar]
  • D. Bertsimas and M. Sim, Robust discrete optimization and network flows. Math. Program. 98 (2003) 49–71. [Google Scholar]
  • D. Bertsimas and M. Sim, The price of robustness. Oper. Res. 52 (2004) 35–53. [Google Scholar]
  • K. Tong, F. You and G. Rong, Robust design and operations of hydrocarbon biofuel supply chain integrating with existing petroleum refineries considering unit cost objective. Comput. Chem. Eng. 68 (2014) 128–139. [CrossRef] [Google Scholar]
  • D. José Alem and R. Morabito, Production planning in furniture settings via robust optimization. Comput. Oper. Res. 39 (2012) 139–150. [CrossRef] [Google Scholar]
  • A. Jabbarzadeh, M. Haughton and F. Pourmehdi, A robust optimization model for efficient and green supply chain planning with postponement strategy. Int. J. Prod. Econ. 214 (2019) 266–283. [CrossRef] [Google Scholar]
  • B. Karimi, S.M.T. Fatemi Ghomi and J.M. Wilson, The capacitated lot sizing problem: a review of models and algorithms. Omega 31 (2003) 365–378. [Google Scholar]
  • G. Taguchi, S. Chowdhury and Y. Wu, Taguchi’s Quality Engineering Handbook. Wiley (2005). [Google Scholar]
  • A.S. Fraser, Simulation of genetic systems by automatic digital computers II. Effects of linkage on rates of advance under selection. Aust. J. Biol. Sci. 10 (1957) 492–500. [CrossRef] [Google Scholar]
  • A. Hiassat, A. Diabat and I. Rahwan, A genetic algorithm approach for location-inventory-routing problem with perishable products. J. Manuf. Syst. 42 (2017) 93–103. [CrossRef] [Google Scholar]
  • Y.C. Huang, Enhanced genetic algorithm-based fuzzy multi-objective approach to distribution network reconfiguration. IEE Proc. Gener. Transm. Distrib. 149 (2002) 615–620. [CrossRef] [Google Scholar]
  • C. Skinner and P. Riddle, Expected rates of building block discovery, retention and combination under 1-point and uniform crossover, in Parallel Problem Solving from Nature – PPSN VIII, edited by X. Yao, E.K. Burke, J.A. Lozano, J. Smith, J.J. Merelo-Guervós, J.A. Bullinaria, J.E. Rowe, P. Tiňo, A. Kabán and H.-P. Schwefel Springer, Berlin Heidelberg, Berlin, Heidelberg (2004) 121–130. [Google Scholar]
  • N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller and E. Teller, Equation of state calculations by fast computing machines. J. Chem. Phys. 21 (1953) 1087–1092. [Google Scholar]
  • T.A. Gardner, M. Benzie, J. Börner, E. Dawkins, S. Fick, R. Garrett, J. Godar, A. Grimard, S. Lake, R.K. Larsen and N. Mardas, Transparency and sustainability in global commodity supply chains. World Dev. 121 (2019) 163–177. [CrossRef] [Google Scholar]
  • E. Adida and G. Perakis, A robust optimization approach to dynamic pricing and inventory control with no backorders. Math. Program. 107 (2006) 97–129. [CrossRef] [MathSciNet] [Google Scholar]
  • D. Alem, E. Curcio, P. Amorim and B. Almada-Lobo, A computational study of the general lot-sizing and scheduling model under demand uncertainty via robust and stochastic approaches. Comput. Oper. Res. 90 (2018) 125–141. [CrossRef] [MathSciNet] [Google Scholar]

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