Open Access
Issue
RAIRO-Oper. Res.
Volume 57, Number 4, July-August 2023
Page(s) 1957 - 1981
DOI https://doi.org/10.1051/ro/2023083
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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.