An optimization model for detailed scheduling of heterogeneous fleet of log trucks considering synchronization

References

• Audy J, D’Amours S, Rönnqvist M. 2012. Planning methods and decision support systems in vehicle routing problems for timber transportation: a review. Montreal: Cirrelt. 45.
   Google Scholar
• Bettinger P, Sessions J, Boston K. 2009. A review of the status and use of validation procedures for heuristics used in forest planning. Math Comput For Nat Sci. 1(1):26–37.
   Google Scholar
• Bordón MR, Montagna JM, Corsano G. 2018. An exact mathematical formulation for the optimal log transportation. For Policy Econ [Internet]. 95:115–122. doi: 10.1016/j.forpol.2018.07.017.
   Web of Science ®Google Scholar
• British Columbia Forest Safety Council. 2015. Logging trucks in British Columbia. For Saf Counc [Internet]. 1–7. https://www2.bcforestsafe.org/files/ref_LoggingTrucksInBritishColumbia.pdf.
   Google Scholar
• Coelho LC, Gagliardi JP, Renaud J, Ruiz A. 2016. Solving the vehicle routing problem with lunch break arising in the furniture delivery industry. J Oper Res Soc. 67(5):743–751. doi: 10.1057/jors.2015.90.
   Web of Science ®Google Scholar
• Darvish M, Larrain H, Coelho LC. 2016. A dynamic multi-plant lot-sizing and distribution problem. Int J Prod Res [Internet]. 54(22):6707–6717. doi: 10.1080/00207543.2016.1154623.
   Web of Science ®Google Scholar
• Derigs U, Pullmann M, Vogel U, Oberscheider M, Gronalt M, Hirsch P. 2012. Multilevel neighborhood search for solving full truckload routing problems arising in timber transportation. Electron Notes Discret Math [Internet]. 39:281–288. doi: 10.1016/j.endm.2012.10.037.
   Google Scholar
• Flisberg P, Lidén B, Rönnqvist M. 2009. A hybrid method based on linear programming and tabu search for routing of logging trucks. Comput Oper Res. 36(4):1122–1144. doi: 10.1016/j.cor.2007.12.012.
   Web of Science ®Google Scholar
• Ghotb S, Sowlati T, Mortyn J, Roeser D, Griess VC. 2022. A goal programming model for the optimization of log logistics considering sorting decisions and social objective. Can J For Res. 11(5):716–726. doi: 10.1139/cjfr-2021-0203.
   Google Scholar
• Gronalt M, Hirsch P. 2007. Metaheuristics. Doerner K, Gendreau M, Greistorfer P, Gutjahr W, Hartl R Reimann M, editors. Boston, MA: Springer US.
   Google Scholar
• El Hachemi N, Gendreau M, Rousseau LM. 2011. A hybrid constraint programming approach to the log-truck scheduling problem. Ann Oper Res. 184(1):163–178. doi: 10.1007/s10479-010-0698-x.
   Web of Science ®Google Scholar
• El Hachemi N, Gendreau M, Rousseau LM. 2013. A heuristic to solve the synchronized log-truck scheduling problem. Comput Oper Res [Internet]. 40(3):666–673. doi: 10.1016/j.cor.2011.02.002.
   Web of Science ®Google Scholar
• El Hachemi N, El Hallaoui I, Gendreau M, Rousseau LM. 2015. Flow-based integer linear programs to solve the weekly log-truck scheduling problem. Ann Oper Res [Internet]. 232(1):87–97. doi: 10.1007/s10479-014-1527-4.
   Web of Science ®Google Scholar
• Han SK, Murphy GE. 2012. Solving a woody biomass truck scheduling problem for a transport company in western Oregon, USA. Biomass Bioenergy [Internet]. 44:47–55. doi: 10.1016/j.biombioe.2012.04.015.
   Web of Science ®Google Scholar
• Haridass K, Valenzuela J, Yucekaya AD, McDonald T. 2014. Scheduling a log transport system using simulated annealing. Inf Sci (Ny) [Internet]. 264:302–316. doi: 10.1016/j.ins.2013.12.005.
   Web of Science ®Google Scholar
• Hirsch P. 2011. Minimizing empty truck loads in round timber transport with Tabu search strategies. Int J Inf Syst Supply Chain Manag. 4(2):15–41. doi: 10.4018/jisscm.2011040102.
   Google Scholar
• Lagzi S 2016. A study of time representation in a class of short term scheduling problems [Internet]. https://uwspace.uwaterloo.ca/handle/10012/10642
   Google Scholar
• Lee H, Maravelias CT. 2018. Combining the advantages of discrete- and continuous-time scheduling models: part 1. Framework and mathematical formulations. Comput Chem Eng [Internet]. 116:176–190. doi: 10.1016/j.compchemeng.2017.12.003.
   Web of Science ®Google Scholar
• Malladi KT, Quirion-Blais O, Sowlati T. 2018. Development of a decision support tool for optimizing the short-term logistics of forest-based biomass. Appl Energy [Internet]. 216:662–677. doi: 10.1016/j.apenergy.2018.02.027.
   Web of Science ®Google Scholar
• Malladi KT, Sowlati T. 2017. Optimization of operational level transportation planning in forestry: a review. Int J For Eng [Internet]. 28(3):198–210. doi: 10.1080/14942119.2017.1362825.
   Google Scholar
• Melchiori L, Nasini G, Montagna JM, Corsano G. 2022. A mathematical modeling for simultaneous routing and scheduling of logging trucks in the forest supply chain. For Policy Econ [Internet]. 136:102693. doi: 10.1016/j.forpol.2022.102693.
   Web of Science ®Google Scholar
• Murphy G. 2003. Reducing trucks on the road through optimal route scheduling and shared log transport services. South J Appl For. 27(3):198–205. doi: 10.1093/sjaf/27.3.198.
   Web of Science ®Google Scholar
• Oberscheider M, Zazgornik J, Henriksen CB, Gronalt M, Hirsch P. 2013. Minimizing driving times and greenhouse gas emissions in timber transport with a near-exact solution approach. Scand J For Res. 28(5):493–506. doi: 10.1080/02827581.2012.758309.
   Web of Science ®Google Scholar
• Palmgren M, Rönnqvist M, Värbrand P, Palmgren M, Ro M. 2003. A solution approach for log truck scheduling based on composite pricing and branch bound. Int Trans Oper Res. 10(5):433–447. doi: 10.1111/1475-3995.00420.
   Google Scholar
• Palmgren M, Rönnqvist M, Värbrand P. 2004. A near-exact method for solving the log-truck scheduling problem. Int Trans Oper Res. 11(4):447–464. doi: 10.1111/j.1475-3995.2004.00469.x.
   Google Scholar
• Rey PA, Muñoz JA, Weintraub A. 2009. A column generation model for truck routing in the Chilean forest industry. INFOR: Inf Syst Oper Res. 47(3):215–221. doi: 10.3138/infor.47.3.215.
   Web of Science ®Google Scholar
• Rix G, Rousseau LM, Pesant G. 2015. A column generation algorithm for tactical timber transportation planning. J Oper Res Soc. 66(2):278–287. doi: 10.1057/jors.2013.170.
   Web of Science ®Google Scholar
• Soares R, Marques A, Amorim P, Rasinmäki J. 2019. Multiple vehicle synchronisation in a full truck-load pickup and delivery problem: a case-study in the biomass supply chain. Eur J Oper Res [Internet]. 277(1):174–194. doi: 10.1016/j.ejor.2019.02.025.
   Web of Science ®Google Scholar
• Stefansson H, Sigmarsdottir S, Jensson P, Shah N. 2011. Discrete and continuous time representations and mathematical models for large production scheduling problems: a case study from the pharmaceutical industry. Eur J Oper Res [Internet]. 215(2):383–392. doi: 10.1016/j.ejor.2011.06.021.
   Web of Science ®Google Scholar
• Vancas MF. 2003. Feasibility studies: just how good are they? Electrometall Environ Hydrometall [Internet]. 2:1407–1413. doi: 10.1002/9781118804407.ch26.
   Google Scholar
• Vitale I, Broz D, Dondo R. 2021. Optimizing log transportation in the Argentinean forest industry by column generation. For Policy Econ [Internet]. 128:102483. doi: 10.1016/j.forpol.2021.102483.
   Web of Science ®Google Scholar
• Zazgornik J, Gronalt M, Hirsch P. 2012. The combined vehicle routing and foldable container scheduling problem: a model formulation and tabu search based solution approaches. INFOR: Inf Syst Oper Res. 50(4):147–162. doi: 10.3138/infor.50.4.147.
   Web of Science ®Google Scholar