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Volume 1, Issue 2
  • ISSN: 2666-2949
  • E-ISSN: 2666-2957

Abstract

This work studies the single vehicle routing problem (VRP) with multi-shift and fuzzy uncertainty. In this case, a company perpetually exploits a vehicle to accomplish demand over a scheduling period of several work shifts. In our problem, a crew performs maintenance jobs at different locations. The working team operates in different shifts with a maximum duration but recurrently returns to the depot by the end of the shift to avoid overtime.

The objective is to minimize the number of shifts and the completion time (makespan). In addition, we analyze the influence of uncertainty in driving and processing times on the overtime avoidance constraint in shift duration. We develop an Artificial Immune Heuristic to determine optimal solutions considering both makespan and overtime avoidance. We implement a Pareto-based framework to evaluate the impact of uncertainty.

We present several numerical case studies to examine the problem. In particular, we analyze different case study scenarios inferred from the environmental changes in travel and processing times observed in the Apulia region (SE Italy) during the COVID-19 lockdown periods that occurred in spring (started on March 9, 2020) and autumn (after November 6, 2020) of the year 2020.

The work program was revised as soon as the Italian COVID-19 restrictions were implemented in the spring and autumn of 2020 due to the changing environment. Our approach allowed for the rapid release of new robust maintenance programs. Results show significant improvements with the presented approach.

© 2022 Bentham Science Publishers
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2022-09-01
2024-11-22

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References

  1. TanS.Y. YehW.C. The vehicle routing problem: State-of-the-art classification and review.Appl. Sci. (Basel)202111211029510.3390/app112110295
     [Google Scholar]
  2. Zangeneh-KhamooshiS. ZabinskyZ.B. HeimJ.A. A multi-shift vehicle routing problem with windows and cycle times.Optim. Lett.2013761215122510.1007/s11590‑012‑0497‑1
     [Google Scholar]
  3. Nucamendi-GuillénS. Martínez-SalazarI. Angel-BelloF. Moreno-VegaJ.M. A mixed integer formulation and an efficient metaheuristic procedure for the k-travelling repairmen problem.J. Oper. Res. Soc.20166781121113410.1057/jors.2015.113
     [Google Scholar]
  4. OnderG. KaraI. DeryaT. New integer programming formulation for multiple traveling repairmen problem.Transp. Res. Procedia20172235536110.1016/j.trpro.2017.03.042
     [Google Scholar]
  5. KaraoğlanI. A branch-and-cut algorithm for the vehicle routing problem with multiple use of vehicles.Int. J. Lean Thinking201561
     [Google Scholar]
  6. BaiR. XueN. ChenJ. RobertsG.W. A set-covering model for a bidirectional multi-shift full truckload vehicle routing problem.Transp. Res., Part B: Methodol.20157913414810.1016/j.trb.2015.06.002
     [Google Scholar]
  7. SparksK. CooperC.L. FriedY. ShiromA. The effects of working hours on health: A meta-analytic reviewFrom Stress to Wellbeing., Palgrave Macmillan UK: London, pp. vol. 1, 292-314, 2013.10.1057/9781137310651_14
     [Google Scholar]
  8. CarusoC.C. Negative impacts of shiftwork and long work hours.Rehabil. Nurs.2014391162510.1002/rnj.10723780784
     [Google Scholar]
  9. CostaG. Shift work and health: Current problems and preventive actions.Saf. Health Work20101211212310.5491/SHAW.2010.1.2.11222953171
     [Google Scholar]
  10. LeeC. LeeK. ParkS. Robust vehicle routing problem with deadlines and travel time/demand uncertainty.J. Oper. Res. Soc.20126391294130610.1057/jors.2011.136
     [Google Scholar]
  11. CookT.M. RussellR.A. A simulation and statistical analysis of stochastic vehicle routing with timing constraints.Decis. Sci.19789467368710.1111/j.1540‑5915.1978.tb00753.x
     [Google Scholar]
  12. GendreauM. LaporteG. SéguinR. Stochastic vehicle routing.Eur. J. Oper. Res.199688131210.1016/0377‑2217(95)00050‑X
     [Google Scholar]
  13. YaohuangG. BingleiX. QiangG. Overview of stochastic vehicle routing problems.J. Southwest Jiaotong Univ.2002102113121
     [Google Scholar]
  14. BattarraM. ErdoganG. VigoD. Exact algorithms for the clustered vehicle routing problem.Oper. Res.2014621587110.1287/opre.2013.1227
     [Google Scholar]
  15. MaC. HaoW. HeR. JiaX. PanF. FanJ. XiongR. Distribution path robust optimization of electric vehicle with multiple distribution centers.PLoS Onevol. 13, no. 3, pp. e0193789, 2018.10.1371/journal.pone.019378929518169
     [Google Scholar]
  16. ZhaoH. XuW.A. JiangR. XuW. JiangR. The memetic algorithm for the optimization of urban transit network.Expert Syst. Appl.20154273760377310.1016/j.eswa.2014.11.056
     [Google Scholar]
  17. BockS. Solving the traveling repairman problem on a line with general processing times and deadlines.Eur. J. Oper. Res.2015244369070310.1016/j.ejor.2015.02.009
     [Google Scholar]
  18. LuoZ. QinH. LimA. Branch-and-price-and-cut for the multiple traveling repairman problem with distance constraints.Eur. J. Oper. Res.20142341496010.1016/j.ejor.2013.09.014
     [Google Scholar]
  19. SchönfelderR. LeuckerM. WaltherS. Efficient Profile Routing for Electric Vehicles. In:R.C.H. Hsu, S. Wang, (eds) Internet of Vehicles-Technologies and Services. IOV 2014. Lecture Notes in Computer Science, vol 8662, Springer, Champp. 21-30, 2014.10.1007/978‑3‑319‑11167‑4_3
     [Google Scholar]
  20. AthanasopoulosT. MinisI. Efficient techniques for the multi-period vehicle routing problem with time windows within a branch and price framework.Ann. Oper. Res.2013206112210.1007/s10479‑013‑1366‑8
     [Google Scholar]
  21. BaumM. DibbeltJ. PajorT. WagnerD. Energy-optimal routes for electric vehiclesProceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - SIGSPATIAL’132013, pp. 54-6310.1145/2525314.2525361
     [Google Scholar]
  22. DewildeT. CattrysseD. CoeneS. SpieksmaF.C.R. VansteenwegenP. Heuristics for the traveling repairman problem with profits.Comput. Oper. Res.20134071700170710.1016/j.cor.2013.01.003
     [Google Scholar]
  23. Angel-BelloF. AlvarezA. GarcíaI. Two improved formulations for the minimum latency problem.Appl. Math. Model.20133742257226610.1016/j.apm.2012.05.026
     [Google Scholar]
  24. DerigsU. PullmannM. VogelU. OberscheiderM. GronaltM. HirschP. Multilevel neighborhood search for solving full truckload routing problems arising in timber transportation.Electron. Notes Discrete Math.20123928128810.1016/j.endm.2012.10.037
     [Google Scholar]
  25. XuJ. YanF. LiS. Vehicle routing optimization with soft time windows in a fuzzy random environment.Transp. Res., Part E Logist. Trans. Rev.20114761075109110.1016/j.tre.2011.04.002
     [Google Scholar]
  26. KahramanC. ÖztayşiB. OnarS. Ç. A comprehensive literature review of 50 years of fuzzy set theoryInt. J. Comput. Intell Syst.vol. 9, no. sup1, pp. 3-24, 2016.10.1080/18756891.2016.1180817
     [Google Scholar]
  27. TeodorovićD. PavkovićG. The fuzzy set theory approach to the vehicle routing problem when demand at nodes is uncertain.Fuzzy Sets Syst.199682330731710.1016/0165‑0114(95)00276‑6
     [Google Scholar]
  28. TanK.K. TangK.Z. Vehicle dispatching system based on Taguchi-tuned fuzzy rules.Eur. J. Oper. Res.2001128354555710.1016/S0377‑2217(99)00373‑2
     [Google Scholar]
  29. GomesL. de C.T. Von ZubenF.J. Multiple criteria optimization based on unsupervised learning and fuzzy inference applied to the vehicle routing problem.J. Intell. Fuzzy Syst. Appl. Eng. Technol.2002132-4143154
     [Google Scholar]
  30. HeY. XuJ. A class of random fuzzy programming model and its application to vehicle routing problem.World J. Modelling Simulation200511311
     [Google Scholar]
  31. SáezD. CortésC.E. NúñezA. Hybrid adaptive predictive control for the multi-vehicle dynamic pick-up and delivery problem based on genetic algorithms and fuzzy clustering.Comput. Oper. Res.200835113412343810.1016/j.cor.2007.01.025
     [Google Scholar]
  32. GhannadpourS.F. NooriS. Tavakkoli-MoghaddamR. GhoseiriK. A multi-objective dynamic vehicle routing problem with fuzzy time windows: Model, solution and application.Appl. Soft Comput.20141450452710.1016/j.asoc.2013.08.015
     [Google Scholar]
  33. NovaesA.G.N. BezE.T. BurinP.J. AragãoD.P.Jr Dynamic milk-run OEM operations in over-congested traffic conditions.Comput. Ind. Eng.201588C32634010.1016/j.cie.2015.07.010
     [Google Scholar]
  34. Muñoz-CarpinteroD. SáezD. CortésC.E. NúñezA. A methodology based on evolutionary algorithms to solve a dynamic pickup and delivery problem under a hybrid predictive control approach.Transport. Sci.201549223925310.1287/trsc.2014.0569
     [Google Scholar]
  35. EwbankH. WankeP. Hadi-VenchehA. An unsupervised fuzzy clustering approach to the capacitated vehicle routing problem.Neural Comput. Appl.201627485786710.1007/s00521‑015‑1901‑4
     [Google Scholar]
  36. ZhuZ. XiaoJ. HeS. JiZ. SunY. A multi-objective memetic algorithm based on locality-sensitive hashing for one-to-many-to-one dynamic pickup-and-delivery problem.Inf. Sci.2016329C738910.1016/j.ins.2015.09.006
     [Google Scholar]
  37. HuiruM. LiminJ. XingchenZ. JianruiM. JiandongS. Travelling salesman problem in uncertain environments.Open Cybern. Systemics J.20159131331710.2174/1874110X01509010313
     [Google Scholar]
  38. AvciM. AvciM.G. A GRASP with iterated local search for the traveling repairman problem with profits.Comput. Ind. Eng.201711332333210.1016/j.cie.2017.09.032
     [Google Scholar]
  39. ZamoranoE. StolletzR. Branch-and-price approaches for the multiperiod technician routing and scheduling problem.Eur. J. Oper. Res.20172571556810.1016/j.ejor.2016.06.058
     [Google Scholar]
  40. ChenX. HewittM. ThomasB.W. An approximate dynamic programming method for the multi-period technician scheduling problem with experience-based service times and stochastic customers.Int. J. Prod. Econ.201819612213410.1016/j.ijpe.2017.10.028
     [Google Scholar]
  41. MirandaD.M. ConceiçãoS.V. The vehicle routing problem with hard time windows and stochastic travel and service time.Expert Syst. Appl.20166410411610.1016/j.eswa.2016.07.022
     [Google Scholar]
  42. YalçındağS. MattaA. ŞahinE. ShanthikumarJ.G. The patient assignment problem in home health care: Using a data-driven method to estimate the travel times of care givers.Flex. Serv. Manuf. J.2016281-230433510.1007/s10696‑015‑9222‑6
     [Google Scholar]
  43. RiveraJ.C. AfsarH.M. PrinsC. A multistart iterated local search for the multitrip cumulative capacitated vehicle routing problem.Comput. Optim. Appl.201561115918710.1007/s10589‑014‑9713‑5
     [Google Scholar]
  44. Solano-CharrisE.L. PrinsC. SantosA.C. Heuristic Approaches for the Robust Vehicle Routing Problem.ChamSpringer201438439510.1007/978‑3‑319‑09174‑7_33
     [Google Scholar]
  45. ChenX. ThomasB.W. HewittM. The technician routing problem with experience-based service times.Omega201661C496110.1016/j.omega.2015.07.006
     [Google Scholar]
  46. NucciF. Multi-shift single-vehicle routing problem under fuzzy uncertainty.Intell. Techniq. Smart Innov. Solut20211620162710.1007/978‑3‑030‑51156‑2_189
     [Google Scholar]
  47. KahramanC. OnarS.C. OztaysiB. Fuzzy multicriteria decision-making: A literature review.Int. J. Comput. Int. Syst.20158463766610.1080/18756891.2015.1046325
     [Google Scholar]
  48. KöppenM. VeenhuisC. Multi-objective particle swarm optimization by fuzzy-pareto-dominance meta-heuristic.Int. J. Hybrid Intell. Syst.20063417918610.3233/HIS‑2006‑3401
     [Google Scholar]
  49. ZhengJ.C.Y. ChenJ. A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser.Acta Mech. Sin.201834227528410.1007/s10409‑017‑0703‑6
     [Google Scholar]
  50. ZhengY. HanB. ChenJ. ZhongJ. LiJ. Maximizing the load carrying capacity of a variable stiffness composite cylinder based on the multi-objective optimization method.Int. J. Comput. Methodsvol. 18, no. 5, pp. 2150001, 202010.1142/S0219876221500018
     [Google Scholar]
  51. HapkeM. JaszkiewiczA. SłowińskiR. Pareto simulated annealing for fuzzy multi-objective combinatorial optimization.J. Heuristics20006332934510.1023/A:1009678314795
     [Google Scholar]
  52. Aguilar-LasserreA.A. PibouleauL. Azzaro-PantelC. DomenechS. Enhanced genetic algorithm-based fuzzy multiobjective strategy to multiproduct batch plant design.Appl. Soft Comput.2009941321133010.1016/j.asoc.2009.05.005
     [Google Scholar]
  53. GiannopoulosN. MoulianitisV.C. NearchouA.C. Multi-objective optimization with fuzzy measures and its application to flow-shop scheduling.Eng. Appl. Artif. Intell.20122571381139410.1016/j.engappai.2012.06.011
     [Google Scholar]
  54. BahriO. TalbiE.G. Ben AmorN. A generic fuzzy approach for multi-objective optimization under uncertainty.Swarm Evol. Comput.20184016618310.1016/j.swevo.2018.02.002
     [Google Scholar]
  55. Al-EneziJ. AbbodM. AlsharhanS. Artificial immune systems - models, algorithms and applications.Int. J. Res. Rev Appl. Sci.201032118131
     [Google Scholar]
  56. CorusD. OlivetoP.S. YazdaniD. When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms.Theor. Comput. Sci.202083216618510.1016/j.tcs.2019.03.002
     [Google Scholar]
  57. LiuJ. ZhiQ. JiH. LiB. LeiS. Wheel hub customization with an interactive artificial immune algorithm.J. Intell. Manuf.20213251305132210.1007/s10845‑020‑01613‑x
     [Google Scholar]
  58. BagheriA. ZandiehM. MahdaviI. YazdaniM. An artificial immune algorithm for the flexible job-shop scheduling problem.Future Gener. Comput. Syst.201026453354110.1016/j.future.2009.10.004
     [Google Scholar]
  59. MobiniM. MobiniZ. RabbaniM. An artificial immune algorithm for the project scheduling problem under resource constraints.Appl. Soft Comput.20111121975198210.1016/j.asoc.2010.06.013
     [Google Scholar]
  60. CorusD. OlivetoP.S. YazdaniD. Fast Artificial Immune Systems” In: A. Auger, C. Fonseca, N. Lourenço, P. Machado, L. Paquete, D. Whitley, (eds) Parallel Problem Solving from Nature - PPSN XV. Lecture Notes in Computer Science, Springer, Cham.vol 11102, pp. 67-78, 201810.1007/978‑3‑319‑99259‑4_6
     [Google Scholar]
  61. DebK. PratapA. AgarwalS. MeyarivanT. A fast and elitist multiobjective genetic algorithm: NSGA-II.IEEE Trans. Evol. Comput.20026218219710.1109/4235.996017
     [Google Scholar]
  62. ZitzlerE. LaumannsM. ThieleL. SPEA2 - Improving the strength pareto evolutionary algorithmTIK-Reportvol. 103, 200110.3929/ethz‑a‑004284029
     [Google Scholar]
  63. ZitzlerE. KünzliS. Indicator-Based Selection in Multiobjective SearchPPSN VIII.Berlin, Heidelberg200483284210.1007/978‑3‑540‑30217‑9‑84
     [Google Scholar]
  64. SunX. ZhaoL. ZhangP. BaoL. ChenY. Enhanced NSGA-II with evolving directions prediction for interval multi-objective optimization.Swarm Evol. Comput.20194912413310.1016/j.swevo.2019.05.009
     [Google Scholar]
  65. AnthonyI. NggadaS. QuenumJ. Distributed Optimisation of Perfect Preventive Maintenance and Component Replacement Schedules Using, vol. SPEA2, In:P. Vasant, I. Zelinka, G.W. Weber, (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. pp. 297- 310, 2021.10.1007/978‑3‑030‑68154‑8_29
     [Google Scholar]
  66. HaleT. AngristN. GoldszmidtR. KiraB. PetherickA. PhillipsT. WebsterS. Cameron-BlakeE. HallasL. MajumdarS. TatlowH. A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker).Nat. Hum. Behav.20215452953810.1038/s41562‑021‑01079‑833686204
     [Google Scholar]
  67. WiśniewskaA. BernardS. Burn-MurdochJ. HannenT. HaslettB. NevittC. PongJ. RininslandE. SmithA. StabeM. TilfordC. Lockdowns compared: Tracking governments’ coronavirus responses.Financial Time2021
     [Google Scholar]
/content/journals/flme/10.2174/2666294901666220510095557
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Multi-Shift Single-Vehicle Routing Problem Under Fuzzy Uncertainty During the COVID-19 Pandemic
Journal of Fuzzy Logic and Modeling in Engineering 1, e100522204511 (2022); https://doi.org/10.2174/2666294901666220510095557
/content/journals/flme/10.2174/2666294901666220510095557
/content/journals/flme/10.2174/2666294901666220510095557
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  • Article Type:     Research Article
Keyword(s): COVID-19; decision making; Heuristics; problem solving; scheduling; uncertainty

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