Routing and Scheduling

  • Dmitry Ivanov
  • Alexander Tsipoulanidis
  • Jörn Schönberger
Chapter
Part of the Springer Texts in Business and Economics book series (STBE)

Abstract

In this chapter, scheduling and routing principles are discussed. At the beginning, a typical case for operative decision making and mathematical graphs for the representation of decision situations in a network structure are introduced. Additionally, the first insights into the algorithmic processing of graph-data as the basic ingredient for decision making in network structures are provided. The consideration of complex restrictions during the deployment of a resource is discussed by means of the traveling salesman problem (TSP) in which the sequencing of operations to build a schedule for a resource is in the focus of the decision making. The integrated consideration of assignment and scheduling/sequencing decision problems under limited resource availability is addressed in the context of the capacitated vehicle routing problem (CVRP). Finally, a short introduction to the scheduling of the production machines is given. The chapter is completed by an E-Supplement providing additional case studies, Excel templates, tasks and video streams.

Bibliography

  1. Agnetis A, Hall NG, Pacciarelli D (2006) Supply chain scheduling: sequence coordination. Discrete Appl Math 154(15):2044–2063CrossRefGoogle Scholar
  2. Albers S (1997) Better bounds for online scheduling. SIAM J Comput 29(2):459–473CrossRefGoogle Scholar
  3. Andersson A, Hoff A, Christiansen M, Hasle G, Løkketangen A (2010) Industrial aspects and literature survey: combined inventory management and routing. Comput Oper Res 37:1515–1536CrossRefGoogle Scholar
  4. Artigues C, Billaut J-C, Esswein C (2005) Maximization of solution flexibility for robust shop scheduling. Eur J Oper Res 165(2):314–328CrossRefGoogle Scholar
  5. Aytug H, Lawley MA, McKay K, Mohan S, Uzsoy R (2005) Executing production schedules in the face of uncertainties: a review and some future directions. Eur J Oper Res 161(1):86–100CrossRefGoogle Scholar
  6. Berrichi A, Yalaoui F (2013) Efficient bi-objective ant colony approach to minimize total tardiness and system unavailability for a parallel machine scheduling problem. Int J Adv Manuf Tech 68(9-12):2295–2310CrossRefGoogle Scholar
  7. Blazewicz J, Ecker K, Pesch E, Schmidt G, Weglarz J (2001) Scheduling computer and manufacturing processes, 2nd edn. Springer, BerlinCrossRefGoogle Scholar
  8. Bożek A, Wysocki M (2015) Flexible job shop with continuous material flow. Int J Prod Res 53(4):1273–1290CrossRefGoogle Scholar
  9. Chen Z-L (2010) Integrated production and outbound distribution scheduling: review and extensions. Oper Res 58(1):130–148CrossRefGoogle Scholar
  10. Clarke G, Wright JW (1964) Scheduling of vehicles from a central depot to a number of delivery points. Oper Res 12(4):568–581CrossRefGoogle Scholar
  11. Croes G (1958) A method for solving traveling-salesman problems. Oper Res 6(6):791–812CrossRefGoogle Scholar
  12. Desrochers M, Laporte G (1991) Improvements and extensions to the Miller-Tucker-Zemlin subtour elimination constraints. Oper Res Lett 10:27–36CrossRefGoogle Scholar
  13. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1:269–271CrossRefGoogle Scholar
  14. Doerner KF, Gronalt M, Hartl RF, Kiechle G, Reimann M (2008) Exact and heuristic algorithms for the vehicle routing problem with multiple, interdependent time windows. Comput Oper Res 35:3034–3048CrossRefGoogle Scholar
  15. Dolgui A, Eremeev AV, Kovalyov MY, Kuznetsov PM (2010) Multi-product lot-sizing and scheduling on unrelated parallel machines. IIE Trans 42(7):514–524CrossRefGoogle Scholar
  16. Dolgui A, Proth J-M (2010) Supply chain engineering: useful methods and techniques. Springer, BerlinCrossRefGoogle Scholar
  17. Gendreau M, Laporte G, Potvin J-Y (2002) Metaheuristics for the capacitated VRP. In: Toth P, Vigo D (eds) The vehicle routing problem. Society for Industrial and Applied Mathematics, Philadelphia, pp 129–154CrossRefGoogle Scholar
  18. Gillett BE, Miller LR (1974) A heuristic algorithm for the vehicle-dispatch problem. Oper Res 22(2):340–349CrossRefGoogle Scholar
  19. Gomes MC, Barbosa-Póvoa AP, Novais AQ (2013) Reactive scheduling in a make-to-order flexible job shop with re-entrant process and assembly a mathematical programming approach. Int J Prod Res 51(17):5120–5141CrossRefGoogle Scholar
  20. Grünert T, Irnich S (2005) Optimierung im transport - band I: grundlagen. Shaker, AachenGoogle Scholar
  21. Harjunkoski I, Maravelias CT, Bongers P, Castro PM, Engell S, Grossmann IE, Hooker J, Méndez C, Sand G, Wassick J (2014) Scope for industrial applications of production scheduling models and solution methods. Comput Chem Eng 62:161–193CrossRefGoogle Scholar
  22. Hazir O, Haouari M, Erel E (2010) Robust scheduling and robustness measures for the discrete time/cost trade-off problem. Eur J Oper Res 207(2):633–643CrossRefGoogle Scholar
  23. Ivanov D, Sokolov B, Dolgui A, Werner F, Ivanova M (2016) A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory Industry 4.0. Int J Prod Res 54(2):386–402CrossRefGoogle Scholar
  24. Ivanov D, Sokolov B (2012) Dynamic supply chain scheduling. J Sched 15(2):201–216CrossRefGoogle Scholar
  25. Joereßen A, Sebastian H-J (1998) Problemlösung mit Modellen und Algorithmen. Teubner, Stuttgart, LeipzigCrossRefGoogle Scholar
  26. Johnson SM (1954) Optimal two- and three-stage production schedules with setup times included. Nav Res Logist Q 1(1):61–68CrossRefGoogle Scholar
  27. Kolisch R, Hess K (2000) Efficient methods for scheduling make-to-order assemblies under resource, assembly area and part availability constraints. Int J Prod Res 38(1):207–228CrossRefGoogle Scholar
  28. Kovalyov MY, Ng CT, Cheng TCE (2007) Fixed interval scheduling: models, applications, computational complexity and algorithms. Eur J Oper Res 178:331–342CrossRefGoogle Scholar
  29. Kyparisis GJ, Koulamas CP (2006) Flexible flow shop scheduling with uniform parallel machines. Eur J Oper Res 168:985–997CrossRefGoogle Scholar
  30. Li Y, Chen R (2010) Stochastic single machine scheduling to minimize the weighted number of tardy jobs. In: Cao B-Y, Wang E, Guo S-Z, Chen S-L (eds) Fuzzy information and engineering 2010. Springer, Berlin, Heidelberg, pp 363–368CrossRefGoogle Scholar
  31. Liu Z, Ro YK (2014) Rescheduling for machine disruption to minimize makespan and maximum lateness. J Sched 17(4):339–352CrossRefGoogle Scholar
  32. Maccarthy BL, Liu J (1993) Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling. Int J Prod Res 31(1):59–79CrossRefGoogle Scholar
  33. Mattfeld DC (1996) Evolutionary search and the job shop. Physica-Verlag, HeidelbergCrossRefGoogle Scholar
  34. Moore JM (1968) An n job, one machine sequencing algorithm for minimizing the number of late jobs. Manage Sci 15(1):102–109CrossRefGoogle Scholar
  35. Pinedo ML (2010) Theory, algorithms, and systems, 4th edn. Springer, New YorkGoogle Scholar
  36. Ritzinger U, Puchinger J, Hartl RF (2016) A survey on dynamic and stochastic vehicle routing problems. Int J Prod Res 54(1):215–231CrossRefGoogle Scholar
  37. Sawik T (2013) Integrated selection of suppliers and scheduling of customer orders in the presence of supply chain disruption risks. Int J Prod Res 51(23-24):7006–7022CrossRefGoogle Scholar
  38. Shah N (2004) Process industry supply chains: advances and challenges. Comput Aided Chem Eng 18:123–138CrossRefGoogle Scholar
  39. Solomon MM (1987) Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper Res 5(2):254–265CrossRefGoogle Scholar
  40. Sotskov YN, Lai T-C, Werner F (2013) Measures of problem uncertainty for scheduling with interval processing times. OR Spectrum 35(3):659–689CrossRefGoogle Scholar
  41. Thonemann U (2010) Operations management: konzepte, methoden und anwendungen, 2nd edn. Pearson, MünchenGoogle Scholar
  42. Toth P, Vigo D (2002) Models, relaxations and exact approaches for the capacitated vehicle routing problem. Discrete Appl Math 123(1–3):487–512CrossRefGoogle Scholar
  43. Van de Vonder S, Demeulemeester E, Herroelen W (2007) A classification of predictive-reactive project scheduling procedures. J Sched 10(3):195–207CrossRefGoogle Scholar
  44. Vieira GE, Herrmann JW, Lin E (2003) Rescheduling manufacturing systems: a framework of strategies, policies, and methods. J Sched 6(1):35–58CrossRefGoogle Scholar
  45. Werner F (2013) A survey of genetic algorithms for shop scheduling problems. In: Siarry P (ed) Heuristics: theory and applications. Nova Science, New York, pp 161–222Google Scholar
  46. References for Sect. 14.4.1Google Scholar
  47. Konrad A (2013) Meet ORION, software that will save ups millions by improving drivers’ routes. http://www.forbes.com/sites/alexkonrad/2013/11/01/meet-orion-software-that-will-save-ups-millions-by-improving-drivers-routes/, accessed 19 Jan 2015
  48. Noyes K (2014) The shortest distance between two points? At UPS, it’s complicated. Fortune Online, July 25, 2014, 10:58. http://fortune.com/2014/07/25/the-shortest-distance-between-two-points-at-ups-its-complicated/, accessed 2 Dec 2014, 12:03
  49. Shontell A (2011) Why UPS Is So Efficient: “Our Trucks Never Turn Left”. Business Insider, March 24, 2011, 16:28. http://www.businessinsider.com/ups-efficiency-secret-our-trucks-never-turn-left-2011-3, accessed 28 Nov 2014, 19:33
  50. Wohlsen M (2013) The Astronomical Math behind UPS’ New Tool to Deliver Packages Faster. Wired Magazine Online, December 13, 2013, 6:30). http://www.wired.com/2013/06/ups-astronomical-math/, accessed: 9 Dec 2014, 13:05

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Dmitry Ivanov
    • 1
  • Alexander Tsipoulanidis
    • 1
  • Jörn Schönberger
    • 2
  1. 1.Department of Business AdministrationBerlin School of Economics and LawBerlinGermany
  2. 2.Faculty of Transportation and Traffic Science “Friedrich List”Technical University of DresdenDresdenGermany

Personalised recommendations