Advertisement

Planning and Scheduling

  • Ioannis T. Christou
Chapter

Abstract

Operational planning and scheduling rank among the most important activities an industrial organization has to carry out, as they lie at the heart of the operations of the enterprise. Production planning, together with personnel scheduling comprise the decision making procedures regarding what will be produced, when, how, and by whom.

Keywords

Planning Horizon Production Schedule Feasible Schedule Customer Order Crew Member 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Aiex RM, Binato S, Resende MGC (2003) Parallel GRASP with path-relinking for job shop scheduling. Parallel Comput 29:393–430MathSciNetCrossRefGoogle Scholar
  2. Apostolopoulos P (2008) A decision making system for orders for the pull-based part of the available-to-promise strategy. M.Sc. thesis, Athens Information TechnologyGoogle Scholar
  3. Aydin ME, Fogarty TC (2004) A distributed evolutionary simulated annealing algorithm for combinatorial optimisation problems. J Heuristics 10:269–292CrossRefGoogle Scholar
  4. Ball MO, Chen C-Y, Zhao Z-Y (2004) Available to promise. In: Simchi-Levi D, Wu SD, Shen ZM (eds) Handbook of quantitative supply chain analysis: modeling in the e-business era. Springer, NYGoogle Scholar
  5. Bilgen B, Gunther H-O (2009) Integrated production and distribution planning in the fast moving consumer goods industry: a block planning application. OR Spectrum, 18 June 2009Google Scholar
  6. Binato S, Hery W, Loewenstern D, Resende MGC (2001) GRASP for job shop scheduling. In: Essays and surveys on meta-heuristics. Kluwer, AmsterdamGoogle Scholar
  7. Blackstone JH, Cox JF (2004) APICS Dictionary, 11th edn. McGraw Hill, Falls ChurchGoogle Scholar
  8. Brizuela CA, Sannomiya N (2000) A selection scheme in genetic algorithms for a complex scheduling problem. In: Proceedings of the GECCO 2000 genetic and evolutionary computation conference, Las Vegas, NVGoogle Scholar
  9. Brucker P, Jurisch B, Sievers B (1994) A branch and bound algorithm for the job-shop scheduling problem. Discret Appl Math 49:107–127MathSciNetMATHCrossRefGoogle Scholar
  10. Chen C-Y, Zhao Z-Y, Ball MO (2001) Quantity and due-date quoting available to promise. Inform Syst Frontiers 3(4):477–488CrossRefGoogle Scholar
  11. Chen C-Y, Zhao Z-Y, Ball MO (2002) A model for batch advanced available to promise. Prod Oper Manag 11(4):424–440CrossRefGoogle Scholar
  12. Chen-Ritzo C-H (2006) Availability management for configure-to-order supply chain systems. Ph.D. dissertation, College of Business Administration, Pennsylvania state UniversityGoogle Scholar
  13. Christopher M (2005) Logistics and supply chain management: creating value-adding networks, 3rd edn. Prentice-Hall, HarlowGoogle Scholar
  14. Christou IT, Ponis S (2008) Enhancing traditional ATP functionality in open source ERP systems: a case-study from the food and beverages industry. Int J Enterp Inf Syst 4(1):18–33CrossRefGoogle Scholar
  15. Christou IT, Ponis S (2009) A hierarchical system for efficient coordination of available-to-promise logic mechanisms. Int J Prod Res 47(11):3063–3078MATHCrossRefGoogle Scholar
  16. Christou IT, Zakarian A (2000) Domain knowledge and representation issues in genetic algorithms for scheduling problems. In: Proceedings of the GECCO 2000 genetics and evolutionary computation conference, Las Vegas, NVGoogle Scholar
  17. Christou IT, Zakarian A, Liu J-M, Carter H (1999) A two phase genetic algorithm for solving large scale bid-line generation problems at Delta Air Lines. Interfaces 29(5):51–65CrossRefGoogle Scholar
  18. Christou IT, Lagodimos AG, Lycopoulou D (2007) Hierarchical production planning for multi-product lines in the beverage industry. J Prod Plan Control 18(5):367–376CrossRefGoogle Scholar
  19. Dorndorf U, Pesch E (1995) Evolution based learning in a job-shop scheduling environment. Comput Oper Res 22:25–40MATHCrossRefGoogle Scholar
  20. Ervolina T, Dietrich B (2001) Moving toward dynamic available to promise. In: Gass PI, Jones AT (eds) Supply chain management practice and research: status and future directions. Manufacturing Engineering Laboratory, RH School of Business, University of MarylandGoogle Scholar
  21. Fernandes S, Lourenco HR (2007) A GRASP and branch-and-bound meta-heuristic for job shop scheduling. Lecture notes in computer science, vol 4446, pp 60–71Google Scholar
  22. French S (1982) Sequencing and scheduling: an introduction to the mathematics of the job shop. E. Horwood, ChichesterMATHGoogle Scholar
  23. Friedrich J-M, Speyerer J (2002) XML-based available-to-promise logic for small and medium enterprises. In: Proceedings of the 35th international conference on system sciences, Hawaii, HWGoogle Scholar
  24. Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. WH Freeman, NYMATHGoogle Scholar
  25. Graham RL, Lawler EL, Lenstra JK, Rinnooy Kan AHG (1979) Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann Discret Math 5:287–326MathSciNetMATHCrossRefGoogle Scholar
  26. Hopp W, Spearman M (2008) Factory physics, 3rd edn. McGraw-Hill/Irwin, NYGoogle Scholar
  27. Keskinocak P, Tayur S (2004) Due date management policies. In: Simchi-Levi D, Wu SD, Shen ZM (eds) Handbook of quantitative supply chain analysis: modeling in the e-business era. Springer, NYGoogle Scholar
  28. Kilger C, Schneeweiss L (2005) Demand Fulfillment and ATP. In: Stadtler H, Kilger C (eds) Supply chain management and advanced planning: concepts, models, software and case studies, 3rd edn. Springer, BerlinGoogle Scholar
  29. Kolonko M (1999) Some new results on simulated annealing applied to the job shop scheduling problems. Eur J Oper Res 113:123–136MATHCrossRefGoogle Scholar
  30. Lawler EL (1982) Preemptive scheduling of precedence-constrained jobs on parallel machines. In: Dempster MAH, Lenstra JK, Rinnooy Kan AHG (eds) Deterministic and Stochastic Scheduling. D Reidel Publishing Company, DordrechtGoogle Scholar
  31. Lee DS, Vassiliadis VS, Park JM (2004) A novel threshold accepting meta-heuristic for the job-shop scheduling problem. Comput Oper Res 31:2199–2213MATHCrossRefGoogle Scholar
  32. Lenstra JK, Rinnooy Kan AHG (1979) Computational complexity of discrete optimization problems. Ann Discret Math 4:121–140MathSciNetMATHCrossRefGoogle Scholar
  33. Mattfeld DC (1996) Evolutionary search and the job shop: investigations on genetic algorithms for production scheduling. Physica-Verlag, HeidelbergMATHGoogle Scholar
  34. Nanda R, Browne J (1992) Introduction to employee scheduling. Van Nostrand-Reinhold, NYGoogle Scholar
  35. Nowicki E, Smutnicki C (2005) An advanced tabu search algorithm for the job shop problem. J Sched 8:145–159MathSciNetMATHCrossRefGoogle Scholar
  36. Pardalos PM, Shylo O (2006) An algorithm for the job shop scheduling problem based on global equilibrium search techniques. Comput Manag Sci 3(4):331–348MathSciNetMATHCrossRefGoogle Scholar
  37. Pinedo M (2008) Scheduling: theory, algorithms and systems. Springer, NYMATHGoogle Scholar
  38. Sevkli M, Aydin ME (2006) A variable neighborhood search algorithm for job shop scheduling problems. Lecture notes in computer science, vol 3906, pp 261–271Google Scholar
  39. Silver EA, Pyke DF, Peterson R (1998) Inventory management and production planning and scheduling, 3rd edn. Wiley, HobokenGoogle Scholar
  40. Smith B, Leimkuhler J, Darrow R, Samuels J (1992) Yield management at American Air-Lines. Interfaces 22:8–31CrossRefGoogle Scholar
  41. Taillard ED (1994) Parallel taboo search techniques for the job shop scheduling problem. ORSA J Comput 6:108–117MATHGoogle Scholar
  42. Tarantilis CD, Kiranoudis CT (2002) A list-based threshold accepting method for the job-shop scheduling problems. Int J Prod Econ 77:159–171CrossRefGoogle Scholar
  43. Van Laarhoven PJM, Aarts EHL, Lenstra JK (1992) Job shop scheduling by simulated annealing. Oper Res 40:113–125MathSciNetMATHCrossRefGoogle Scholar
  44. Vásquez M, Whitley L (2000) A comparison of genetic algorithms for the static job shop scheduling problem. In: Proceedings of the 6th parallel problem solving from nature conference, PPSN VIGoogle Scholar
  45. Wagner HM, Whitin T (1958) Dynamic version of the economic lot size model. Manag Sci 5:89–96MathSciNetMATHCrossRefGoogle Scholar
  46. Watson J-P, Beck J, Howe A, Whitley L (2003) Problem difficulty for tabu search in job-shop scheduling. Artif Intell 143(2):189–217MathSciNetMATHCrossRefGoogle Scholar
  47. Wu A, Chiang D, Chang C-W (2010) Using order admission control to maximize revenue under capacity utilization requirements in MTO B2B industries. J Oper Res Soc Jpn 53(4):38–44MathSciNetGoogle Scholar
  48. Zhao Z, Ball MO, Kotake M (2005) Optimization-based available-to-promise with multi-stage resource availability. Ann Oper Res 135(1):65–85MathSciNetMATHCrossRefGoogle Scholar
  49. Zobolas GI, Tarantilis CD, Ioannou G (2009) A hybrid evolutionary algorithm for the job-shop scheduling problem. J Oper Res Soc 60:221–235MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Athens Information TechnologyPaianiaGreece

Personalised recommendations