OR Spectrum

, Volume 28, Issue 2, pp 199–222 | Cite as

A hybrid policy for order acceptance in batch process industries

  • V. Cristina Ivănescu
  • Jan C. FransooEmail author
  • J. Will M. Bertrand
Regular Paper


Customer order acceptance is an important process in make-to-order industries. Acceptance policies should operate such that a pre-specified delivery reliability is achieved, while maximizing resource utilization. By selecting orders with specific characteristics that maximize resource utilization, an important and often unforeseen effect occurs: the mix of orders changes such that the expected delivery reliability is no longer met. This paper investigates the selectivity of an aggregate and a detailed acceptance procedure, for batch process industries featuring complex job and resource structures. We found that the detailed policy maximizes resource utilization but underestimates the consequences on the realized makespan of significantly changing the job mix. The aggregate policy, while being selective, performs much better with respect to the delivery reliability, but achieves a lower capacity utilization. We propose a third procedure, the hybrid policy, which combines the strengths of both the detailed and aggregate acceptance procedures. Simulation experiments show that the hybrid policy successfully controls the delivery reliability, without loosing much of the beneficial effect of the selectivity on utilization.


Schedule Policy Customer Order Workload Balance Actual Processing Time Delivery Reliability 
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.


  1. Bertrand JWM, Wortmann JC, Wijngaard J (1990) Production control: a structural and design oriented approach. Elsevier, AmsterdamGoogle Scholar
  2. Carlier J (1987) Scheduling jobs with release dates and tails on identical machines to minimize the makespan. Eur J Oper Res 29:298–306CrossRefGoogle Scholar
  3. Duenyas I (1995) Single facility due date setting with multiple customer classes. Manage Sci 41:608–619Google Scholar
  4. Duenyas I, Hopp WC (1995) Quoting customer lead times. Manage Sci 41:43–57CrossRefGoogle Scholar
  5. Ghosh JB (1997) Job selection in a heavily loaded shop. Comput Oper Res 24:141–145CrossRefMathSciNetGoogle Scholar
  6. Hahn GJ, Meeker WQ (1991) Statistical intervals. A guide for practitioners. Wiley, New YorkGoogle Scholar
  7. Hollander M, Wolfe DA (1999) Nonparametric statistical methods. Wiley, New YorkGoogle Scholar
  8. Ivanescu CV, Fransoo JC, Bertrand JWM (2002) Makespan estimation and order acceptance in batch process industries when processing times are uncertain. OR-Spectrum 24:467–495CrossRefGoogle Scholar
  9. Kallrath J (2003) Planning and scheduling in the process industry. In: Günther HO, van Beek P (eds) Advanced planning and scheduling solutions in process industries. Springer, Berlin Heidelberg New YorkGoogle Scholar
  10. Leon VJ, Wu SD, Storer RH (1994) Robustness measures and robust scheduling for job shops. IIE Trans 26:32–43CrossRefGoogle Scholar
  11. Lewis HF, Slotnick SA (2002) Multi-period job selection: planning work loads to maximize profit. Comput Oper Res 29:1081–1098CrossRefGoogle Scholar
  12. Montgomery DC, Peck EA (1992) Introduction to linear regression analysis. Wiley, New YorkGoogle Scholar
  13. Raaymakers WHM, Fransoo JC (2000) Identification of aggregate resource and job set characteristics for predicting job set makespan in batch process industries. Int J Prod Econ 68:137–149CrossRefGoogle Scholar
  14. Raaymakers WHM, Hoogeveen JA (2000) Scheduling no-wait job shops by simulated annealing. Eur J Oper Res 126:131–151CrossRefGoogle Scholar
  15. Raaymakers WHM, Bertrand JWM, Fransoo JC (2000a) The performance of workload rules for order acceptance in batch chemical manufacturing. J Intell Manuf 11:217–228CrossRefGoogle Scholar
  16. Raaymakers WHM, Bertrand JWM, Fransoo JC (2000b) Using aggregate estimation models for order acceptance in a descentralized production control for batch chemical manufacturing. IIE Trans 32:989–998CrossRefGoogle Scholar
  17. Reklaitis GV (1996) Overview of scheduling and planning of batch process operations. In: Reklaitis GV, Sunol AK, Rippin DWT, Hortacsu O (eds) Batch processing systems engineering. Springer, Berlin Heidelberg New YorkGoogle Scholar
  18. Schneeweiß C (1995) Hierarchical structures in organisations: a conceptual framework. Eur J Oper Res 86:4–31CrossRefGoogle Scholar
  19. Slotnick SA, Morton TE (1996) Selecting jobs for a heavily loaded shop with lateness penalties. Comput Oper Res 23:131–140CrossRefGoogle Scholar
  20. Ten Kate HA (1994) Towards a better understanding of order acceptance. Int J Prod Econ 37:139–152CrossRefGoogle Scholar
  21. Wester FAW, Wijngaard J, Zijm WHM (1994) Order acceptance strategies in a production-to-order environment with setup times and due-dates. Int J Prod Econ 30:1313–1326Google Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • V. Cristina Ivănescu
    • 1
  • Jan C. Fransoo
    • 2
    Email author
  • J. Will M. Bertrand
    • 2
  1. 1.Quintiles ConsultingHoofddorpThe Netherlands
  2. 2.Department of Technology ManagementTechnische Universiteit EindhovenEindhovenThe Netherlands

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