Makespan estimation and order acceptance in batch process industries when processing times are uncertain

被引:34
作者
Ivanescu C.V. [1 ]
Fransoo J.C. [1 ]
Bertrand J.W.M. [1 ]
机构
[1] Department of Technology Management, Technische Universiteit Eindhoven, 5600 MB Eindhoven
关键词
Batch process industries; Order acceptance; Regression analysis;
D O I
10.1007/s00291-002-0108-0
中图分类号
学科分类号
摘要
Batch process industries are characterized by complex precedence relationships between operations, which renders the estimation of an acceptable workload very difficult. A detailed schedule based model can be used for this purpose, but for large problems this may require a prohibitive large amount of computation time. We propose a regression based model to estimate the makespan of a set of jobs. We extend earlier work based on deterministic processing times by considering Erlang-distributed processing times in our model. This regression-based model is used to support customer order acceptance. Three order acceptance policies are compared by means of simulation experiments: a scheduling policy, a workload policy and a regression policy. The results indicate that the performance of the regression policy can compete with the performance of the scheduling policy in situations with high variety in the job mix and high uncertainty in the processing times.
引用
收藏
页码:467 / 495
页数:28
相关论文
共 17 条
  • [1] Bertrand J.W.M., Wortmann J.C., Wijngaard J., Production Control: A Structural and Design Oriented Approach, (1990)
  • [2] Carlier J., Scheduling jobs with release dates and tails on identical machines to minimize the makespan, European Journal of Operational Research, 29, pp. 298-306, (1987)
  • [3] Cheng T.C.E., Gupta M.C., Survey of scheduling research involving due date determination decisions, European Journal of Operational Research, 38, pp. 156-166, (1989)
  • [4] Hopp W.J., Spearman M.L., Factory Physics: Foundations of Manufacturing Management, 2nd Edn., (2000)
  • [5] Lawrence S.R., Sewell E.C., Heuristic, optimal, static and dynamic schedules when processing times are uncertain, Journal of Operations Management, 15, pp. 71-82, (1997)
  • [6] Lenstra J.K., Rinnooy Kan A.H.G., Brucker P., Complexity of machine scheduling problems, Annals of Discrete Mathematics, 1, pp. 343-362, (1977)
  • [7] Leon V.J., Wu S.D., Storer R.H., Robustness measures and robust scheduling for job shops, IIE Transactions, 26, pp. 32-43, (1994)
  • [8] Montgomery D.C., Peck E.A., Introduction to Linear Regression Analysis, (1992)
  • [9] Pinedo M., Scheduling: Theory, Algorithms and Systems, (1995)
  • [10] Raaymakers W.H.M., Bertrand J.W.M., Fransoo J.C., The performance of workload rules for order acceptance in batch chemical manufacturing, Journal of Intelligent Manufacturing, 11, pp. 217-228, (2000)