Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Regenerative scheduling problem in engineer to order manufacturing: an economic assessment

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

The dynamic production scheduling is a very complex process that may arise from the occurrence of unpredictable situations such as the arrival of new orders besides the ones already accepted. As a consequence, companies may often encounter several difficulties to make decisions about the new orders acceptance and sequencing along with the production of the existing ones. With this recognition, a mathematical programming model for the regenerative scheduling problem with deterministic processing times is formulated in the present paper to evaluate the economic advantage of accepting a new order in an engineer to order (ETO) manufacturing organization. The real case of an Italian ETO company which produces hydraulic marine and offshore cranes is afterwards presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Artigues, C., Michelon, P., & Reusser, S. (2003). Insertion techniques for static and dynamic resource-constrained project scheduling. European Journal of Operation Research, 149, 249–267.

    Article  Google Scholar 

  • Basso, F., Guajardo, M., & Varas, M. (2020). Collaborative job scheduling in the wine bottling process. Omega, 91, 102021.

    Article  Google Scholar 

  • Bertrand, J. W. M., & Muntslag, D. R. (1993). Production control in engineer-to-order firms. International Journal of Production Economics, 30–31, 3–22.

    Article  Google Scholar 

  • Charnsirisakskul, K., Griffin, P. M., & Keskinocak, P. (2004). Order selection and scheduling with leadtime flexibility. IIE Transactions, 36(7), 697–707.

    Article  Google Scholar 

  • Chen, S.-H., Liou, Y.-C., Chen, Y.-H., & Wang, K.-C. (2019). Order acceptance and scheduling problem with carbon emission reduction and electricity tariffs on a single machine. Sustainability, 11, 5432.

    Article  Google Scholar 

  • Church, L. K., & Uzsoy, R. (1992). Analysis of periodic and event-driven rescheduling policies in dynamic shops. International Journal of Computer Integrated Manufacturing, 5(3), 153–163.

    Article  Google Scholar 

  • Dallasega, P., Rojas, R. A., Bruno, G., & Rauch, E. (2019). An agile scheduling and control approach in ETO construction supply chains. Computers Industry, 112, 103122.

    Article  Google Scholar 

  • Earl, C., Song, D. P., & Hicks, C. (2003). Planning complex engineer-to-order products. In G. Gogu, D. Coutellier, P. Chedmail, & P. Ray (Eds.), Recent advances in integrated design and manufacturing in mechanical engineering (pp. 463–472). Dordecht: Kluwer.

    Chapter  Google Scholar 

  • Ebben, M. J. R., Hans, E. W., & Olde Weghuis, F. M. (2005). Workload based order acceptance in job shop environments. OR Spectrum, 27, 107–122.

    Article  Google Scholar 

  • Esmaeilbeigi, R., Charkhgard, P., & Charkhgard, H. (2016). Order acceptance and scheduling problems in two-machine flow shops: New mixed integer programming formulations. European Journal of Operational Research, 251, 419–431.

    Article  Google Scholar 

  • Fanjul-Peyro, L. (2020). Models and an exact method for the Unrelated Parallel Machine scheduling problem with setups and resources. Expert Systems with Applications, 5, 100022.

    Article  Google Scholar 

  • Gosling, J., & Naim, M. M. (2009). Engineer-to-order supply chain management: A literature review and research agenda. International Journal of Production Economics, 122, 741–754.

    Article  Google Scholar 

  • Grabenstetter, D. H., & Usher, J. M. (2013). Determining job complexity in an engineer to order environment for due date estimation using a proposed framework. International Journal of Production Research, 51(19), 5728–5740.

    Article  Google Scholar 

  • Grabenstetter, D. H., & Usher, J. M. (2014). Developing due dates in an engineer-to-order engineering environment. International Journal of Production Research, 52, 6349–6361.

    Article  Google Scholar 

  • Grabenstetter, D. H., & Usher, J. M. (2015). Sequencing jobs in an engineer-to-order engineering environment. Production & Manufacturing Research, 3(1), 201–217.

    Article  Google Scholar 

  • Herbots, J., Herroelen, W., & Leus, R. (2007). Dynamic order acceptance and capacity planning on a single bottleneck resource. Naval Research Logistics, 54(8), 874–889.

    Article  Google Scholar 

  • Hicks, C., Song, D. P., & Earl, C. F. (2007). Dynamic scheduling for complex engineer-to order products. International Journal of Production Research, 45(15), 3477–3503.

    Article  Google Scholar 

  • Jian, C., & Xi, J. T. (2019). Dynamic scheduling in the engineer-to-order (ETO) assembly process by the combined immune algorithm and simulated annealing method. Advances in Production Engineering & Management, 14(3), 271–283.

    Article  Google Scholar 

  • Jiang, C., Hu, X., & Xi, J. (2019). Integrated multi-project scheduling and hierarchical workforce allocation in the ETO assembly process. Applied Science, 9, 885.

    Article  Google Scholar 

  • Koyuncu, E. (2017). A fuzzy mathematical model for order acceptance and scheduling problem. International Journal of Mathematical and Computational Sciences, 11(4), 145–148.

    Google Scholar 

  • La Fata, C. M., & Passannanti, G. (2014). A multi-objective genetic algorithm for the passenger maritime transportation problem. In OPTI 2014 an international conference on engineering and applied sciences optimization (pp. 111–128), 4–6 June 2014, Kos Island, Greece.

  • La Scalia, G., Micale, R., Aiello, G., & Enea, M. (2015). An integrated approach for modeling and solving multimode job shop scheduling problem using multi objective genetic algorithm. International Journal of Applied Engineering Research, 10(16), 37894–37899.

    Google Scholar 

  • Lee, Y., & Lee, K. (2020). Lot-sizing and scheduling in flat-panel display manufacturing process. Omega, 93, 102036.

    Article  Google Scholar 

  • Li, Z., Barenji, A. V., Jiang, J., Zhong, R. Y., & Xu, G. (2020). A mechanism for scheduling multi robot intelligent warehouse system face with dynamic demand. Journal of Intelligent Manufacturing, 31(2), 469–480.

    Article  Google Scholar 

  • Little, D., Rollins, R., Peck, M., & Porter, J. K. (2000). Integrated planning and scheduling in the engineer-to-order sector. International Journal of Computer Integrated Manufacturing, 13(6), 545–554.

    Article  Google Scholar 

  • Melchiors, P., Leus, R., Creemers, S., & Kolisch, R. (2018). Dynamic order acceptance and capacity planning in a stochastic multi-project environment with a bottleneck resource. International Journal of Production Research, 56(1–2), 459–475.

    Article  Google Scholar 

  • Meredith, J. R., & Akinc, U. (2007). Characterizing and structuring a new make-to-forecast production strategy. Journal of Operations Management, 25, 623–642.

    Article  Google Scholar 

  • Nobibon, F. T., & Leus, R. (2011). Exact algorithms for a generalization of the order acceptance and scheduling problem in a single-machine environment. Computer & Operations Research, 38, 367–378.

    Article  Google Scholar 

  • Oǧuz, C., Salman, F. S., & Yalcin, Z. B. (2010). Order acceptance and scheduling decisions in make-to-order systems. International Journal of Production Economics, 125, 200–211.

    Article  Google Scholar 

  • Ou, J., Zhong, X., & Wang, G. (2015). An improved heuristic parallel machine scheduling with rejection. European Journal of Operational Research, 241, 653–661.

    Article  Google Scholar 

  • Palakiti, V. P., Mohan, U., & Ganesan, V. K. (2019). Order acceptance and scheduling: overview and complexity results. International Journal of Operational Research, 34(3), 369–386.

    Article  Google Scholar 

  • Perez-Gonzalez, P., & Framinan, J. M. (2015). Assessing scheduling policies in a permutation flowshop with common due dates. International Journal of Production Research, 53(19), 5742–5754.

    Article  Google Scholar 

  • Rahman, H. F., Sarker, R., & Essam, D. (2015). A real-time order acceptance and scheduling approach for permutation flow shop problems. European Journal of Operational Research, 247(2), 488–503.

    Article  Google Scholar 

  • Slotnick, S. A. (2011). Order acceptance and scheduling: a taxonomy and review. European Journal of Operational Research, 212, 1–11.

    Article  Google Scholar 

  • Wang, H., Huang, M., & Wang, J. (2019a). An effective metaheuristic algorithm for flowshop scheduling with deteriorating jobs. Journal of Intelligent Manufacturing, 30(7), 2733–2742.

    Article  Google Scholar 

  • Wang, Z., Qi, Y., Cui, H., & Zhang, J. (2019b). A hybrid algorithm for order acceptance and scheduling problem in make-to-stock/make-to-order industries. Computer and Industrial Engineering, 127, 841–852.

    Article  Google Scholar 

  • Wang, S., & Ye, B. (2019). Exact methods for order acceptance and scheduling on unrelated parallel machines. Computer and Operations Research, 104, 159–173.

    Article  Google Scholar 

  • Weygandt, J. J., & Kieso, D. E. (2015). Accounting principles (12th ed.). Hoboken: Wiley.

    Google Scholar 

  • Zhang, J., Ding, G., Zou, Y., Qin, S., & Fu, J. (2019). Review of job shop scheduling research and its new perspectives under Industry 40. Journal of Intelligent Manufacturing, 30(4), 1809–1830.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Engineering, University of Palermo, Viale delle Scienze, 90128, Palermo, Italy

    R. Micale, C. M. La Fata, M. Enea & G. La Scalia

Authors
  1. R. Micale
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. M. La Fata
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Enea
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. La Scalia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. M. La Fata.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Micale, R., La Fata, C.M., Enea, M. et al. Regenerative scheduling problem in engineer to order manufacturing: an economic assessment. J Intell Manuf 32, 1913–1925 (2021). https://doi.org/10.1007/s10845-020-01728-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-020-01728-1

Keywords

Search

Navigation